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mammography</title> <meta name="description" content="Search results for: mammography"> <meta name="keywords" content="mammography"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="mammography"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 59</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: mammography</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">59</span> Comparative Diagnostic Performance of Diffusion-Weighted Imaging Combined With Microcalcifications on Mammography for Discriminating Malignant From Benign Bi-rads 4 Lesions With the Kaiser Score</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wangxu%20Xia">Wangxu Xia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> BACKGROUND BI-RADS 4 lesions raise the possibility of malignancy that warrant further clinical and radiologic work-up. This study aimed to evaluate the predictive performance of diffusion-weighted imaging(DWI) and microcalcifications on mammography for predicting malignancy of BI-RADS 4 lesions. In addition, the predictive performance of DWI combined with microcalcifications was alsocompared with the Kaiser score. METHODS During January 2021 and June 2023, 144 patients with 178 BI-RADS 4 lesions underwent conventional MRI, DWI, and mammography were included. The lesions were dichotomized intobenign or malignant according to the pathological results from core needle biopsy or surgical mastectomy. DWI was performed with a b value of 0 and 800s/mm2 and analyzed using theapparent diffusion coefficient, and a Kaiser score > 4 was considered to suggest malignancy. Thediagnostic performances for various diagnostic tests were evaluated with the receiver-operatingcharacteristic (ROC) curve. RESULTS The area under the curve (AUC) for DWI was significantly higher than that of the of mammography (0.86 vs 0.71, P<0.001), but was comparable with that of the Kaiser score (0.86 vs 0.84, P=0.58). However, the AUC for DWI combined with mammography was significantly highthan that of the Kaiser score (0.93 vs 0.84, P=0.007). The sensitivity for discriminating malignant from benign BI-RADS 4 lesions was highest at 89% for Kaiser score, but the highest specificity of 83% can be achieved with DWI combined with mammography. CONCLUSION DWI combined with microcalcifications on mammography could discriminate malignant BI-RADS4 lesions from benign ones with a high AUC and specificity. However, Kaiser score had a better sensitivity for discrimination. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRI" title="MRI">MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=DWI" title=" DWI"> DWI</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20disease" title=" breast disease"> breast disease</a> </p> <a href="https://publications.waset.org/abstracts/183824/comparative-diagnostic-performance-of-diffusion-weighted-imaging-combined-with-microcalcifications-on-mammography-for-discriminating-malignant-from-benign-bi-rads-4-lesions-with-the-kaiser-score" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183824.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">59</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">58</span> Knowledge and Utilization of Mammography among Undergraduate Female Students in a Nigerian University</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Arazeem%20Abdullahi">Ali Arazeem Abdullahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariam%20Seedat-Khan"> Mariam Seedat-Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Bamidele%20S.%20Akanni"> Bamidele S. Akanni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Like the rest of the world, cancer of the breast is a life-threatening disease to Nigerian women. The utilization of mammography is however very poor among the general population. Whereas, there strong indications that women who engage in the regular screening of breast cancer using mammography are more likely to have a lower risk of developing and dying from advanced breast cancer compared to unscreened women. This study examined knowledge of breast cancer and utilization of mammography among undergraduate female students at the University of Ilorin, Nigeria. Health Belief Model (HBM) was deployed to guide the conduct of the study. Method: Self-administered questionnaire was used to collect data from 292 undergraduate female students from the faculties of Social and Management Sciences of the University. A simple random sampling technique was used to select the respondents. Data was analyzed using both descriptive and inferential statistics. Results: The study found that apart from high knowledge of breast cancer and mammography, perceived threat, perceived susceptibility and perceived seriousness of breast cancer were equally high. However, the uptake of mammography was very poor largely due to perceived barriers including being single and young and poor history of breast cancer in families (cues to action). The test of hypotheses showed that there is a weak relationship of about 6.8% between knowledge of breast cancer and utilization of mammography (p-value= 0.244) at 0.05 level of significance. However, 64.4% of the respondents were willing to utilize mammography in the future if the opportunity arises. While the study found a significant statistical relationship between the perceived benefits of mammography and its utilization among the respondents, no significant statistical association was found between the socio-demographic characteristics of the respondents and the uptake of mammography. Recommendations: Findings highlight the need for health education interventions to promote breast cancer screening and the utilization mammography, while addressing barriers to the uptake of mammography among female undergraduate students of the University of Ilorin and Nigeria in general. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20of%20the%20breast" title="cancer of the breast">cancer of the breast</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=female%20undergraduate%20students" title=" female undergraduate students"> female undergraduate students</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20belief%20model" title=" health belief model"> health belief model</a>, <a href="https://publications.waset.org/abstracts/search?q=University%20of%20Ilorin" title=" University of Ilorin"> University of Ilorin</a> </p> <a href="https://publications.waset.org/abstracts/59927/knowledge-and-utilization-of-mammography-among-undergraduate-female-students-in-a-nigerian-university" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59927.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">242</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">57</span> Nurse’s Role in Early Detection of Breast Cancer through Mammography and Genetic Screening and Its Impact on Patient's Outcome</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salwa%20Hagag%20Abdelaziz">Salwa Hagag Abdelaziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Dorria%20Salem"> Dorria Salem</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoda%20Zaki"> Hoda Zaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Suzan%20Atteya"> Suzan Atteya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Early detection of breast cancer saves many thousands of lives each year via application of mammography and genetic screening and many more lives could be saved if nurses are involved in breast care screening practices. So, the aim of the study was to identify nurse's role in early detection of breast cancer through mammography and genetic screening and its impact on patient's outcome. In order to achieve this aim, 400 women above 40 years, asymptomatic were recruited for mammography and genetic screening. In addition, 50 nurses and 6 technologists were involved in the study. A descriptive analytical design was used. Five tools were utilized: sociodemographic, mammographic examination and risk factors, women's before, during and after mammography, items relaying to technologists, and items related to nurses were also obtained. The study finding revealed that 3% of women detected for malignancy and 7.25% for fibroadenoma. Statistically, significant differences were found between mammography results and age, family history, genetic screening, exposure to smoke, and using contraceptive pills. Nurses have insufficient knowledge about screening tests. Based on these findings the present study recommended involvement of nurses in breast care which is very important to in force population about screening practices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mammography" title="mammography">mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=early%20detection" title=" early detection"> early detection</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20screening" title=" genetic screening"> genetic screening</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a> </p> <a href="https://publications.waset.org/abstracts/22557/nurses-role-in-early-detection-of-breast-cancer-through-mammography-and-genetic-screening-and-its-impact-on-patients-outcome" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22557.