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3D images</title> <meta name="description" content="Search results for: ultrasound 3D images"> <meta name="keywords" content="ultrasound 3D images"> <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 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action="https://publications.waset.org/abstracts/search"> <div 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="ultrasound 3D images"> <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> 2815</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: ultrasound 3D images</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2815</span> Reduction of Speckle Noise in Echocardiographic Images: A Survey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fathi%20Kallel">Fathi Kallel</a>, <a href="https://publications.waset.org/abstracts/search?q=Saida%20Khachira"> Saida Khachira</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ben%20Slima"> Mohamed Ben Slima</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Ben%20Hamida"> Ahmed Ben Hamida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speckle noise is a main characteristic of cardiac ultrasound images, it corresponding to grainy appearance that degrades the image quality. For this reason, the ultrasound images are difficult to use automatically in clinical use, then treatments are required for this type of images. Then a filtering procedure of these images is necessary to eliminate the speckle noise and to improve the quality of ultrasound images which will be then segmented to extract the necessary forms that exist. In this paper, we present the importance of the pre-treatment step for segmentation. This work is applied to cardiac ultrasound images. In a first step, a comparative study of speckle filtering method will be presented and then we use a segmentation algorithm to locate and extract cardiac structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20processing" title="medical image processing">medical image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20images" title=" ultrasound images"> ultrasound images</a>, <a href="https://publications.waset.org/abstracts/search?q=Speckle%20noise" title=" Speckle noise"> Speckle noise</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=speckle%20filtering" title=" speckle filtering"> speckle filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=snakes" title=" snakes"> snakes</a> </p> <a href="https://publications.waset.org/abstracts/19064/reduction-of-speckle-noise-in-echocardiographic-images-a-survey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19064.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">530</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">2814</span> Review of Ultrasound Image Processing Techniques for Speckle Noise Reduction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kwazikwenkosi%20Sikhakhane">Kwazikwenkosi Sikhakhane</a>, <a href="https://publications.waset.org/abstracts/search?q=Suvendi%20Rimer"> Suvendi Rimer</a>, <a href="https://publications.waset.org/abstracts/search?q=Mpho%20Gololo"> Mpho Gololo</a>, <a href="https://publications.waset.org/abstracts/search?q=Khmaies%20Oahada"> Khmaies Oahada</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnan%20Abu-Mahfouz"> Adnan Abu-Mahfouz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical ultrasound imaging is a crucial diagnostic technique due to its affordability and non-invasiveness compared to other imaging methods. However, the presence of speckle noise, which is a form of multiplicative noise, poses a significant obstacle to obtaining clear and accurate images in ultrasound imaging. Speckle noise reduces image quality by decreasing contrast, resolution, and signal-to-noise ratio (SNR). This makes it difficult for medical professionals to interpret ultrasound images accurately. To address this issue, various techniques have been developed to reduce speckle noise in ultrasound images, which improves image quality. This paper aims to review some of these techniques, highlighting the advantages and disadvantages of each algorithm and identifying the scenarios in which they work most effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=speckle" title=" speckle"> speckle</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/166509/review-of-ultrasound-image-processing-techniques-for-speckle-noise-reduction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166509.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">111</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">2813</span> Study on Discontinuity Properties of Phased-Array Ultrasound Transducer Affecting to Sound Pressure Fields Pattern</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tran%20Trong%20Thang">Tran Trong Thang</a>, <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20Phan%20Kien"> Nguyen Phan Kien</a>, <a href="https://publications.waset.org/abstracts/search?q=Trinh%20Quang%20Duc"> Trinh Quang Duc</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The phased-array ultrasound transducer types are utilities for medical ultrasonography as well as optical imaging. However, their discontinuity characteristic limits the applications due to the artifacts contaminated into the reconstructed images. Because of the effects of the ultrasound pressure field pattern to the echo ultrasonic waves as well as the optical modulated signal, the side lobes of the focused ultrasound beam induced by discontinuity of the phased-array ultrasound transducer might the reason of the artifacts. In this paper, a simple method in approach of numerical simulation was used to investigate the limitation of discontinuity of the elements in phased-array ultrasound transducer and their effects to the ultrasound pressure field. Take into account the change of ultrasound pressure field patterns in the conditions of variation of the pitches between elements of the phased-array ultrasound transducer, the appropriated parameters for phased-array ultrasound transducer design were asserted quantitatively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phased-array%20ultrasound%20transducer" title="phased-array ultrasound transducer">phased-array ultrasound transducer</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20pressure%20pattern" title=" sound pressure pattern"> sound pressure pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=discontinuous%20sound%20field" title=" discontinuous sound field"> discontinuous sound field</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20visualization" title=" numerical visualization"> numerical visualization</a> </p> <a href="https://publications.waset.org/abstracts/15171/study-on-discontinuity-properties-of-phased-array-ultrasound-transducer-affecting-to-sound-pressure-fields-pattern" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15171.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">506</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">2812</span> Classifier for Liver Ultrasound Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soumya%20Sajjan">Soumya Sajjan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=segmentation" title="segmentation">segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machine" title=" Support Vector Machine"> Support Vector Machine</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20liver%20lesion" title=" ultrasound liver lesion"> ultrasound liver lesion</a>, <a href="https://publications.waset.org/abstracts/search?q=co-occurance%20Matrix" title=" co-occurance Matrix"> co-occurance Matrix</a> </p> <a href="https://publications.waset.org/abstracts/10244/classifier-for-liver-ultrasound-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10244.