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mammograms</title> <meta name="description" content="Search results for: mammograms"> <meta name="keywords" content="mammograms"> <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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style="font-size:.9rem"><span class="badge badge-info">21</span> Using Priority Order of Basic Features for Circumscribed Masses Detection in Mammograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Minh%20Dong%20Le">Minh Dong Le</a>, <a href="https://publications.waset.org/abstracts/search?q=Viet%20Dung%20Nguyen"> Viet Dung Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Do%20Huu%20Viet"> Do Huu Viet</a>, <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20Huu%20Tu"> Nguyen Huu Tu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new method for circumscribed masses detection in mammograms. Our method is evaluated on 23 mammographic images of circumscribed masses and 20 normal mammograms from public Mini-MIAS database. The method is quite sanguine with sensitivity (SE) of 95% with only about 1 false positive per image (FPpI). To achieve above results we carry out a progression following: Firstly, the input images are preprocessed with the aim to enhance key information of circumscribed masses; Next, we calculate and evaluate statistically basic features of abnormal regions on training database; Then, mammograms on testing database are divided into equal blocks which calculated corresponding features. Finally, using priority order of basic features to classify blocks as an abnormal or normal regions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mammograms" title="mammograms">mammograms</a>, <a href="https://publications.waset.org/abstracts/search?q=circumscribed%20masses" title=" circumscribed masses"> circumscribed masses</a>, <a href="https://publications.waset.org/abstracts/search?q=evaluated%20statistically" title=" evaluated statistically"> evaluated statistically</a>, <a href="https://publications.waset.org/abstracts/search?q=priority%20order%20of%20basic%20features" title=" priority order of basic features"> priority order of basic features</a> </p> <a href="https://publications.waset.org/abstracts/48163/using-priority-order-of-basic-features-for-circumscribed-masses-detection-in-mammograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48163.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">334</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">20</span> An Approach Based on Statistics and Multi-Resolution Representation to Classify Mammograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nebi%20Gedik">Nebi Gedik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wave%20atom%20transform" title="wave atom transform">wave atom transform</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20features" title=" statistical features"> statistical features</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-resolution%20representation" title=" multi-resolution representation"> multi-resolution representation</a>, <a href="https://publications.waset.org/abstracts/search?q=mammogram" title=" mammogram"> mammogram</a> </p> <a href="https://publications.waset.org/abstracts/62356/an-approach-based-on-statistics-and-multi-resolution-representation-to-classify-mammograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62356.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">222</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">19</span> Computer Aided Classification of Architectural Distortion in Mammograms Using Texture Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Birmohan%20Singh">Birmohan Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=V.K.Jain"> V.K.Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=architecture%20distortion" title="architecture distortion">architecture distortion</a>, <a href="https://publications.waset.org/abstracts/search?q=mammograms" title=" mammograms"> mammograms</a>, <a href="https://publications.waset.org/abstracts/search?q=GLCM%20texture%20features" title=" GLCM texture features"> GLCM texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=GLRLM%20texture%20features" title=" GLRLM texture features"> GLRLM texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20classifier" title=" support vector machine classifier"> support vector machine classifier</a> </p> <a href="https://publications.waset.org/abstracts/29952/computer-aided-classification-of-architectural-distortion-in-mammograms-using-texture-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29952.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">491</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">18</span> Early Detection of Breast Cancer in Digital Mammograms Based on Image Processing and Artificial Intelligence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sehreen%20Moorat">Sehreen Moorat</a>, <a href="https://publications.waset.org/abstracts/search?q=Mussarat%20Lakho"> Mussarat Lakho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A method of artificial intelligence using digital mammograms data has been proposed in this paper for detection of breast cancer. Many researchers have developed techniques for the early detection of breast cancer; the early diagnosis helps to save many lives. The detection of breast cancer through mammography is effective method which detects the cancer before it is felt and increases the survival rate. In this paper, we have purposed image processing technique for enhancing the image to detect the graphical table data and markings. Texture features based on Gray-Level Co-Occurrence Matrix and intensity based features are extracted from the selected region. For classification purpose, neural network based supervised classifier system has been used which can discriminate between benign and malignant. Hence, 68 digital mammograms have been used to train the classifier. The obtained result proved that automated detection of breast cancer is beneficial for early diagnosis and increases the survival rates of breast cancer patients. The proposed system will help radiologist in the better interpretation of breast cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20imaging" title="medical imaging">medical imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer" title=" cancer"> cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=processing" title=" processing"> processing</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/80474/early-detection-of-breast-cancer-in-digital-mammograms-based-on-image-processing-and-artificial-intelligence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80474.