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">562</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">56</span> Assessing Relationships between Glandularity and Gray Level by Using Breast Phantoms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yun-Xuan%20Tang">Yun-Xuan Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pei-Yuan%20Liu"> Pei-Yuan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kun-Mu%20Lu"> Kun-Mu Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Min-Tsung%20Tseng"> Min-Tsung Tseng</a>, <a href="https://publications.waset.org/abstracts/search?q=Liang-Kuang%20Chen"> Liang-Kuang Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuh-Feng%20Tsai"> Yuh-Feng Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ching-Wen%20Lee"> Ching-Wen Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jay%20Wu"> Jay Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer is predominant of malignant tumors in females. The increase in the glandular density increases the risk of breast cancer. BI-RADS is a frequently used density indicator in mammography; however, it significantly overestimates the glandularity. Therefore, it is very important to accurately and quantitatively assess the glandularity by mammography. In this study, 20%, 30% and 50% glandularity phantoms were exposed using a mammography machine at 28, 30 and 31 kVp, and 30, 55, 80 and 105 mAs, respectively. The regions of interest (ROIs) were drawn to assess the gray level. The relationship between the glandularity and gray level under various compression thicknesses, kVp, and mAs was established by the multivariable linear regression. A phantom verification was performed with automatic exposure control (AEC). The regression equation was obtained with an R-square value of 0.928. The average gray levels of the verification phantom were 8708, 8660 and 8434 for 0.952, 0.963 and 0.985 g/cm3, respectively. The percent differences of glandularity to the regression equation were 3.24%, 2.75% and 13.7%. We concluded that the proposed method could be clinically applied in mammography to improve the glandularity estimation and further increase the importance of breast cancer screening. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mammography" title="mammography">mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=glandularity" title=" glandularity"> glandularity</a>, <a href="https://publications.waset.org/abstracts/search?q=gray%20value" title=" gray value"> gray value</a>, <a href="https://publications.waset.org/abstracts/search?q=BI-RADS" title=" BI-RADS"> BI-RADS</a> </p> <a href="https://publications.waset.org/abstracts/60797/assessing-relationships-between-glandularity-and-gray-level-by-using-breast-phantoms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60797.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">492</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">55</span> Optimizing Exposure Parameters in Digital Mammography: A Study in Morocco </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Talbi%20Mohammed">Talbi Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Oustous%20Aziz"> Oustous Aziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Messaoud%20Mounir"> Ben Messaoud Mounir</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebihi%20Rajaa"> Sebihi Rajaa</a>, <a href="https://publications.waset.org/abstracts/search?q=Khalis%20Mohammed"> Khalis Mohammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Breast cancer is the leading cause of death for women around the world. Screening mammography is the reference examination, due to its sensitivity for detecting small lesions and micro-calcifications. Therefore, it is essential to ensure quality mammographic examinations with the most optimal dose. These conditions depend on the choice of exposure parameters. Clinically, practices must be evaluated in order to determine the most appropriate exposure parameters. Material and Methods: We performed our measurements on a mobile mammography unit (PLANMED Sofie-classic.) in Morocco. A solid dosimeter (AGMS Radcal) and a MTM 100 phantom allow to quantify the delivered dose and the image quality. For image quality assessment, scores are defined by the rate of visible inserts (MTM 100 phantom), obtained and compared for each acquisition. Results: The results show that the parameters of the mammography unit on which we have made our measurements can be improved in order to offer a better compromise between image quality and breast dose. The last one can be reduced up from 13.27% to 22.16%, while preserving comparable image quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mammography" title="Mammography">Mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=Breast%20Dose" title=" Breast Dose"> Breast Dose</a>, <a href="https://publications.waset.org/abstracts/search?q=Image%20Quality" title=" Image Quality"> Image Quality</a>, <a href="https://publications.waset.org/abstracts/search?q=Phantom" title=" Phantom"> Phantom</a> </p> <a href="https://publications.waset.org/abstracts/116596/optimizing-exposure-parameters-in-digital-mammography-a-study-in-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116596.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">172</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">54</span> Contrast Enhancement of Masses in Mammograms Using Multiscale Morphology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Kamra">Amit Kamra</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20K.%20Jain"> V. K. Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Pragya"> Pragya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography is widely used technique for breast cancer screening. There are various other techniques for breast cancer screening but mammography is the most reliable and effective technique. The images obtained through mammography are of low contrast which causes problem for the radiologists to interpret. Hence, a high quality image is mandatory for the processing of the image for extracting any kind of information from it. Many contrast enhancement algorithms have been developed over the years. In the present work, an efficient morphology based technique is proposed for contrast enhancement of masses in mammographic images. The proposed method is based on Multiscale Morphology and it takes into consideration the scale of the structuring element. The proposed method is compared with other state-of-the-art techniques. The experimental results show that the proposed method is better both qualitatively and quantitatively than the other standard contrast enhancement techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enhancement" title="enhancement">enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scale" title=" multi-scale"> multi-scale</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20morphology" title=" mathematical morphology"> mathematical morphology</a> </p> <a href="https://publications.waset.org/abstracts/29677/contrast-enhancement-of-masses-in-mammograms-using-multiscale-morphology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29677.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">53</span> Monte Carlo Simulation of X-Ray Spectra in Diagnostic Radiology and Mammography Using MCNP4C</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Heidary">Sahar Heidary</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Ghasemi%20Shayan"> Ramin Ghasemi Shayan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The overall goal Monte Carlo N-atom radioactivity transference PC program (MCNP4C) was done for the regeneration of x-ray groups in diagnostic radiology and mammography. The electrons were transported till they slow down and stopover in the target. Both bremsstrahlung and characteristic x-ray creation were measured in this study. In this issue, the x-ray spectra forecast by several computational models recycled in the diagnostic radiology and mammography energy kind have been calculated by appraisal with dignified spectra and their outcome on the scheming of absorbed dose and effective dose (ED) told to the adult ORNL hermaphroditic phantom quantified. This comprises practical models (TASMIP and MASMIP), semi-practical models (X-rayb&m, X-raytbc, XCOMP, IPEM, Tucker et al., and Blough et al.), and Monte Carlo modeling (EGS4, ITS3.0, and MCNP4C). Images got consuming synchrotron radiation (SR) and both screen-film and the CR system were related with images of the similar trials attained with digital mammography equipment. In sight of the worthy feature of the effects gained, the CR system was used in two mammographic inspections with SR. For separately mammography unit, the capability acquiesced bilateral mediolateral oblique (MLO) and craniocaudal(CC) mammograms attained in a woman with fatty breasts and a woman with dense breasts. Referees planned the common groups and definite absences that managed to a choice to miscarry the part that formed the scientific imaginings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mammography" title="mammography">mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=monte%20carlo" title=" monte carlo"> monte carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=effective%20dose" title=" effective dose"> effective dose</a>, <a href="https://publications.waset.org/abstracts/search?q=radiology" title=" radiology"> radiology</a> </p> <a href="https://publications.waset.org/abstracts/144857/monte-carlo-simulation-of-x-ray-spectra-in-diagnostic-radiology-and-mammography-using-mcnp4c" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144857.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">52</span> Comparison of Breast Surface Doses for Full-Field Digital Mammography and Digital Breast Tomosynthesis Using Breast Phantoms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Hui%20Chen">Chia-Hui Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Chien-Kuo%20Wang"> Chien-Kuo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Full field digital mammography (FFDM) is widely used in diagnosis of breast cancer. Digital breast tomosynthesis (DBT) has recently been introduced into the clinic and is being used for screening for breast cancer in the general population. Hence, the radiation dose delivered to the patients involved in an imaging protocol is of utmost concern. Aim: To compare the surface radiation dose (ESD) of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) by using breast phantoms. Method: We analyzed the average entrance surface dose (ESD) of FFDM and DBT by using breast phantoms. Optically Stimulated luminescent Dosimeters (OSLD) were placed in a tissue-equivalent Breast phantom at difference sites of interest. Absorbed dose measurements were obtained after digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) exposures. Results: An automatic exposure control (AEC) is proposed for surface dose measurement during DBT and FFDM. The mean ESD values for DBT and FFDM were 6.37 mGy and 3.51mGy, respectively. Using of OSLD measured for surface dose during DBT and FFDM. There were 19.87 mGy and 11.36 mGy, respectively. The surface exposure dose of DBT could possibly be increased by two times with FFDM. Conclusion: The radiation dose from DBT was higher than that of FFDM and the difference in dose between AEC and OSLD measurements at phantom surface. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=full-field%20digital%20mammography" title="full-field digital mammography">full-field digital mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20breast%20tomosynthesis" title=" digital breast tomosynthesis"> digital breast tomosynthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=optically%20stimulated%20luminescent%20dosimeters" title=" optically stimulated luminescent dosimeters"> optically stimulated luminescent dosimeters</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20dose" title=" surface dose"> surface dose</a> </p> <a href="https://publications.waset.org/abstracts/73090/comparison-of-breast-surface-doses-for-full-field-digital-mammography-and-digital-breast-tomosynthesis-using-breast-phantoms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">420</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">51</span> Knowledge of Quality Assurance and Quality Control in Mammography; A Study among Radiographers of Mammography Settings in Sri Lanka</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Niroshani">H. S. Niroshani</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20M.%20Ediri%20Arachchi"> W. M. Ediri Arachchi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Tudugala"> R. Tudugala</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20J.%20M.%20A.%20L.%20Jayasinghe"> U. J. M. A. L. Jayasinghe</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20M.%20U.%20J.%20Jayasekara"> U. M. U. J. Jayasekara</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20B.%20Hewavithana"> P. B. Hewavithana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography is used as a screening tool for early diagnosis of breast cancer. It is also useful in refining the diagnosis of breast cancer either by assessment or work up after a suspicious area in the breast has been detected. In order to detect breast cancer accurately and at the earliest possible stage, the image must have an optimum contrast to reveal mass densities and spiculated fibrous structures radiating from them. In addition, the spatial resolution must be adequate to reveal the suffusion of micro calcifications and their shape. The above factors can be optimized by implementing an effective QA programme to enhance the accurate diagnosis of mammographic imaging. Therefore, the radiographer’s knowledge on QA is greatly instrumental in routine mammographic practice. The aim of this study was to assess the radiographer’s knowledge on Quality Assurance and Quality Control programmes in relation to mammographic procedures. A cross-sectional study was carried out among all radiographers working in each mammography setting in Sri Lanka. Pre-tested, anonymous self-administered questionnaires were circulated among the study population and duly filled questionnaires returned within a period of three months were taken into the account. The data on demographical information, knowledge on QA programme and associated QC tests, overall knowledge on QA and QC programmes were obtained. Data analysis was performed using IBM SPSS statistical software (version 20.0). The total response rate was 59.6% and the average knowledge score was 54.15±11.29 SD out of 100. Knowledge was compared on the basis of education level, special training of mammography, and the years of working experience in a mammographic setting of the individuals. Out of 31 subjects, 64.5% (n=20) were graduate radiographers and 35.5% (n=11) were diploma holders while 83.9% (n=26) of radiographers have been specially trained for mammography and 16.1% (n=5) have not been attended for any special training for mammography. It is also noted that 58.1% (n=18) of individuals possessed their experience of less than one year and rest 41.9% (n=13) of them were greater than that. Further, the results found that there is a significant difference (P < 0.05) in the knowledge of QA and overall knowledge on QA and QC programme in the categories of education level and working experience. Also, results imply that there was a significant difference (P < 0.05) in the knowledge of QC test among the groups of trained and non-trained radiographers. This study reveals that education level, working experience and the training obtained particularly in the field of mammography have a significant impact on their knowledge on QA and QC in mammography. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge" title="knowledge">knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20assurance" title=" quality assurance"> quality assurance</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a> </p> <a href="https://publications.waset.org/abstracts/34038/knowledge-of-quality-assurance-and-quality-control-in-mammography-a-study-among-radiographers-of-mammography-settings-in-sri-lanka" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34038.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">50</span> Aspects and Studies of Fractal Geometry in Automatic Breast Cancer Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mrinal%20Kanti%20Bhowmik">Mrinal Kanti Bhowmik</a>, <a href="https://publications.waset.org/abstracts/search?q=Kakali%20Das%20Jr."> Kakali Das Jr.</a>, <a href="https://publications.waset.org/abstracts/search?q=Barin%20Kumar%20De"> Barin Kumar De</a>, <a href="https://publications.waset.org/abstracts/search?q=Debotosh%20Bhattacharjee"> Debotosh Bhattacharjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer is the most common cancer and a leading cause of death for women in the 35 to 55 age group. Early detection of breast cancer can decrease the mortality rate of breast cancer. Mammography is considered as a ‘Gold Standard’ for breast cancer detection and a very popular modality, presently used for breast cancer screening and detection. The screening of digital mammograms often leads to over diagnosis and a consequence to unnecessary traumatic & painful biopsies. For that reason recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. Tumor is a significant sign of breast cancer in both mammography and thermography. The tumors are complex in structure and they also exhibit a different statistical and textural features compared to the breast background tissue. Fractal geometry is a geometry which is used to describe this type of complex structure as per their main characteristic, where traditional Euclidean geometry fails. Over the last few years, fractal geometrics have been applied mostly in many medical image (1D, 2D, or 3D) analysis applications. In breast cancer detection using digital mammogram images, also it plays a significant role. Fractal is also used in thermography for early detection of the masses using the thermal texture. This paper presents an overview of the recent aspects and initiatives of fractals in breast cancer detection in both mammography and thermography. The scope of fractal geometry in automatic breast cancer detection using digital mammogram and thermogram images are analysed, which forms a foundation for further study on application of fractal geometry in medical imaging for improving the efficiency of automatic detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal" title="fractal">fractal</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor" title=" tumor"> tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=thermography" title=" thermography"> thermography</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a> </p> <a href="https://publications.waset.org/abstracts/22188/aspects-and-studies-of-fractal-geometry-in-automatic-breast-cancer-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22188.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">388</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">49</span> Thermalytix: An Advanced Artificial Intelligence Based Solution for Non-Contact Breast Screening</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sudhakar">S. Sudhakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Geetha%20Manjunath"> Geetha Manjunath</a>, <a href="https://publications.waset.org/abstracts/search?q=Siva%20Teja%20Kakileti"> Siva Teja Kakileti</a>, <a href="https://publications.waset.org/abstracts/search?q=Himanshu%20Madhu"> Himanshu Madhu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20screening" title="breast cancer screening">breast cancer screening</a>, <a href="https://publications.waset.org/abstracts/search?q=radiology" title=" radiology"> radiology</a>, <a href="https://publications.waset.org/abstracts/search?q=thermalytix" title=" thermalytix"> thermalytix</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=thermography" title=" thermography"> thermography</a> </p> <a href="https://publications.waset.org/abstracts/87848/thermalytix-an-advanced-artificial-intelligence-based-solution-for-non-contact-breast-screening" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87848.