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">411</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">2811</span> Development of Ultrasounf Probe Holder for Automatic Scanning Asymmetric Reflector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nabilah%20Ibrahim">Nabilah Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hafiz%20Mohd%20Zaini"> Hafiz Mohd Zaini</a>, <a href="https://publications.waset.org/abstracts/search?q=Wan%20Fatin%20Liyana%20Mutalib"> Wan Fatin Liyana Mutalib</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ultrasound equipment or machine is capable to scan in two dimensional (2D) areas. However there are some limitations occur during scanning an object. The problem will occur when scanning process that involving the asymmetric object. In this project, the ultrasound probe holder for asymmetric reflector scanning in 3D image is proposed to make easier for scanning the phantom or object that has asymmetric shape. Initially, the constructed asymmetric phantom that construct will be used in 2D scanning. Next, the asymmetric phantom will be interfaced by the movement of ultrasound probe holder using the Arduino software. After that, the performance of the ultrasound probe holder will be evaluated by using the various asymmetric reflector or phantom in constructing a 3D image <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ultrasound%203D%20images" title="ultrasound 3D images">ultrasound 3D images</a>, <a href="https://publications.waset.org/abstracts/search?q=axial%20and%20lateral%20resolution" title=" axial and lateral resolution"> axial and lateral resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetric%20reflector" title=" asymmetric reflector"> asymmetric reflector</a>, <a href="https://publications.waset.org/abstracts/search?q=Arduino%20software" title=" Arduino software"> Arduino software</a> </p> <a href="https://publications.waset.org/abstracts/22856/development-of-ultrasounf-probe-holder-for-automatic-scanning-asymmetric-reflector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22856.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">560</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">2810</span> The Detection of Implanted Radioactive Seeds on Ultrasound Images Using Convolution Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edward%20Holupka">Edward Holupka</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Rossman"> John Rossman</a>, <a href="https://publications.waset.org/abstracts/search?q=Tye%20Morancy"> Tye Morancy</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Aronovitz"> Joseph Aronovitz</a>, <a href="https://publications.waset.org/abstracts/search?q=Irving%20Kaplan"> Irving Kaplan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prostate" title="prostate">prostate</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title=" deep neural network"> deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=seed%20implant" title=" seed implant"> seed implant</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/93735/the-detection-of-implanted-radioactive-seeds-on-ultrasound-images-using-convolution-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93735.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">198</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">2809</span> Robust Segmentation of Salient Features in Automatic Breast Ultrasound (ABUS) Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lamees%20Nasser">Lamees Nasser</a>, <a href="https://publications.waset.org/abstracts/search?q=Yago%20Diez"> Yago Diez</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Mart%C3%AD"> Robert Martí</a>, <a href="https://publications.waset.org/abstracts/search?q=Joan%20Mart%C3%AD"> Joan Martí</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Sadek"> Ibrahim Sadek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Automated%203D%20Breast%20Ultrasound" title="Automated 3D Breast Ultrasound">Automated 3D Breast Ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=Eigenvalues%20of%20Hessian%20matrix" title=" Eigenvalues of Hessian matrix"> Eigenvalues of Hessian matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=Nipple%20detection" title=" Nipple detection"> Nipple detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Rib%20detection" title=" Rib detection"> Rib detection</a> </p> <a href="https://publications.waset.org/abstracts/41104/robust-segmentation-of-salient-features-in-automatic-breast-ultrasound-abus-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41104.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">2808</span> Endocardial Ultrasound Segmentation using Level Set method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daoudi%20Abdelaziz">Daoudi Abdelaziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoudi%20Sa%C3%AFd"> Mahmoudi Saïd</a>, <a href="https://publications.waset.org/abstracts/search?q=Chikh%20Mohamed%20Amine"> Chikh Mohamed Amine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a fully automatic segmentation method of the left ventricle at End Systolic (ES) and End Diastolic (ED) in the ultrasound images by means of an implicit deformable model (level set) based on Geodesic Active Contour model. A pre-processing Gaussian smoothing stage is applied to the image, which is essential for a good segmentation. Before the segmentation phase, we locate automatically the area of the left ventricle by using a detection approach based on the Hough Transform method. Consequently, the result obtained is used to automate the initialization of the level set model. This initial curve (zero level set) deforms to search the Endocardial border in the image. On the other hand, quantitative evaluation was performed on a data set composed of 15 subjects with a comparison to ground truth (manual segmentation). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=level%20set%20method" title="level set method">level set method</a>, <a href="https://publications.waset.org/abstracts/search?q=transform%20Hough" title=" transform Hough"> transform Hough</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20smoothing" title=" Gaussian smoothing"> Gaussian smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=left%20ventricle" title=" left ventricle"> left ventricle</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20images." title=" ultrasound images."> ultrasound images.</a> </p> <a href="https://publications.waset.org/abstracts/19105/endocardial-ultrasound-segmentation-using-level-set-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19105.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">465</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">2807</span> Direct Integration of 3D Ultrasound Scans with Patient Educational Mobile Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zafar%20Iqbal">Zafar Iqbal</a>, <a href="https://publications.waset.org/abstracts/search?q=Eugene%20Chan"> Eugene Chan</a>, <a href="https://publications.waset.org/abstracts/search?q=Fareed%20Ahmed"> Fareed Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Jama"> Mohamed Jama</a>, <a href="https://publications.waset.org/abstracts/search?q=Avez%20Rizvi"> Avez Rizvi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advancements in Ultrasound Technology have enabled machines to capture 3D and 4D images with intricate features of the growing fetus. Sonographers can now capture clear 3D images and 4D videos of the fetus, especially of the face. Fetal faces are often seen on the ultrasound scan of the third trimester where anatomical features become more defined. Parents often want 3D/4D images and videos of their ultrasounds, and particularly image that capture the child’s face. Sidra Medicine developed a patient education mobile app called 10 Moons to improve care and provide useful information during the length of their pregnancy. In addition to general information, we built the ability to send ultrasound images directly from the modality to the mobile application, allowing expectant mothers to easily store and share images of their baby. 10 Moons represent the length of the pregnancy on a lunar calendar, which has both cultural and religious significance in the Middle East. During the third trimester scan, sonographers can capture 3D pictures of the fetus. Ultrasound machines are connected with a local 10 Moons Server with a Digital Imaging and Communications in Medicine (DICOM) application running on it. Sonographers are able to send images directly to the DICOM server by a preprogrammed button on the ultrasound modality. Mothers can also request which pictures they would like to be available on the app. An internally built DICOM application receives the image and saves the patient information from DICOM header (for verification purpose). The application also anonymizes the image by removing all the DICOM header information and subsequently converts it into a lossless JPEG. Finally, and the application passes the image to the mobile application server. On the 10 Moons mobile app – patients enter their Medical Record Number (MRN) and Date of Birth (DOB) to receive a One Time Password (OTP) for security reasons to view the images. Patients can also share the images anonymized images with friends and family. Furthermore, patients can also request 3D printed mementos of their child through 10 Moons. 