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">259</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">17</span> Automated Digital Mammogram Segmentation Using Dispersed Region Growing and Pectoral Muscle Sliding Window Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayush%20Shrivastava">Ayush Shrivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Arpit%20Chaudhary"> Arpit Chaudhary</a>, <a href="https://publications.waset.org/abstracts/search?q=Devang%20Kulshreshtha"> Devang Kulshreshtha</a>, <a href="https://publications.waset.org/abstracts/search?q=Vibhav%20Prakash%20Singh"> Vibhav Prakash Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajeev%20Srivastava"> Rajeev Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Early diagnosis of breast cancer can improve the survival rate by detecting cancer at an early stage. Breast region segmentation is an essential step in the analysis of digital mammograms. Accurate image segmentation leads to better detection of cancer. It aims at separating out Region of Interest (ROI) from rest of the image. The procedure begins with removal of labels, annotations and tags from the mammographic image using morphological opening method. Pectoral Muscle Sliding Window Algorithm (PMSWA) is used for removal of pectoral muscle from mammograms which is necessary as the intensity values of pectoral muscles are similar to that of ROI which makes it difficult to separate out. After removing the pectoral muscle, Dispersed Region Growing Algorithm (DRGA) is used for segmentation of mammogram which disperses seeds in different regions instead of a single bright region. To demonstrate the validity of our segmentation method, 322 mammographic images from Mammographic Image Analysis Society (MIAS) database are used. The dataset contains medio-lateral oblique (MLO) view of mammograms. Experimental results on MIAS dataset show the effectiveness of our proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CAD" title="CAD">CAD</a>, <a href="https://publications.waset.org/abstracts/search?q=dispersed%20region%20growing%20algorithm%20%28DRGA%29" title=" dispersed region growing algorithm (DRGA)"> dispersed region growing algorithm (DRGA)</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=pectoral%20muscle%20sliding%20window%20algorithm%20%28PMSWA%29" title=" pectoral muscle sliding window algorithm (PMSWA)"> pectoral muscle sliding window algorithm (PMSWA)</a> </p> <a href="https://publications.waset.org/abstracts/69020/automated-digital-mammogram-segmentation-using-dispersed-region-growing-and-pectoral-muscle-sliding-window-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69020.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">312</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">16</span> Computer Aided Diagnosis Bringing Changes in Breast Cancer Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devadrita%20Dey%20Sarkar">Devadrita Dey Sarkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Regardless of the many technologic advances in the past decade, increased training and experience, and the obvious benefits of uniform standards, the false-negative rate in screening mammography remains unacceptably high .A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this abstract which employs features extracted by a new technique based on independent component analysis. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral breast images has the potential to improve the overall performance in the detection of breast lumps. Because breast lumps can be detected reliably by computer on lateral breast mammographs, radiologists’ accuracy in the detection of breast lumps would be improved by the use of CAD, and thus early diagnosis of breast cancer would become possible. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for breast CAD may include the computerized detection of breast nodules, as well as the computerized classification of benign and malignant nodules. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with these CAD systems, which would be reliable and useful method for quantifying the similarity of a pair of images for visual comparison by radiologists. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CAD%28computer-aided%20design%29" title="CAD(computer-aided design)">CAD(computer-aided design)</a>, <a href="https://publications.waset.org/abstracts/search?q=lesions" title=" lesions"> lesions</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=ROS%28region%20of%20suspicion%29" title=" ROS(region of suspicion)"> ROS(region of suspicion)</a> </p> <a href="https://publications.waset.org/abstracts/23237/computer-aided-diagnosis-bringing-changes-in-breast-cancer-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23237.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">456</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">15</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">137</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">14</span> 99mTc Scintimammography in an Equivocal Breast Lesion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malak%20Shawky%20Matter%20Elyas">Malak Shawky Matter Elyas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Early detection of breast cancer is the main tool to decrease morbidity and mortality rates. Many diagnostic tools are used, such as mammograms, ultrasound and magnetic resonance imaging, but none of them is conclusive, especially in very small sizes, less than 1 cm. So, there is a need for more accurate tools. Patients and methods: This study involved 13 patients with different breast lesions. 