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">48</span> Factors Associated with Mammography Screening Behaviors: A Cross-Sectional Descriptive Study of Egyptian Women </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salwa%20Hagag%20Abdelaziz">Salwa Hagag Abdelaziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Naglaa%20Fathy%20Youssef"> Naglaa Fathy Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Abdellatif%20Hassan"> Nadia Abdellatif Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Rasha%20Wesam%20Abdelrahman"> Rasha Wesam Abdelrahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer is considered as a substantial health concern and practicing mammography screening [MS] is important in minimizing its related morbidity. So it is essential to have a better understanding of breast cancer screening behaviors of women and factors that influence utilization of them. The aim of this study is to identify the factors that are linked to MS behaviors among the Egyptian women. A cross-sectional descriptive design was carried out to provide a snapshot of the factors that are linked to MS behaviors. A convenience sample of 311 women was utilized and all eligible participants admitted to the Women Imaging Unit who are 40 years of age or above, coming for mammography assessment, not pregnant or breast feeding and who accepted to participate in the study were included. A structured questionnaire was developed by the researchers and contains three parts; Socio-demographic data; Motivating factors associated with MS; and association between MS and model of behavior change. The analyzed data indicated that most of the participated women (66.6 %) belonged to the age group of 40-49.A high proportion of participants (58.1%) of group having previous MS influenced by their neighbors to practice MS, whereas 32.7 % in group not having previous MS were influenced by family members which indicated significant differences (P <0.05). Doctors and media are shown to be the least influence of others to practice MS. Women with intention to have a future mammogram had higher OR (1.404) for practicing MS compared with women with no intention. Further studies are needed to examine the relation between Trans-theoretical Model [TTM] and practicing MS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=screening%20behaviors" title=" screening behaviors"> screening behaviors</a>, <a href="https://publications.waset.org/abstracts/search?q=morbidity" title=" morbidity"> morbidity</a> </p> <a href="https://publications.waset.org/abstracts/28433/factors-associated-with-mammography-screening-behaviors-a-cross-sectional-descriptive-study-of-egyptian-women" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28433.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">442</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">47</span> Content-Based Mammograms Retrieval Based on Breast Density Criteria Using Bidimensional Empirical Mode Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sourour%20Khouaja">Sourour Khouaja</a>, <a href="https://publications.waset.org/abstracts/search?q=Hejer%20Jlassi"> Hejer Jlassi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Feddaoui"> Nadia Feddaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Hamrouni"> Kamel Hamrouni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most medical images, and especially mammographies, are now stored in large databases. Retrieving a desired image is considered of great importance in order to find previous similar cases diagnosis. Our method is implemented to assist radiologists in retrieving mammographic images containing breast with similar density aspect as seen on the mammogram. This is becoming a challenge seeing the importance of density criteria in cancer provision and its effect on segmentation issues. We used the BEMD (Bidimensional Empirical Mode Decomposition) to characterize the content of images and Euclidean distance measure similarity between images. Through the experiments on the MIAS mammography image database, we confirm that the results are promising. The performance was evaluated using precision and recall curves comparing query and retrieved images. Computing recall-precision proved the effectiveness of applying the CBIR in the large mammographic image databases. We found a precision of 91.2% for mammography with a recall of 86.8%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BEMD" title="BEMD">BEMD</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20density" title=" breast density"> breast density</a>, <a href="https://publications.waset.org/abstracts/search?q=contend-based" title=" contend-based"> contend-based</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a> </p> <a href="https://publications.waset.org/abstracts/59187/content-based-mammograms-retrieval-based-on-breast-density-criteria-using-bidimensional-empirical-mode-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59187.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">232</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">46</span> Breast Cancer Early Recognition, New Methods of Screening, and Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Heidary">Sahar Heidary</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer is a main public common obstacle global. Additionally, it is the second top reason for tumor death across women. Considering breast cancer cure choices can aid private doctors in precaution for their patients through future cancer treatment. This article reviews usual management centered on stage, histology, and biomarkers. The growth of breast cancer is a multi-stage procedure including numerous cell kinds and its inhibition residues stimulating in the universe. Timely identification of breast cancer is one of the finest methods to stop this illness. Entirely chief therapeutic administrations mention screening mammography for women aged 40 years and older. Breast cancer metastasis interpretations for the mainstream of deaths from breast cancer. The discovery of breast cancer metastasis at the initial step is essential for managing and estimate of breast cancer development. Developing methods consuming the exploration of flowing cancer cells illustrate talented outcomes in forecasting and classifying the initial steps of breast cancer metastasis in patients. In public, mammography residues are the key screening implement though the efficiency of medical breast checks and self-checkup is less. Innovative screening methods are doubtful to exchange mammography in the close upcoming for screening the overall people. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=screening" title=" screening"> screening</a>, <a href="https://publications.waset.org/abstracts/search?q=metastasis" title=" metastasis"> metastasis</a>, <a href="https://publications.waset.org/abstracts/search?q=methods" title=" methods"> methods</a> </p> <a href="https://publications.waset.org/abstracts/154991/breast-cancer-early-recognition-new-methods-of-screening-and-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154991.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">45</span> Estimating X-Ray Spectra for Digital Mammography by Using the Expectation Maximization Algorithm: A Monte Carlo Simulation Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chieh-Chun%20Chang">Chieh-Chun Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Ting%20Shih"> Cheng-Ting Shih</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan-Lin%20Liu"> Yan-Lin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shu-Jun%20Chang"> Shu-Jun Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jay%20Wu"> Jay Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the widespread use of digital mammography (DM), radiation dose evaluation of breasts has become important. X-ray spectra are one of the key factors that influence the absorbed dose of glandular tissue. In this study, we estimated the X-ray spectrum of DM using the expectation maximization (EM) algorithm with the transmission measurement data. The interpolating polynomial model proposed by Boone was applied to generate the initial guess of the DM spectrum with the target/filter combination of Mo/Mo and the tube voltage of 26 kVp. The Monte Carlo N-particle code (MCNP5) was used to tally the transmission data through aluminum sheets of 0.2 to 3 mm. The X-ray spectrum was reconstructed by using the EM algorithm iteratively. The influence of the initial guess for EM reconstruction was evaluated. The percentage error of the average energy between the reference spectrum inputted for Monte Carlo simulation and the spectrum estimated by the EM algorithm was -0.14%. The normalized root mean square error (NRMSE) and the normalized root max square error (NRMaSE) between both spectra were 0.6% and 2.3%, respectively. We conclude that the EM algorithm with transmission measurement data is a convenient and useful tool for estimating x-ray spectra for DM in clinical practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20mammography" title="digital mammography">digital mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=expectation%20maximization%20algorithm" title=" expectation maximization algorithm"> expectation maximization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=X-Ray%20spectrum" title=" X-Ray spectrum"> X-Ray spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=X-Ray" title=" X-Ray"> X-Ray</a> </p> <a href="https://publications.waset.org/abstracts/3616/estimating-x-ray-spectra-for-digital-mammography-by-using-the-expectation-maximization-algorithm-a-monte-carlo-simulation-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3616.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">730</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">44</span> A Novel Breast Cancer Detection Algorithm Using Point Region Growing Segmentation and Pseudo-Zernike Moments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aileen%20F.%20Wang">Aileen F. Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography has been one of the most reliable methods for early detection and diagnosis of breast cancer. However, mammography misses about 17% and up to 30% of breast cancers due to the subtle and unstable appearances of breast cancer in their early stages. Recent computer-aided diagnosis (CADx) technology using Zernike moments has improved detection accuracy. However, it has several drawbacks: it uses manual segmentation, Zernike moments are not robust, and it still has a relatively high false negative rate (FNR)–17.6%. This project will focus on the development of a novel breast cancer detection algorithm to automatically segment the breast mass and further reduce FNR. The algorithm consists of automatic segmentation of a single breast mass using Point Region Growing Segmentation, reconstruction of the segmented breast mass using Pseudo-Zernike moments, and classification of the breast mass using the root mean square (RMS). A comparative study among the various algorithms on the segmentation and reconstruction of breast masses was performed on randomly selected mammographic images. The results demonstrated that the newly developed algorithm is the best in terms of accuracy and cost effectiveness. More importantly, the new classifier RMS has the lowest FNR–6%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20diagnosis" title="computer aided diagnosis">computer aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20region%20growing%20segmentation" title=" point region growing segmentation"> point region growing segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=pseudo-zernike%20moments" title=" pseudo-zernike moments"> pseudo-zernike moments</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20mean%20square" title=" root mean square"> root mean square</a> </p> <a href="https://publications.waset.org/abstracts/10488/a-novel-breast-cancer-detection-algorithm-using-point-region-growing-segmentation-and-pseudo-zernike-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10488.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">453</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">43</span> How Group Education Impacts Female Factory Workers’ Behavior and Readiness to Receive Mammography and Pap Smears</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Memnun%20Seven">Memnun Seven</a>, <a href="https://publications.waset.org/abstracts/search?q=Mine%20Bahar"> Mine Bahar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayg%C3%BCl%20Aky%C3%BCz"> Aygül Akyüz</a>, <a href="https://publications.waset.org/abstracts/search?q=Hatice%20Erdo%C4%9Fan"> Hatice Erdoğan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The workplace has been deemed a suitable location for educating many women at once about cancer screening. Objective: To determine how group education about early diagnostic methods for breast and cervical cancer affects women’s behavior and readiness to receive mammography and Pap smears. Methods: This semi-interventional study was conducted at a textile factory in Istanbul, Turkey. Female workers (n = 125) were included in the study. A participant identification form and knowledge evaluation form developed for this study, along with the trans-theoretical model, were used to collect data. A 45-min interactive group education was given to the participants. Results: Upon contacting participants 3 months after group education, 15.4% (n = 11) stated that they had since received a mammogram and 9.8% (n = 7) a Pap smear. As suggested by the trans-theoretical model, group education increased participants’ readiness to receive cancer screening, along with their knowledge of breast and cervical cancer. Conclusions: Group education positively impacted women’s knowledge of cancer and their readiness to receive mammography and Pap smears. Group education can therefore potentially create awareness of cancer screening tests among women and improve their readiness to receive such tests. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20screening" title="cancer screening">cancer screening</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20intervention" title=" educational intervention"> educational intervention</a>, <a href="https://publications.waset.org/abstracts/search?q=participation" title=" participation"> participation</a>, <a href="https://publications.waset.org/abstracts/search?q=women" title=" women "> women </a> </p> <a href="https://publications.waset.org/abstracts/16775/how-group-education-impacts-female-factory-workers-behavior-and-readiness-to-receive-mammography-and-pap-smears" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16775.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">329</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">42</span> Call-Back Laterality and Bilaterality: Possible Screening Mammography Quality Metrics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samson%20Munn">Samson Munn</a>, <a href="https://publications.waset.org/abstracts/search?q=Virginia%20H.%20Kim"> Virginia H. Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Huija%20Chen"> Huija Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sean%20Maldonado"> Sean Maldonado</a>, <a href="https://publications.waset.org/abstracts/search?q=Michelle%20Kim"> Michelle Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Koscheski"> Paul Koscheski</a>, <a href="https://publications.waset.org/abstracts/search?q=Babak%20N.%20Kalantari"> Babak N. Kalantari</a>, <a href="https://publications.waset.org/abstracts/search?q=Gregory%20Eckel"> Gregory Eckel</a>, <a href="https://publications.waset.org/abstracts/search?q=Albert%20Lee"> Albert Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In terms of screening mammography quality, neither the portion of reports that advise call-back imaging that should be bilateral versus unilateral nor how much the unilateral call-backs may appropriately diverge from 50–50 (left versus right) is known. Many factors may affect detection laterality: display arrangement, reflections preferentially striking one display location, hanging protocols, seating positions with respect to others and displays, visual field cuts, health, etc. The call-back bilateral fraction may reflect radiologist experience (not in our data) or confidence level. Thus, laterality and bilaterality of call-backs advised in screening mammography reports could be worthy quality metrics. Here, laterality data did not reveal a concern until drilling down to individuals. Bilateral screening mammogram report recommendations by five breast imaging, attending radiologists at Harbor-UCLA Medical Center (Torrance, California) 9/1/15--8/31/16 and 9/1/16--8/31/17 were retrospectively reviewed. Recommended call-backs for bilateral versus unilateral, and for left versus right, findings were counted. Chi-square (χ²) statistic was applied. Year 1: of 2,665 bilateral screening mammograms, reports of 556 (20.9%) recommended call-back, of which 99 (17.8% of the 556) were for bilateral findings. Of the 457 unilateral recommendations, 222 (48.6%) regarded the left breast. Year 2: of 2,106 bilateral screening mammograms, reports of 439 (20.8%) recommended call-back, of which 65 (14.8% of the 439) were for bilateral findings. Of the 374 unilateral recommendations, 182 (48.7%) regarded the left breast. Individual ranges of call-backs that were bilateral were 13.2–23.3%, 10.2–22.5%, and 13.6–17.9%, by year(s) 1, 2, and 1+2, respectively; these ranges were unrelated to experience level; the two-year mean was 15.8% (SD=1.9%). The lowest χ² p value of the group's sidedness disparities years 1, 2, and 1+2 was > 0.4. Regarding four individual radiologists, the lowest p value was 0.42. However, the fifth radiologist disfavored the left, with p values of 0.21, 0.19, and 0.07, respectively; that radiologist had the greatest number of years of experience. There was a concerning, 93% likelihood that bias against left breast findings evidenced by one of our radiologists was not random. Notably, very soon after the period under review, he retired, presented with leukemia, and died. We call for research to be done, particularly by large departments with many radiologists, of two possible, new, quality metrics in screening mammography: laterality and bilaterality. (Images, patient outcomes, report validity, and radiologist psychological confidence levels were not assessed. No intervention nor subsequent data collection was conducted. This uncomplicated collection of data and simple appraisal were not designed, nor had there been any intention to develop or contribute, to generalizable knowledge (per U.S. DHHS 45 CFR, part 46)). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mammography" title="mammography">mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=screening%20mammography" title=" screening mammography"> screening mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20metrics" title=" quality metrics"> quality metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=laterality" title=" laterality"> laterality</a> </p> <a href="https://publications.waset.org/abstracts/133741/call-back-laterality-and-bilaterality-possible-screening-mammography-quality-metrics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133741.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">41</span> A Comparative Study between Digital Mammography, B Mode Ultrasound, Shear-Wave and Strain Elastography to Distinguish Benign and Malignant Breast Masses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arjun%20Prakash">Arjun Prakash</a>, <a href="https://publications.waset.org/abstracts/search?q=Samanvitha%20H."> Samanvitha H.