10 Moons is unique patient education and information application where expected mothers can also see 3D ultrasound images of their children. Sidra Medicine staff has the added benefit of a full content management administrative backend where updates to content can be made. The app is available on secure infrastructure with both local and public interfaces. The application is also available in both English and Arabic languages to facilitate most of the patients in the region. Innovation is at the heart of modern healthcare management. With Innovation being one of Sidra Medicine’s core values, our 10 Moons application provides expectant mothers with unique educational content as well as the ability to store and share images of their child and purchase 3D printed mementos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=patient%20educational%20mobile%20application" title="patient educational mobile application">patient educational mobile application</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20images" title=" ultrasound images"> ultrasound images</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20imaging%20and%20communications%20in%20medicine%20%28DICOM%29" title=" digital imaging and communications in medicine (DICOM)"> digital imaging and communications in medicine (DICOM)</a>, <a href="https://publications.waset.org/abstracts/search?q=imaging%20informatics" title=" imaging informatics"> imaging informatics</a> </p> <a href="https://publications.waset.org/abstracts/98765/direct-integration-of-3d-ultrasound-scans-with-patient-educational-mobile-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98765.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">140</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">2806</span> Comparative Study Between Continuous Versus Pulsed Ultrasound in Knee Osteoarthritis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Mohamed%20Fawzy%20Ghuiba">Karim Mohamed Fawzy Ghuiba</a>, <a href="https://publications.waset.org/abstracts/search?q=Alaa%20Aldeen%20Abd%20Al%20Hakeem%20Balbaa"> Alaa Aldeen Abd Al Hakeem Balbaa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shams%20Elbaz"> Shams Elbaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objectives: To compare between the effects continuous and pulsed ultrasound on pain and function in patient with knee osteoarthritis. Design: Randomized-Single blinded Study. Participants: 6 patients with knee osteoarthritis with mean age 53.66±3.61years, Altman Grade II or III. Interventions: Subjects were randomly assigned into two groups; Group A received continuous ultrasound and Group B received pulsed ultrasound. Outcome measures: Effects of pulsed and continuous ultrasound were evaluated by pain threshold assessed by visual analogue scale (VAS) scores and function assessed by the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) scores. Results: There was no significant decrease in VAS and WOMAC scores in patients treated with pulsed or continuous ultrasound; and there were no significant differences between both groups. Conclusion: there is no difference between the effects of pulsed and continuous ultrasound in pain relief or functional outcome in patients with knee osteoarthritis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knee%20osteoarthritis" title="knee osteoarthritis">knee osteoarthritis</a>, <a href="https://publications.waset.org/abstracts/search?q=pulsed%20ultrasound" title=" pulsed ultrasound"> pulsed ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20therapy" title=" ultrasound therapy"> ultrasound therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20ultrasound" title=" continuous ultrasound"> continuous ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/45728/comparative-study-between-continuous-versus-pulsed-ultrasound-in-knee-osteoarthritis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45728.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">285</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">2805</span> CFD Modeling of Mixing Enhancement in a Pitted Micromixer by High Frequency Ultrasound Waves </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faezeh%20Mohammadi">Faezeh Mohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ebrahim%20Ebrahimi"> Ebrahim Ebrahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Neda%20Azimi"> Neda Azimi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Use of ultrasound waves is one of the techniques for increasing the mixing and mass transfer in the microdevices. Ultrasound propagation into liquid medium leads to stimulation of the fluid, creates turbulence and so increases the mixing performance. In this study, CFD modeling of two-phase flow in a pitted micromixer equipped with a piezoelectric with frequency of 1.7 MHz has been studied. CFD modeling of micromixer at different velocity of fluid flow in the absence of ultrasound waves and with ultrasound application has been performed. The hydrodynamic of fluid flow and mixing efficiency for using ultrasound has been compared with the layout of no ultrasound application. The result of CFD modeling shows well agreements with the experimental results. The results showed that the flow pattern inside the micromixer in the absence of ultrasound waves is parallel, while when ultrasound has been applied, it is not parallel. In fact, propagation of ultrasound energy into the fluid flow in the studied micromixer changed the hydrodynamic and the forms of the flow pattern and caused to mixing enhancement. In general, from the CFD modeling results, it can be concluded that the applying ultrasound energy into the liquid medium causes an increase in the turbulences and mixing and consequently, improves the mass transfer rate within the micromixer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CFD%20modeling" title="CFD modeling">CFD modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=mixing" title=" mixing"> mixing</a>, <a href="https://publications.waset.org/abstracts/search?q=mass%20transfer" title=" mass transfer"> mass transfer</a> </p> <a href="https://publications.waset.org/abstracts/102598/cfd-modeling-of-mixing-enhancement-in-a-pitted-micromixer-by-high-frequency-ultrasound-waves" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102598.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">182</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">2804</span> Ophthalmic Ultrasound in the Diagnosis of Retinoblastoma</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20Algaeed">Abdulrahman Algaeed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Ophthalmic Ultrasound is the easiest method of early diagnosing Retinoblastoma after clinical examination. It can be done with ease without sedation. King Khaled Eye Specialist Hospital is a tertiary care center where Retinoblastoma patients are often seen and treated there. The first modality to rule out the disease is Ophthalmic Ultrasound. Classic Retinoblastoma is easily diagnosed by using the conventional 10MHz Ophthalmic Ultrasound probe in the regular clinic setup. Retinal lesion with multiple, very highly reflective surfaces within lesion typical of Calcium deposits. The use of Standardized A-scan is very useful where internal reflectivity is classified as very highly reflective. Color Doppler is extremely useful as well to show the blood flow within lesion/s. In conclusion: Ophthalmic Ultrasound should be the first tool to be used to diagnose Retinoblastoma after clinical examination. The accuracy of the Exam is very high. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=doppler" title="doppler">doppler</a>, <a href="https://publications.waset.org/abstracts/search?q=retinoblastoma" title=" retinoblastoma"> retinoblastoma</a>, <a href="https://publications.waset.org/abstracts/search?q=reflectivity" title=" reflectivity"> reflectivity</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/165364/ophthalmic-ultrasound-in-the-diagnosis-of-retinoblastoma" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165364.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">113</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">2803</span> Network Conditioning and Transfer Learning for Peripheral Nerve Segmentation in Ultrasound Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harold%20Mauricio%20D%C3%ADaz-Vargas">Harold Mauricio Díaz-Vargas</a>, <a href="https://publications.waset.org/abstracts/search?q=Cristian%20Alfonso%20Jimenez-Casta%C3%B1o"> Cristian Alfonso Jimenez-Castaño</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Augusto%20C%C3%A1rdenas-Pe%C3%B1a"> David Augusto Cárdenas-Peña</a>, <a href="https://publications.waset.org/abstracts/search?q=Guillermo%20Alberto%20Ortiz-G%C3%B3mez"> Guillermo Alberto Ortiz-Gómez</a>, <a href="https://publications.waset.org/abstracts/search?q=Alvaro%20Angel%20Orozco-Gutierrez"> Alvaro Angel Orozco-Gutierrez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nerve%20segmentation" title="nerve segmentation">nerve segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=U-Net" title=" U-Net"> U-Net</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=ultrasound%20imaging" title=" ultrasound imaging"> ultrasound imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=peripheral%20nerve%20blocking" title=" peripheral nerve blocking"> peripheral nerve blocking</a> </p> <a href="https://publications.waset.org/abstracts/152338/network-conditioning-and-transfer-learning-for-peripheral-nerve-segmentation-in-ultrasound-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152338.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">2802</span> Automatic Differential Diagnosis of Melanocytic Skin Tumours Using Ultrasound and Spectrophotometric Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kristina%20Sakalauskiene">Kristina Sakalauskiene</a>, <a href="https://publications.waset.org/abstracts/search?q=Renaldas%20Raisutis"> Renaldas Raisutis</a>, <a href="https://publications.waset.org/abstracts/search?q=Gintare%20Linkeviciute"> Gintare Linkeviciute</a>, <a href="https://publications.waset.org/abstracts/search?q=Skaidra%20Valiukeviciene"> Skaidra Valiukeviciene</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cutaneous melanoma is a melanocytic skin tumour, which has a very poor prognosis while is highly resistant to treatment and tends to metastasize. Thickness of melanoma is one of the most important biomarker for stage of disease, prognosis and surgery planning. In this study, we hypothesized that the automatic analysis of spectrophotometric images and high-frequency ultrasonic 2D data can improve differential diagnosis of cutaneous melanoma and provide additional information about tumour penetration depth. This paper presents the novel complex automatic system for non-invasive melanocytic skin tumour differential diagnosis and penetration depth evaluation. The system is composed of region of interest segmentation in spectrophotometric images and high-frequency ultrasound data, quantitative parameter evaluation, informative feature extraction and classification with linear regression classifier. The segmentation of melanocytic skin tumour region in ultrasound image is based on parametric integrated backscattering coefficient calculation. The segmentation of optical image is based on Otsu thresholding. In total 29 quantitative tissue characterization parameters were evaluated by using ultrasound data (11 acoustical, 4 shape and 15 textural parameters) and 55 quantitative features of dermatoscopic and spectrophotometric images (using total melanin, dermal melanin, blood and collagen SIAgraphs acquired using spectrophotometric imaging device SIAscope). In total 102 melanocytic skin lesions (including 43 cutaneous melanomas) were examined by using SIAscope and ultrasound system with 22 MHz center frequency single element transducer. The diagnosis and Breslow thickness (pT) of each MST were evaluated during routine histological examination after excision and used as a reference. The results of this study have shown that automatic analysis of spectrophotometric and high frequency ultrasound data can improve non-invasive classification accuracy of early-stage cutaneous melanoma and provide supplementary information about tumour penetration depth. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cutaneous%20melanoma" title="cutaneous melanoma">cutaneous melanoma</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20diagnosis" title=" differential diagnosis"> differential diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=high-frequency%20ultrasound" title=" high-frequency ultrasound"> high-frequency ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=melanocytic%20skin%20tumours" title=" melanocytic skin tumours"> melanocytic skin tumours</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrophotometric%20imaging" title=" spectrophotometric imaging"> spectrophotometric imaging</a> </p> <a href="https://publications.waset.org/abstracts/94893/automatic-differential-diagnosis-of-melanocytic-skin-tumours-using-ultrasound-and-spectrophotometric-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94893.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">270</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">2801</span> Effect of Depth on Texture Features of Ultrasound Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Alqahtani">M. A. Alqahtani</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20P.%20Coleman"> D. P. Coleman</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20D.%20Pugh"> N. D. Pugh</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20D.%20M.%20Nokes"> L. D. M. Nokes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In diagnostic ultrasound, the echo graphic B-scan texture is an important area of investigation since it can be analyzed to characterize the histological state of internal tissues. An important factor requiring consideration when evaluating ultrasonic tissue texture is the depth. The effect of attenuation with depth of ultrasound, the size of the region of interest, gain, and dynamic range are important variables to consider as they can influence the analysis of texture features. These sources of variability have to be considered carefully when evaluating image texture as different settings might influence the resultant image. The aim of this study is to investigate the effect of depth on the texture features in-vivo using a 3D ultrasound probe. The left leg medial head of the gastrocnemius muscle of 10 healthy subjects were scanned. Two regions A and B were defined at different depth within the gastrocnemius muscle boundary. The size of both ROI’s was 280*20 pixels and the distance between region A and B was kept constant at 5 mm. Texture parameters include gray level, variance, skewness, kurtosis, co-occurrence matrix; run length matrix, gradient, autoregressive (AR) model and wavelet transform were extracted from the images. The paired t –test was used to test the depth effect for the normally distributed data and the Wilcoxon–Mann-Whitney test was used for the non-normally distributed data. The gray level, variance, and run length matrix were significantly lowered when the depth increased. The other texture parameters showed similar values at different depth. All the texture parameters showed no significant difference between depths A and B (p > 0.05) except for gray level, variance and run length matrix (p < 0.05). This indicates that gray level, variance, and run length matrix are depth dependent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20image" title="ultrasound image">ultrasound image</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20parameters" title=" texture parameters"> texture parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20biology" title=" computational biology"> computational biology</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical%20engineering" title=" biomedical engineering"> biomedical engineering</a> </p> <a href="https://publications.waset.org/abstracts/2991/effect-of-depth-on-texture-features-of-ultrasound-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2991.