6 Patients had breast cancer, and one of them had metastatic axillary lymph nodes without clinically nor mammographically detected breast mass proved by biopsy and histopathology. Of the other 7 Patients, 4 of them had benign breast lesions proved by biopsy and histopathology, and 3 Patients showed Equivocal breast lesions on a mammogram. A volume of 370-444Mbq of (99m) Tc/ bombesin was injected. Dynamic 1-min images by Gamma Camera were taken for 20 minutes immediately after injection in the anterior view. Thereafter, two static images in anterior and prone lateral views by Gamma Camera were taken for 5 minutes. Finally, single-photon emission computed tomography images were taken for each patient. The definitive diagnosis was based on biopsy and histopathology. Results: 6 Patients with breast cancer proved by biopsy and histopathology showed Positive findings on Sestamibi (Scintimammography). 1 out of 4 Patients with benign breast lesions proved by biopsy and histopathology showed Positive findings on Sestamibi (Scintimammography) while the other 3 Patients showed Negative findings on Sestamibi. 3 Patients out of 3 Patients with equivocal breast findings on mammogram showed Positive Findings on Sestamibi (Scintimammography) and proved by biopsy and histopathology. Conclusions: While we agree that Scintimammography will not replace mammograms as a mass screening tool, we believe that many patients will benefit from Scintimammography, especially women with dense breast tissues and in the presence of breast implants that are difficult to diagnose by mammogram, wherein its sensitivity is low and in women with metastatic axillary lymph nodes without clinically nor mammographically findings. We can use Scintimammography in sentinel lymph node mapping as a more accurate tool, especially since it is non-invasive. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast." title="breast.">breast.</a>, <a href="https://publications.waset.org/abstracts/search?q=radiodiagnosis" title=" radiodiagnosis"> radiodiagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=lifestyle" title=" lifestyle"> lifestyle</a>, <a href="https://publications.waset.org/abstracts/search?q=surgery" title=" surgery"> surgery</a> </p> <a href="https://publications.waset.org/abstracts/189033/99mtc-scintimammography-in-an-equivocal-breast-lesion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189033.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">31</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">13</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 px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">350</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">12</span> Lacunarity measures on Mammographic Image Applying Fractal Dimension and Lacunarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sushma">S. Sushma</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Balasubramanian"> S. Balasubramanian</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20C.%20Latha"> K. C. Latha</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Sridhar"> R. Sridhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and lacunarity contribute to assess breast cancer risk. Fractal Dimension represents the complexity while the lacunarity characterize the gap of a fractal dimension. In this paper, we present our result confirming that the lacunarity value resulted in algorithm using mammogram images states that level of lacunarity will be low when the Fractal Dimension value will be high. <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=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20analysis" title=" image analysis"> image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=lacunarity" title=" lacunarity"> lacunarity</a>, <a href="https://publications.waset.org/abstracts/search?q=mammogram" title=" mammogram"> mammogram</a> </p> <a href="https://publications.waset.org/abstracts/13593/lacunarity-measures-on-mammographic-image-applying-fractal-dimension-and-lacunarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13593.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">389</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">11</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">10</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">423</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">9</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">8</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">7</span> Computer Aided Analysis of Breast Based Diagnostic Problems from Mammograms Using Image Processing and Deep Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Berkan%20Ural">Ali Berkan Ural</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems. <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=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20growing" title=" region growing"> region growing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/155700/computer-aided-analysis-of-breast-based-diagnostic-problems-from-mammograms-using-image-processing-and-deep-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155700.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">95</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">6</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">5</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">4</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">3</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">105</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">2</span> Beyond Inclusion: The Need for Health Equity for Women with Disabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaishree%20Ellis">Jaishree Ellis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The United States Centers for Disease Control tells us that many women with disabilities will not receive regular health screenings, including Pap Smears and mammograms. This article was comprised and written to recognize the barriers to care, gaps in existing healthcare implementation, and viable methodologies for the provision of comprehensive and robust gynecologic care for women with disabilities. According to the World Health Organization, 15% of the world's population, or approximately 1 billion people, have disabilities, most of whom are identified as women. Women with disabilities are described as being multi-disabled, as in some places, they suffer exclusion because of their disabilities