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> BACKGROUND: Breast cancer is the commonest malignancy among women globally, with an estimated incidence of 2.3 million new cases as of 2020, representing 11.7% of all malignancies. As per Globocan data 2020, it accounted for 13.5% of all cancers and 10.6% of all cancer deaths in India. Early diagnosis and treatment can improve the overall morbidity and mortality, which necessitates the importance of differentiating benign from malignant breast masses. OBJECTIVE: The objective of the present study was to evaluate and compare the role of Digital Mammography (DM), B mode Ultrasound (USG), Shear Wave Elastography (SWE) and Strain Elastography (SE) in differentiating benign and malignant breast masses (ACR BI-RADS 3 - 5). Histo-Pathological Examination (HPE) was considered the Gold standard. MATERIALS & METHODS: We conducted a cross-sectional study on 53 patients with 64 breast masses over a period of 10 months. All patients underwent DM, USG, SWE and SE. These modalities were individually assessed to know their accuracy in differentiating benign and malignant masses. All Digital Mammograms were done using the Fujifilm AMULET Innovality Digital Mammography system and all Ultrasound examinations were performed on SAMSUNG RS 80 EVO Ultrasound system equipped with 2 to 9 MHz and 3 – 16 MHz linear transducers. All masses were subjected to HPE. Independent t-test and Chi-square or Fisher’s exact test were used to assess continuous and categorical variables, respectively. ROC analysis was done to assess the accuracy of diagnostic tests. RESULTS: Of 64 lesions, 51 (79.68%) were malignant and 13 (20.31%) (p < 0.0001) were benign. SE was the most specific (100%) (p < 0.0001) and USG (98%) (p < 0.0001) was the most sensitive of all the modalities. E max, E mean, E max ratio, E mean ratio and Strain Ratio of the malignant masses significantly differed from those of the benign masses. Maximum SWE value showed the highest sensitivity (88.2%) (p < 0.0001) among the elastography parameters. A combination of USG, SE and SWE had good sensitivity (86%) (p < 0.0001). CONCLUSION: A combination of USG, SE and SWE improves overall diagnostic yield in differentiating benign and malignant breast masses. Early diagnosis and treatment of breast carcinoma will reduce patient mortality and morbidity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20mammography" title="digital mammography">digital mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=elastography" title=" elastography"> elastography</a> </p> <a href="https://publications.waset.org/abstracts/167688/a-comparative-study-between-digital-mammography-b-mode-ultrasound-shear-wave-and-strain-elastography-to-distinguish-benign-and-malignant-breast-masses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167688.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">40</span> Intelligent Prediction of Breast Cancer Severity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wahab%20Ali">Wahab Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Oyebade%20K.%20Oyedotun"> Oyebade K. Oyedotun</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnan%20Khashman"> Adnan Khashman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer remains a threat to the woman’s world in view of survival rates, it early diagnosis and mortality statistics. So far, research has shown that many survivors of breast cancer cases are in the ones with early diagnosis. Breast cancer is usually categorized into stages which indicates its severity and corresponding survival rates for patients. Investigations show that the farther into the stages before diagnosis the lesser the chance of survival; hence the early diagnosis of breast cancer becomes imperative, and consequently the application of novel technologies to achieving this. Over the year, mammograms have used in the diagnosis of breast cancer, but the inconclusive deductions made from such scans lead to either false negative cases where cancer patients may be left untreated or false positive where unnecessary biopsies are carried out. This paper presents the application of artificial neural networks in the prediction of severity of breast tumour (whether benign or malignant) using mammography reports and other factors that are related to breast cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20classification" title=" intelligent classification"> intelligent classification</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a> </p> <a href="https://publications.waset.org/abstracts/25662/intelligent-prediction-of-breast-cancer-severity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25662.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">39</span> Comparison of Radiation Dosage and Image Quality: Digital Breast Tomosynthesis vs. Full-Field Digital Mammography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Okhee%20Woo">Okhee Woo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: With increasing concern of individual radiation exposure doses, studies analyzing radiation dosage in breast imaging modalities are required. Aim of this study is to compare radiation dosage and image quality between digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM). Methods and Materials: 303 patients (mean age 52.1 years) who studied DBT and FFDM were retrospectively reviewed. Radiation dosage data were obtained by radiation dosage scoring and monitoring program: Radimetrics (Bayer HealthCare, Whippany, NJ). Entrance dose and mean glandular doses in each breast were obtained in both imaging modalities. To compare the image quality of DBT with two-dimensional synthesized mammogram (2DSM) and FFDM, 5-point scoring of lesion clarity was assessed and the better modality between the two was selected. Interobserver performance was compared with kappa values and diagnostic accuracy was compared using McNemar test. The parameters of radiation dosages (entrance dose, mean glandular dose) and image quality were compared between two modalities by using paired t-test and Wilcoxon rank sum test. Results: For entrance dose and mean glandular doses for each breasts, DBT had lower values compared with FFDM (p-value < 0.0001). Diagnostic accuracy did not have statistical difference, but lesion clarity score was higher in DBT with 2DSM and DBT was chosen as a better modality compared with FFDM. Conclusion: DBT showed lower radiation entrance dose and also lower mean glandular doses to both breasts compared with FFDM. Also, DBT with 2DSM had better image quality than FFDM with similar diagnostic accuracy, suggesting that DBT may have a potential to be performed as an alternative to FFDM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radiation%20dose" title="radiation dose">radiation dose</a>, <a href="https://publications.waset.org/abstracts/search?q=DBT" title=" DBT"> DBT</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20mammography" title=" digital mammography"> digital mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20quality" title=" image quality"> image quality</a> </p> <a href="https://publications.waset.org/abstracts/79784/comparison-of-radiation-dosage-and-image-quality-digital-breast-tomosynthesis-vs-full-field-digital-mammography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79784.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">38</span> Correlation Between Different Radiological Findings and Histopathological diagnosis of Breast Diseases: Retrospective Review Conducted Over Sixth Years in King Fahad University Hospital in Eastern Province, Saudi Arabia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadeem%20Aljamaan">Sadeem Aljamaan</a>, <a href="https://publications.waset.org/abstracts/search?q=Reem%20Hariri"> Reem Hariri</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahaf%20Alghamdi"> Rahaf Alghamdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Batool%20Alotaibi"> Batool Alotaibi</a>, <a href="https://publications.waset.org/abstracts/search?q=Batool%20Alsenan"> Batool Alsenan</a>, <a href="https://publications.waset.org/abstracts/search?q=Lama%20Althunayyan"> Lama Althunayyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Areej%20Alnemer"> Areej Alnemer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to correlate between radiological findings and histopathological results in regard to the breast imaging-reporting and data system scores, size of breast masses, molecular subtypes and suspicious radiological features, as well as to assess the concordance rate in histological grade between core biopsy and surgical excision among breast cancer patients, followed by analyzing the change of concordance rate in relation to neoadjuvant chemotherapy in a Saudi population. A retrospective review was conducted over 6-year period (2017-2022) on all breast core biopsies of women preceded by radiological investigation. Chi-squared test (χ2) was performed on qualitative data, the Mann-Whitney test for quantitative non-parametric variables, and the Kappa test for grade agreement. A total of 641 cases were included. Ultrasound, mammography, and magnetic resonance imaging demonstrated diagnostic accuracies of 85%, 77.9% and 86.9%; respectively. magnetic resonance imaging manifested the highest sensitivity (72.2%), and the lowest was for ultrasound (61%). Concordance in tumor size with final excisions was best in magnetic resonance imaging, while mammography demonstrated a higher tendency of overestimation (41.9%), and ultrasound showed the highest underestimation (67.7%). The association between basal-like molecular subtypes and the breast imaging-reporting and data system score 5 classifications was statistically significant only for magnetic resonance imaging (p=0.04). Luminal subtypes demonstrated a significantly higher percentage of speculation in mammography. Breast imaging-reporting and data system score 4 manifested a substantial number of benign pathologies in all the 3 modalities. A fair concordance rate (k= 0.212 & 0.379) was demonstrated between excision and the preceding core biopsy grading with and without neoadjuvant therapy, respectively. The results demonstrated a down-grading in cases post-neoadjuvant therapy. In cases who did not receive neoadjuvant therapy, underestimation of tumor grade in biopsy was evident. In summary, magnetic resonance imaging had the highest sensitivity, specificity, positive predictive value and accuracy of both diagnosis and estimation of tumor size. Mammography demonstrated better sensitivity than ultrasound and had the highest negative predictive value, but ultrasound had better specificity, positive predictive value and accuracy. Therefore, the combination of different modalities is advantageous. The concordance rate of core biopsy grading with excision was not impacted by neoadjuvant therapy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=neoadjuvant" title=" neoadjuvant"> neoadjuvant</a>, <a href="https://publications.waset.org/abstracts/search?q=pathology" title=" pathology"> pathology</a>, <a href="https://publications.waset.org/abstracts/search?q=US" title=" US"> US</a> </p> <a href="https://publications.waset.org/abstracts/172192/correlation-between-different-radiological-findings-and-histopathological-diagnosis-of-breast-diseases-retrospective-review-conducted-over-sixth-years-in-king-fahad-university-hospital-in-eastern-province-saudi-arabia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172192.