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">296</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">2800</span> Assessment of Kinetic Trajectory of the Median Nerve from Wrist Ultrasound Images Using Two Dimensional Baysian Speckle Tracking Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li-Kai%20Kuo">Li-Kai Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyh-Hau%20Wang"> Shyh-Hau Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The kinetic trajectory of the median nerve (MN) in the wrist has shown to be capable of being applied to assess the carpal tunnel syndrome (CTS), and was found able to be detected by high-frequency ultrasound image via motion tracking technique. Yet, previous study may not quickly perform the measurement due to the use of a single element transducer for ultrasound image scanning. Therefore, previous system is not appropriate for being applied to clinical application. In the present study, B-mode ultrasound images of the wrist corresponding to movements of fingers from flexion to extension were acquired by clinical applicable real-time scanner. The kinetic trajectories of MN were off-line estimated utilizing two dimensional Baysian speckle tracking (TDBST) technique. The experiments were carried out from ten volunteers by ultrasound scanner at 12 MHz frequency. Results verified from phantom experiments have demonstrated that TDBST technique is able to detect the movement of MN based on signals of the past and present information and then to reduce the computational complications associated with the effect of such image quality as the resolution and contrast variations. Moreover, TDBST technique tended to be more accurate than that of the normalized cross correlation tracking (NCCT) technique used in previous study to detect movements of the MN in the wrist. In response to fingers’ flexion movement, the kinetic trajectory of the MN moved toward the ulnar-palmar direction, and then toward the radial-dorsal direction corresponding to the extensional movement. TDBST technique and the employed ultrasound image scanner have verified to be feasible to sensitively detect the kinetic trajectory and displacement of the MN. It thus could be further applied to diagnose CTS clinically and to improve the measurements to assess 3D trajectory of the MN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=baysian%20speckle%20tracking" title="baysian speckle tracking">baysian speckle tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=carpal%20tunnel%20syndrome" title=" carpal tunnel syndrome"> carpal tunnel syndrome</a>, <a href="https://publications.waset.org/abstracts/search?q=median%20nerve" title=" median nerve"> median nerve</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20tracking" title=" motion tracking"> motion tracking</a> </p> <a href="https://publications.waset.org/abstracts/28816/assessment-of-kinetic-trajectory-of-the-median-nerve-from-wrist-ultrasound-images-using-two-dimensional-baysian-speckle-tracking-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28816.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">495</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">2799</span> Automated Ultrasound Carotid Artery Image Segmentation Using Curvelet Threshold Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Latha%20Subbiah">Latha Subbiah</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhanalakshmi%20Samiappan"> Dhanalakshmi Samiappan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose denoising Common Carotid Artery (CCA) B mode ultrasound images by a decomposition approach to curvelet thresholding and automatic segmentation of the intima media thickness and adventitia boundary. By decomposition, the local geometry of the image, its direction of gradients are well preserved. The components are combined into a single vector valued function, thus removes noise patches. Double threshold is applied to inherently remove speckle noise in the image. The denoised image is segmented by active contour without specifying seed points. Combined with level set theory, they provide sub regions with continuous boundaries. The deformable contours match to the shapes and motion of objects in the images. A curve or a surface under constraints is developed from the image with the goal that it is pulled into the necessary features of the image. Region based and boundary based information are integrated to achieve the contour. The method treats the multiplicative speckle noise in objective and subjective quality measurements and thus leads to better-segmented results. The proposed denoising method gives better performance metrics compared with other state of art denoising algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curvelet" title="curvelet">curvelet</a>, <a href="https://publications.waset.org/abstracts/search?q=decomposition" title=" decomposition"> decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=levelset" title=" levelset"> levelset</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/56351/automated-ultrasound-carotid-artery-image-segmentation-using-curvelet-threshold-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56351.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">340</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">2798</span> Effect of Ultrasound on the Hydrolysis of Soy Oil Catalyzed by 1,3-Specific Lipase Abstract </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamal%20Abd%20Awadallak">Jamal Abd Awadallak</a>, <a href="https://publications.waset.org/abstracts/search?q=Thiago%20Olinek%20Reinehr"> Thiago Olinek Reinehr</a>, <a href="https://publications.waset.org/abstracts/search?q=Eduardo%20Raizer"> Eduardo Raizer</a>, <a href="https://publications.waset.org/abstracts/search?q=Deise%20Molinari"> Deise Molinari</a>, <a href="https://publications.waset.org/abstracts/search?q=Edson%20Antonio"> Edson Antonio</a>, <a href="https://publications.waset.org/abstracts/search?q=Camila%20da%20Silva%20da%20Silva"> Camila da Silva da Silva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The hydrolysis of soy oil catalyzed by 1,3-specific enzyme (Lecitase Ultra) in a well-stirred bioreactor was studied. Two forms of applications of the ultrasound were evaluated aiming to increase reaction rates, wherein the use of probe ultrasound associated with the use of surfactant to pre-emulsify the substrate showed the best results. Two different reaction periods were found: the first where the ultrasound has great influence on reaction rates, and the second where ultrasound influence is minimal. Studies on the time of pre-emulsification, surfactant concentration and enzyme concentration showed that the initial rate of hydrolysis depends on the interfacial area between the oil phase and the aqueous phase containing the enzyme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=specific%20enzyme" title="specific enzyme">specific enzyme</a>, <a href="https://publications.waset.org/abstracts/search?q=free%20fatty%20acids" title=" free fatty acids"> free fatty acids</a>, <a href="https://publications.waset.org/abstracts/search?q=Hydrolysis" title=" Hydrolysis"> Hydrolysis</a>, <a href="https://publications.waset.org/abstracts/search?q=lecitase%20ultra" title=" lecitase ultra"> lecitase ultra</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/20337/effect-of-ultrasound-on-the-hydrolysis-of-soy-oil-catalyzed-by-13-specific-lipase-abstract" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20337.