as well as their gender. The paucity of information regarding how to create a healthcare system that is inclusive of every woman, regardless of her type of disability (physical, mental, intellectual or medical), has made it challenging to establish an environment that makes it possible for individuals to access care in an equitable, respectful and comprehensive way. A review of the current literature, institutional websites within the United States and American resource guides was implemented to determine where comprehensive models of care for women with disabilities exist, as well as the modalities that are being employed to meet their healthcare needs. The many barriers to care that women with disabilities face were also extracted from various sources within the literature to provide an exhaustive list that can be tackled, one by one. Of the 637 Hospital Systems in the United States, only 7 provide website documentation of health care services that address the unique needs of women with disabilities. The presumption is that if institutions have not marketed such interventions to the community, then it is likely that they do not have a robust suite of services with which to make gynecologic care available to patients with disabilities. Through this review, 7 main barriers to comprehensive gynecologic care were identified, with more than 20 sub-categories existing within those. As with many other areas of community life, inclusion remains lacking in the delivery of healthcare for women with disabilities. There are at least 7 barriers that must be overcome in order to provide equity in the medical office, the exam room, the hospital and the operating room. While few institutions have prioritized this, those few have provided blueprints that can easily be adopted by others. However, as the general population lives longer and ages, the incidence of disabilities increases, as do the healthcare disparities surrounding them. Further compounded by this is a lack of formal education for medical providers in the United States. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=health%20equity" title="health equity">health equity</a>, <a href="https://publications.waset.org/abstracts/search?q=inclusion" title=" inclusion"> inclusion</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20disparities" title=" healthcare disparities"> healthcare disparities</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a> </p> <a href="https://publications.waset.org/abstracts/183349/beyond-inclusion-the-need-for-health-equity-for-women-with-disabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183349.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">54</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">1</span> Effects of the Affordable Care Act On Preventive Care Disparities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cagdas%20Agirdas">Cagdas Agirdas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The Affordable Care Act (ACA) requires non-grandfathered private insurance plans, starting with plan years on or after September 23rd, 2010, to provide certain preventive care services without any cost sharing in the form of deductibles, copayments or co-insurance. This requirement may affect racial and ethnic disparities in preventive care as it provides the largest copay reduction in preventive care. Objectives: We ask whether the ACA’s free preventive care benefits are associated with a reduction in racial and ethnic disparities in the utilization of four preventive services: cholesterol screenings, colonoscopies, mammograms, and pap smears. Methods: We use a data set of over 6,000 individuals from the 2009, 2010, and 2013 Medical Expenditure Panel Surveys (MEPS). We restrict our data set only to individuals who are old enough to be eligible for each preventive service. Our difference-in-differences logistic regression model classifies privately-insured Hispanics, African Americans, and Asians as the treatment groups and 2013 as the after-policy year. Our control group consists of non-Hispanic whites on Medicaid as this program already covered preventive care services for free or at a low cost before the ACA. Results: After controlling for income, education, marital status, preferred interview language, self-reported health status, employment, having a usual source of care, age and gender, we find that the ACA is associated with increases in the probability of the median, privately-insured Hispanic person to get a colonoscopy by 3.6% and a mammogram by 3.1%, compared to a non-Hispanic white person on Medicaid. Similarly, we find that the median, privately-insured African American person’s probability of receiving these two preventive services improved by 2.3% and 2.4% compared to a non-Hispanic white person on Medicaid. We do not find any significant improvements for any racial or ethnic group for cholesterol screenings or pap smears. Furthermore, our results do not indicate any significant changes for Asians compared to non-Hispanic whites in utilizing the four preventive services. These reductions in racial/ethnic disparities are robust to reconfigurations of time periods, previous diagnosis, and residential status. Conclusions: Early effects of the ACA’s provision of free preventive care are significant for Hispanics and African Americans. Further research is needed for the later years as more individuals became aware of these benefits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=preventive%20care" title="preventive care">preventive care</a>, <a href="https://publications.waset.org/abstracts/search?q=Affordable%20Care%20Act" title=" Affordable Care Act"> Affordable Care Act</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20sharing" title=" cost sharing"> cost sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=racial%20disparities" title=" racial disparities"> racial disparities</a> </p> <a href="https://publications.waset.org/abstracts/122759/effects-of-the-affordable-care-act-on-preventive-care-disparities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122759.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">153</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); 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