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">37</span> Basic Study of Mammographic Image Magnification System with Eye-Detector and Simple EEG Scanner</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aika%20Umemuro">Aika Umemuro</a>, <a href="https://publications.waset.org/abstracts/search?q=Mitsuru%20Sato"> Mitsuru Sato</a>, <a href="https://publications.waset.org/abstracts/search?q=Mizuki%20Narita"> Mizuki Narita</a>, <a href="https://publications.waset.org/abstracts/search?q=Saya%20Hori"> Saya Hori</a>, <a href="https://publications.waset.org/abstracts/search?q=Saya%20Sakurai"> Saya Sakurai</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomomi%20Nakayama"> Tomomi Nakayama</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayano%20Nakazawa"> Ayano Nakazawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Toshihiro%20Ogura"> Toshihiro Ogura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography requires the detection of very small calcifications, and physicians search for microcalcifications by magnifying the images as they read them. The mouse is necessary to zoom in on the images, but this can be tiring and distracting when many images are read in a single day. Therefore, an image magnification system combining an eye-detector and a simple electroencephalograph (EEG) scanner was devised, and its operability was evaluated. Two experiments were conducted in this study: the measurement of eye-detection error using an eye-detector and the measurement of the time required for image magnification using a simple EEG scanner. Eye-detector validation showed that the mean distance of eye-detection error ranged from 0.64 cm to 2.17 cm, with an overall mean of 1.24 ± 0.81 cm for the observers. The results showed that the eye detection error was small enough for the magnified area of the mammographic image. The average time required for point magnification in the verification of the simple EEG scanner ranged from 5.85 to 16.73 seconds, and individual differences were observed. The reason for this may be that the size of the simple EEG scanner used was not adjustable, so it did not fit well for some subjects. The use of a simple EEG scanner with size adjustment would solve this problem. Therefore, the image magnification system using the eye-detector and the simple EEG scanner is useful. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG%20scanner" title="EEG scanner">EEG scanner</a>, <a href="https://publications.waset.org/abstracts/search?q=eye-detector" title=" eye-detector"> eye-detector</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=observers" title=" observers"> observers</a> </p> <a href="https://publications.waset.org/abstracts/155822/basic-study-of-mammographic-image-magnification-system-with-eye-detector-and-simple-eeg-scanner" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36</span> Peruvian Diagnostic Reference Levels for Patients Undergoing Different X-Rays Procedures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andres%20Portocarrero%20Bonifaz">Andres Portocarrero Bonifaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Caterina%20Sandra%20Camarena%20Rodriguez"> Caterina Sandra Camarena Rodriguez</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Palma%20Esparza"> Ricardo Palma Esparza</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Antonio%20Romero%20Carlos"> Nicolas Antonio Romero Carlos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reference levels for common X-rays procedures have been set in many protocols. In Peru, during quality control tests, the dose tolerance is set by these international recommendations. Nevertheless, further studies can be made to assess the national reality and relate dose levels with different parameters such as kV, mA/mAs, exposure time, type of processing (digital, digitalized or conventional), etc. In this paper three radiologic procedures were taken into account for study, general X-rays (fixed and mobile), intraoral X-rays (fixed, mobile and portable) and mammography. For this purpose, an Unfors Xi detector was used; the dose was measured at a focus - detector distance which varied depending on the procedure, and was corrected afterward to find the surface entry dose. The data used in this paper was gathered over a period of over 3 years (2015-2018). In addition, each X-ray machine was taken into consideration only once. The results hope to achieve a new standard which reflects the local practice, and address the issues of the ‘Bonn Call for Action’ in Peru. For this purpose, the 75% percentile of the dose of each radiologic procedure was calculated. In future quality control services, those machines with dose values higher than the selected threshold should be informed that they surpass the reference dose levels established in comparison other radiological centers in the country. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20X-rays" title="general X-rays">general X-rays</a>, <a href="https://publications.waset.org/abstracts/search?q=intraoral%20X-rays" title=" intraoral X-rays"> intraoral X-rays</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=reference%20dose%20levels" title=" reference dose levels"> reference dose levels</a> </p> <a href="https://publications.waset.org/abstracts/94936/peruvian-diagnostic-reference-levels-for-patients-undergoing-different-x-rays-procedures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94936.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">35</span> Estimation of Normalized Glandular Doses Using a Three-Layer Mammographic Phantom </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kuan-Jen%20Lai">Kuan-Jen Lai</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang-Yi%20Lin"> Fang-Yi Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Shang-Rong%20Huang"> Shang-Rong Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yun-Zheng%20Zeng"> Yun-Zheng Zeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Po-Chieh%20Hsu"> Po-Chieh Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jay%20Wu"> Jay Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The normalized glandular dose (DgN) estimates the energy deposition of mammography in clinical practice. The Monte Carlo simulations frequently use uniformly mixed phantom for calculating the conversion factor. However, breast tissues are not uniformly distributed, leading to errors of conversion factor estimation. This study constructed a three-layer phantom to estimated more accurate of normalized glandular dose. In this study, MCNP code (Monte Carlo N-Particles code) was used to create the geometric structure. We simulated three types of target/filter combinations (Mo/Mo, Mo/Rh, Rh/Rh), six voltages (25 ~ 35 kVp), six HVL parameters and nine breast phantom thicknesses (2 ~ 10 cm) for the three-layer mammographic phantom. The conversion factor for 25%, 50% and 75% glandularity was calculated. The error of conversion factors compared with the results of the American College of Radiology (ACR) was within 6%. For Rh/Rh, the difference was within 9%. The difference between the 50% average glandularity and the uniform phantom was 7.1% ~ -6.7% for the Mo/Mo combination, voltage of 27 kVp, half value layer of 0.34 mmAl, and breast thickness of 4 cm. According to the simulation results, the regression analysis found that the three-layer mammographic phantom at 0% ~ 100% glandularity can be used to accurately calculate the conversion factors. The difference in glandular tissue distribution leads to errors of conversion factor calculation. The three-layer mammographic phantom can provide accurate estimates of glandular dose in clinical practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title="Monte Carlo simulation">Monte Carlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20glandular%20dose" title=" normalized glandular dose"> normalized glandular dose</a>, <a href="https://publications.waset.org/abstracts/search?q=glandularity" title=" glandularity"> glandularity</a> </p> <a href="https://publications.waset.org/abstracts/97111/estimation-of-normalized-glandular-doses-using-a-three-layer-mammographic-phantom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97111.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">189</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">34</span> Mammographic Multi-View Cancer Identification Using Siamese Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alisher%20Ibragimov">Alisher Ibragimov</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofya%20Senotrusova"> Sofya Senotrusova</a>, <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Beliaeva"> Aleksandra Beliaeva</a>, <a href="https://publications.waset.org/abstracts/search?q=Egor%20Ushakov"> Egor Ushakov</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuri%20Markin"> Yuri Markin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20diagnosis" title=" computer-aided diagnosis"> computer-aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-view%20mammogram" title=" multi-view mammogram"> multi-view mammogram</a>, <a href="https://publications.waset.org/abstracts/search?q=siamese%20neural%20network" title=" siamese neural network"> siamese neural network</a> </p> <a href="https://publications.waset.org/abstracts/173794/mammographic-multi-view-cancer-identification-using-siamese-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173794.