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">578</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">2797</span> Comparison of Back-Projection with Non-Uniform Fast Fourier Transform for Real-Time Photoacoustic Tomography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moung%20Young%20Lee">Moung Young Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Chul%20Gyu%20Song"> Chul Gyu Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Photoacoustic imaging is the imaging technology that combines the optical imaging and ultrasound. This provides the high contrast and resolution due to optical imaging and ultrasound imaging, respectively. We developed the real-time photoacoustic tomography (PAT) system using linear-ultrasound transducer and digital acquisition (DAQ) board. There are two types of algorithm for reconstructing the photoacoustic signal. One is back-projection algorithm, the other is FFT algorithm. Especially, we used the non-uniform FFT algorithm. To evaluate the performance of our system and algorithms, we monitored two wires that stands at interval of 2.89 mm and 0.87 mm. Then, we compared the images reconstructed by algorithms. Finally, we monitored the two hairs crossed and compared between these algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=back-projection" title="back-projection">back-projection</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20comparison" title=" image comparison"> image comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=non-uniform%20FFT" title=" non-uniform FFT"> non-uniform FFT</a>, <a href="https://publications.waset.org/abstracts/search?q=photoacoustic%20tomography" title=" photoacoustic tomography"> photoacoustic tomography</a> </p> <a href="https://publications.waset.org/abstracts/40584/comparison-of-back-projection-with-non-uniform-fast-fourier-transform-for-real-time-photoacoustic-tomography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40584.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">434</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">2796</span> Release of Calcein from Liposomes Using Low and High Frequency Ultrasound</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghaleb%20A.%20Husseini">Ghaleb A. Husseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Salma%20E.%20Ahmed"> Salma E. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Hesham%20G.%20Moussa"> Hesham G. Moussa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20M.%20Martins"> Ana M. Martins</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Al-Sayah"> Mohammad Al-Sayah</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasser%20Qaddoumi"> Nasser Qaddoumi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This abstract aims to investigate the use of targeted liposomes as anticancer drug carriers in vitro in combination with ultrasound applied as drug trigger; in order to reduce the side effects caused by traditional chemotherapy. Pegylated liposomes were used to encapsulate calcein and then release this model drug when 20-kHz, 40-kHz, 1-MHz and 3-MHz ultrasound were applied at different acoustic power densities. Fluorescence techniques were then used to measure the percent drug release of calcein from these targeted liposomes. Results showed that as the power density increases, at the four frequencies studied, the release of calcein also increased. Based on these results, we believe that ultrasound can be used to increase the rate and amount of chemotherapeutics release from liposomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=liposomes" title="liposomes">liposomes</a>, <a href="https://publications.waset.org/abstracts/search?q=calcein%20release" title=" calcein release"> calcein release</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20frequency%20ultrasound" title=" high frequency ultrasound"> high frequency ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20frequency%20ultrasound" title=" low frequency ultrasound"> low frequency ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=fluorescence%20techniques" title=" fluorescence techniques"> fluorescence techniques</a> </p> <a href="https://publications.waset.org/abstracts/24679/release-of-calcein-from-liposomes-using-low-and-high-frequency-ultrasound" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24679.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">426</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">2795</span> Scattering Operator and Spectral Clustering for Ultrasound Images: Application on Deep Venous Thrombi</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thibaud%20Berthomier">Thibaud Berthomier</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Mansour"> Ali Mansour</a>, <a href="https://publications.waset.org/abstracts/search?q=Luc%20Bressollette"> Luc Bressollette</a>, <a href="https://publications.waset.org/abstracts/search?q=Fr%C3%A9d%C3%A9ric%20Le%20Roy"> Frédéric Le Roy</a>, <a href="https://publications.waset.org/abstracts/search?q=Dominique%20Mottier"> Dominique Mottier</a>, <a href="https://publications.waset.org/abstracts/search?q=L%C3%A9o%20Fr%C3%A9chier"> Léo Fréchier</a>, <a href="https://publications.waset.org/abstracts/search?q=Barth%C3%A9l%C3%A9my%20Hermenault"> Barthélémy Hermenault</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep Venous Thrombosis (DVT) occurs when a thrombus is formed within a deep vein (most often in the legs). This disease can be deadly if a part or the whole thrombus reaches the lung and causes a Pulmonary Embolism (PE). This disorder, often asymptomatic, has multifactorial causes: immobilization, surgery, pregnancy, age, cancers, and genetic variations. Our project aims to relate the thrombus epidemiology (origins, patient predispositions, PE) to its structure using ultrasound images. Ultrasonography and elastography were collected using Toshiba Aplio 500 at Brest Hospital. This manuscript compares two classification approaches: spectral clustering and scattering operator. The former is based on the graph and matrix theories while the latter cascades wavelet convolutions with nonlinear modulus and averaging operators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20venous%20thrombosis" title="deep venous thrombosis">deep venous thrombosis</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasonography" title=" ultrasonography"> ultrasonography</a>, <a href="https://publications.waset.org/abstracts/search?q=elastography" title=" elastography"> elastography</a>, <a href="https://publications.waset.org/abstracts/search?q=scattering%20operator" title=" scattering operator"> scattering operator</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20clustering" title=" spectral clustering"> spectral clustering</a> </p> <a href="https://publications.waset.org/abstracts/80700/scattering-operator-and-spectral-clustering-for-ultrasound-images-application-on-deep-venous-thrombi" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80700.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">479</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">2794</span> 35 MHz Coherent Plane Wave Compounding High Frequency Ultrasound Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Chung%20Huang">Chih-Chung Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Po-Hsun%20Peng"> Po-Hsun Peng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ultrasound transient elastography has become a valuable tool for many clinical diagnoses, such as liver diseases and breast cancer. The pathological tissue can be distinguished by elastography due to its stiffness is different from surrounding normal tissues. An ultrafast frame rate of ultrasound imaging is needed for transient elastography modality. The elastography obtained in the ultrafast system suffers from a low quality for resolution, and affects the robustness of the transient elastography. In order to overcome these problems, a coherent plane wave compounding technique has been proposed for conventional ultrasound system which the operating frequency is around 3-15 MHz. The purpose of this study is to develop a novel beamforming technique for high frequency ultrasound coherent plane-wave compounding imaging and the simulated results will provide the standards for hardware developments. Plane-wave compounding imaging produces a series of low-resolution images, which fires whole elements of an array transducer in one shot with different inclination angles and receives the echoes by conventional beamforming, and compounds them coherently. Simulations of plane-wave compounding image and focused transmit image were performed using Field II. All images were produced by point spread functions (PSFs) and cyst phantoms with a 64-element linear array working at 35MHz center frequency, 55% bandwidth, and pitch of 0.05 mm. The F number is 1.55 in all the simulations. The simulated results of PSFs and cyst phantom which were obtained using single, 17, 43 angles plane wave transmission (angle of each plane wave is separated by 0.75 degree), and focused transmission. The resolution and contrast of image were improved with the number of angles of firing plane wave. The lateral resolutions for different methods were measured by -10 dB lateral beam width. Comparison of the plane-wave compounding image and focused transmit image, both images exhibited the same lateral resolution of 70 um as 37 angles were performed. The lateral resolution can reach 55 um as the plane-wave was compounded 47 angles. All the results show the potential of using high-frequency plane-wave compound imaging for realizing the elastic properties of the microstructure tissue, such as eye, skin and vessel walls in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=plane%20wave%20imaging" title="plane wave imaging">plane wave imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20frequency%20ultrasound" title=" high frequency ultrasound"> high frequency ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=elastography" title=" elastography"> elastography</a>, <a href="https://publications.waset.org/abstracts/search?q=beamforming" title=" beamforming"> beamforming</a> </p> <a href="https://publications.waset.org/abstracts/26703/35-mhz-coherent-plane-wave-compounding-high-frequency-ultrasound-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26703.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">539</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">2793</span> Dual-Channel Reliable Breast Ultrasound Image Classification Based on Explainable Attribution and Uncertainty Quantification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haonan%20Hu">Haonan Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuge%20Lei"> Shuge Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Dasheng%20Sun"> Dasheng Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Huabin%20Zhang"> Huabin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kehong%20Yuan"> Kehong Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Dai"> Jian Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=Jijun%20Tang"> Jijun Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on the classification task of breast ultrasound images and conducts research on the reliability measurement of classification results. A dual-channel evaluation framework was developed based on the proposed inference reliability and predictive reliability scores. For the inference reliability evaluation, human-aligned and doctor-agreed inference rationals based on the improved feature attribution algorithm SP-RISA are gracefully applied. Uncertainty quantification is used to evaluate the predictive reliability via the test time enhancement. The effectiveness of this reliability evaluation framework has been verified on the breast ultrasound clinical dataset YBUS, and its robustness is verified on the public dataset BUSI. The expected calibration errors on both datasets are significantly lower than traditional evaluation methods, which proves the effectiveness of the proposed reliability measurement. <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=ultrasound%20imaging" title=" ultrasound imaging"> ultrasound imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=XAI" title=" XAI"> XAI</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20measurement" title=" uncertainty measurement"> uncertainty measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=trustworthy%20AI" title=" trustworthy AI"> trustworthy AI</a> </p> <a href="https://publications.waset.org/abstracts/176771/dual-channel-reliable-breast-ultrasound-image-classification-based-on-explainable-attribution-and-uncertainty-quantification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176771.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">101</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">2792</span> Destruction of Atherosclerotic Plaque Using Pulse Ultrasound with a Planar Rectangular Ultrasound Transducer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christakis%20Damianou">Christakis Damianou</a>, <a href="https://publications.waset.org/abstracts/search?q=Christos%20Christofi"> Christos Christofi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicos%20Mylonas"> Nicos Mylonas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of the proposed study was to evaluate mechanical mode ultrasound using a flat rectangular (3x10 mm2) MRI compatible transducer operating at 5 MHz for destroying atherosclerotic plaque. The system was tested initially in a Hydroxyapatite-polyalactide (HA/PLA) model. An optimized protocol was decided and then applied in atherosclerotic plaque of a rabbit. The plaque in the rabbit was created using a high cholesterol diet. The atherosclerotic plaque was imaged using MRI. This study shows that the destruction of atherosclerotic plaque is feasible. <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=ultrasound" title=" ultrasound"> ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=atherosclerotic" title=" atherosclerotic"> atherosclerotic</a>, <a href="https://publications.waset.org/abstracts/search?q=plaque" title=" plaque"> plaque</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse" title=" pulse"> pulse</a> </p> <a href="https://publications.waset.org/abstracts/11014/destruction-of-atherosclerotic-plaque-using-pulse-ultrasound-with-a-planar-rectangular-ultrasound-transducer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11014.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">303</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">2791</span> Enabling Non-invasive Diagnosis of Thyroid Nodules with High Specificity and Sensitivity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sai%20Maniveer%20Adapa">Sai Maniveer Adapa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sai%20Guptha%20Perla"> Sai Guptha Perla</a>, <a href="https://publications.waset.org/abstracts/search?q=Adithya%20Reddy%20P."> Adithya Reddy P.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thyroid%20tumor%20diagnosis" title="thyroid tumor diagnosis">thyroid tumor diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20images" title=" ultrasound images"> ultrasound images</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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20auto-encoder" title=" convolutional auto-encoder"> convolutional auto-encoder</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/182971/enabling-non-invasive-diagnosis-of-thyroid-nodules-with-high-specificity-and-sensitivity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182971.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">58</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">2790</span> Application of Dual-Stage Sugar Substitution Technique in Tommy Atkins Mangoes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rafael%20A.%20B.%20De%20Medeiros">Rafael A. B. De Medeiros</a>, <a href="https://publications.waset.org/abstracts/search?q=Zilmar%20M.%20P.%20Barros"> Zilmar M. P. Barros</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20B.