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">33</span> A Review of Deep Learning Methods in Computer-Aided Detection and Diagnosis Systems based on Whole Mammogram and Ultrasound Scan Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ian%20Omung%27a">Ian Omung'a</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20diagnosis" title="breast cancer diagnosis">breast cancer diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20detection%20and%20diagnosis" title=" computer aided detection and diagnosis"> computer aided detection and diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=whole%20mammogram%20classfication" title=" whole mammogram classfication"> whole mammogram classfication</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20classification" title=" ultrasound classification"> ultrasound classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/148925/a-review-of-deep-learning-methods-in-computer-aided-detection-and-diagnosis-systems-based-on-whole-mammogram-and-ultrasound-scan-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148925.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">32</span> Design, Shielding and Infrastructure of an X-Ray Diagnostic Imaging Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Diaz">D. Diaz</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Guevara"> C. Guevara</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Rey"> P. Rey </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper contains information about designing, shielding and protocols building in order to avoid ionizing radiation in X-Rays imaging areas as generated by X-Ray, mammography equipment, computed tomography equipment and digital subtraction angiography equipment, according to global standards. Furthermore, tools and elements about infrastructure to improve protection over patients, physicians and staff involved in a diagnostic imaging area are presented. In addition, technical parameters about each machine and the architecture designs and maps are described. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=imaging%20area" title="imaging area">imaging area</a>, <a href="https://publications.waset.org/abstracts/search?q=X-ray" title=" X-ray"> X-ray</a>, <a href="https://publications.waset.org/abstracts/search?q=shielding" title=" shielding"> shielding</a>, <a href="https://publications.waset.org/abstracts/search?q=dose" title=" dose"> dose</a> </p> <a href="https://publications.waset.org/abstracts/4161/design-shielding-and-infrastructure-of-an-x-ray-diagnostic-imaging-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4161.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">448</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31</span> Dynamic Contrast-Enhanced Breast MRI Examinations: Clinical Use and Technical Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janet%20Wing-Chong%20Wai">Janet Wing-Chong Wai</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20Chiu-Wing%20Lee"> Alex Chiu-Wing Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Hailey%20Hoi-Ching%20Tsang"> Hailey Hoi-Ching Tsang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeffrey%20Chiu"> Jeffrey Chiu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwok-Wing%20Tang"> Kwok-Wing Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Mammography has limited sensitivity and specificity though it is the primary imaging technique for detection of early breast cancer. Ultrasound imaging and contrast-enhanced MRI are useful adjunct tools to mammography. The advantage of breast MRI is high sensitivity for invasive breast cancer. Therefore, indications for and use of breast magnetic resonance imaging have increased over the past decade. Objectives: 1. Cases demonstration on different indications for breast MR imaging. 2. To review of the common artifacts and pitfalls in breast MR imaging. Materials and Methods: This is a retrospective study including all patients underwent dynamic contrast-enhanced breast MRI examination in our centre, performed from Jan 2011 to Dec 2017. The clinical data and radiological images were retrieved from the EPR (electronic patient record), RIS (Radiology Information System) and PACS (Picture Archiving and Communication System). Results and Discussion: Cases including (1) Screening of the contralateral breast in patient with a new breast malignancy (2) Breast augmentation with free injection of unknown foreign materials (3) Finding of axillary adenopathy with an unknown site of primary malignancy (4) Neo-adjuvant chemotherapy: before, during, and after chemotherapy to evaluate treatment response and extent of residual disease prior to operation. Relevant images will be included and illustrated in the presentation. As with other types of MR imaging, there are different artifacts and pitfalls that can potentially limit interpretation of the images. Because of the coils and software specific to breast MR imaging, there are some other technical considerations that are unique to MR imaging of breast regions. Case demonstration images will be available in presentation. Conclusion: Breast MR imaging is a highly sensitive and reasonably specific method for the detection of breast cancer. Adherent to appropriate clinical indications and technical optimization are crucial for achieving satisfactory images for interpretation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRI" title="MRI">MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=breast" title=" breast"> breast</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical" title=" clinical"> clinical</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer" title=" cancer"> cancer</a> </p> <a href="https://publications.waset.org/abstracts/86879/dynamic-contrast-enhanced-breast-mri-examinations-clinical-use-and-technical-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">241</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30</span> Automatic Identification of Pectoral Muscle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ana%20L.%20M.%20Pavan">Ana L. M. Pavan</a>, <a href="https://publications.waset.org/abstracts/search?q=Guilherme%20Giacomini"> Guilherme Giacomini</a>, <a href="https://publications.waset.org/abstracts/search?q=Allan%20F.%20F.%20Alves"> Allan F. F. Alves</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcela%20De%20Oliveira"> Marcela De Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20A.%20B.%20Neto"> Fernando A. B. Neto</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20E.%20D.%20Rosa"> Maria E. D. Rosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Andre%20P.%20Trindade"> Andre P. Trindade</a>, <a href="https://publications.waset.org/abstracts/search?q=Diana%20R.%20De%20Pina"> Diana R. De Pina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography is a worldwide image modality used to diagnose breast cancer, even in asymptomatic women. Due to its large availability, mammograms can be used to measure breast density and to predict cancer development. Women with increased mammographic density have a four- to sixfold increase in their risk of developing breast cancer. Therefore, studies have been made to accurately quantify mammographic breast density. In clinical routine, radiologists perform image evaluations through BIRADS (Breast Imaging Reporting and Data System) assessment. However, this method has inter and intraindividual variability. An automatic objective method to measure breast density could relieve radiologist’s workload by providing a first aid opinion. However, pectoral muscle is a high density tissue, with similar characteristics of fibroglandular tissues. It is consequently hard to automatically quantify mammographic breast density. Therefore, a pre-processing is needed to segment the pectoral muscle which may erroneously be quantified as fibroglandular tissue. The aim of this work was to develop an automatic algorithm to segment and extract pectoral muscle in digital mammograms. The database consisted of thirty medio-lateral oblique incidence digital mammography from São Paulo Medical School. This study was developed with ethical approval from the authors’ institutions and national review panels under protocol number 3720-2010. An algorithm was developed, in Matlab® platform, for the pre-processing of images. The algorithm uses image processing tools to automatically segment and extract the pectoral muscle of mammograms. Firstly, it was applied thresholding technique to remove non-biological information from image. Then, the Hough transform is applied, to find the limit of the pectoral muscle, followed by active contour method. Seed of active contour is applied in the limit of pectoral muscle found by Hough transform. An experienced radiologist also manually performed the pectoral muscle segmentation. Both methods, manual and automatic, were compared using the Jaccard index and Bland-Altman statistics. The comparison between manual and the developed automatic method presented a Jaccard similarity coefficient greater than 90% for all analyzed images, showing the efficiency and accuracy of segmentation of the proposed method. The Bland-Altman statistics compared both methods in relation to area (mm²) of segmented pectoral muscle. The statistic showed data within the 95% confidence interval, enhancing the accuracy of segmentation compared to the manual method. Thus, the method proved to be accurate and robust, segmenting rapidly and freely from intra and inter-observer variability. It is concluded that the proposed method may be used reliably to segment pectoral muscle in digital mammography in clinical routine. The segmentation of the pectoral muscle is very important for further quantifications of fibroglandular tissue volume present in the breast. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20contour" title="active contour">active contour</a>, <a href="https://publications.waset.org/abstracts/search?q=fibroglandular%20tissue" title=" fibroglandular tissue"> fibroglandular tissue</a>, <a href="https://publications.waset.org/abstracts/search?q=hough%20transform" title=" hough transform"> hough transform</a>, <a href="https://publications.waset.org/abstracts/search?q=pectoral%20muscle" title=" pectoral muscle"> pectoral muscle</a> </p> <a href="https://publications.waset.org/abstracts/39747/automatic-identification-of-pectoral-muscle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39747.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light 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