%20O.%20De%20Carvalho"> Carlos B. O. De Carvalho</a>, <a href="https://publications.waset.org/abstracts/search?q=Eunice%20G.%20Fraga%20Neta"> Eunice G. Fraga Neta</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20I.%20S.%20Maciel"> Maria I. S. Maciel</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20M.%20Azoubel"> Patricia M. Azoubel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of the sugar substitution technique (D3S) in mango was studied. It consisted of two stages and the use of ultrasound in one or both stages was evaluated in terms of water loss and solid gain. Higher water loss results were found subjecting the fruit samples to ultrasound in the first stage followed by immersion of the samples in Stevia-based solution with application of ultrasound in the second stage, while higher solids gain were obtained without application of ultrasound in second stage. Samples were evaluated in terms of total carotenoids content and total color difference. Samples submitted to ultrasound in both D3S stages presented higher carotenoid retention compared to samples sonicated only in the first stage. Color of man goes after the D3S process showed notable changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mangifera%20indica%20L." title="Mangifera indica L.">Mangifera indica L.</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a>, <a href="https://publications.waset.org/abstracts/search?q=Stevia%20rebaudiana" title=" Stevia rebaudiana"> Stevia rebaudiana</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/43926/application-of-dual-stage-sugar-substitution-technique-in-tommy-atkins-mangoes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43926.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">403</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">2789</span> Timeliness of Doppler Ultrasound for Suspected DVT in Compliance with National Guidelines: 4-Hours and 24-Hour Target</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadaf%20Shaikh">Sadaf Shaikh</a>, <a href="https://publications.waset.org/abstracts/search?q=Aishwariya%20Kapoor"> Aishwariya Kapoor</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustabshira%20Tahir"> Mustabshira Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Zille%20Huma"> Zille Huma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In accordance with national criteria, the purpose of this audit is to assess how quickly Doppler ultrasound can diagnose suspected deep vein thrombosis (DVT). In particular, it emphasizes the 4- and 24-hour ultrasound imaging targets that are advised by medical guidelines. In order to avoid consequences like pulmonary embolism or post-thrombotic syndrome, which might result from postponed treatment, it is imperative that DVT be diagnosed promptly. Data on patients who presented with suspected DVT during a certain time period were extracted from electronic health records as part of the retrospective analysis. The study's main goal is to evaluate the hospital's compliance with the deadlines for Doppler ultrasounds performed after a clinical suspicion of DVT. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DVT" title="DVT">DVT</a>, <a href="https://publications.waset.org/abstracts/search?q=NICE%20compliance" title=" NICE compliance"> NICE compliance</a>, <a href="https://publications.waset.org/abstracts/search?q=Doppler%20ultrasound" title=" Doppler ultrasound"> Doppler ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=d-dimer%20testing" title=" d-dimer testing"> d-dimer testing</a>, <a href="https://publications.waset.org/abstracts/search?q=vascular%20ultrasound" title=" vascular ultrasound"> vascular ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20delivery%20standards" title=" service delivery standards"> service delivery standards</a> </p> <a href="https://publications.waset.org/abstracts/195460/timeliness-of-doppler-ultrasound-for-suspected-dvt-in-compliance-with-national-guidelines-4-hours-and-24-hour-target" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/195460.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">2</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">2788</span> The Effect of Ultrasound as Pre-Treatment for Drying of Red Delicious and Golden Delicious Apples</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gulcin%20Yildiz">Gulcin Yildiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drying (dehydration) is the process of removing water from food in order to preserve the food and an alternative to reduce post-harvest loss of fruits. Different pre-treatment methods have been developed for fruit drying, such as ultrasound. If no pre-treatment is done, the fruits will continue to darken after they are dried. However, the effects of ultrasound as pre-treatment on drying of apples has not been well documented. This study was undertaken to investigate the effect of ultrasound as pre-treatment before oven drying of red delicious and golden delicious apples. Red delicious and golden delicious apples were dried in different temperatures. Before performing drying experiments in an oven at 50, 75 and 100 °C, ultrasound as pretreatment was applied in 5, 10, and 15 minutes. Colors of the dried apples were measured with a Minolta Chroma Meter CR-300 (Minolta Camera Co. Ltd., Osaka, Japan) by directly holding the device vertically to the surface of the samples. Content of total phenols was determined spectrophotometrically with the FolinCiocalteau assay, and the antioxidant capacity was evaluated by using 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay. The samples (both red delicious and golden delicious apples) with longer ultrasound treatment produced higher weight loss due to the changes in tissue structure. However less phenolic content and antioxidant capacity were observed for the samples with longer ultrasound pre-treatment. The highest total phenolic content (TPC) was determined in dried apples at 75 °C with 5 minutes pre-treatment ultrasound and the lowest TPC was determined in dried apples at 50 °C with 15 minutes pre-treatment ultrasound which was subjected to the longest ultrasound pre-treatment and drying. The combination of 5 min of ultrasound pre-treatment and 75 °C of oven-drying showed to be the best combination for an energy efficient process. This combination exhibited good antioxidant properties as well. The present study clearly demonstrated that applying ultrasound as pre-treatment for drying of apples is an effective process in terms of quality of dried products, time, and energy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=golden%20delicious%20apples" title="golden delicious apples">golden delicious apples</a>, <a href="https://publications.waset.org/abstracts/search?q=red%20delicious%20apples" title=" red delicious apples"> red delicious apples</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20phenolic%20content" title=" total phenolic content"> total phenolic content</a>, <a href="https://publications.waset.org/abstracts/search?q=Ultrasound" title=" Ultrasound"> Ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/88399/the-effect-of-ultrasound-as-pre-treatment-for-drying-of-red-delicious-and-golden-delicious-apples" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88399.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">296</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">2787</span> Using Self Organizing Feature Maps for Automatic Prostate Segmentation in TRUS Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahad%20Salimi">Ahad Salimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Masoumi"> Hassan Masoumi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prostate cancer is one of the most common recognized cancers in men, and, is one of the most important mortality factors of cancer in this group. Determining of prostate’s boundary in TRUS (Transrectal Ultra Sound) images is very necessary for prostate cancer treatments. The weakness edges and speckle noise make the ultrasound images inherently to segment. In this paper a new automatic algorithm for prostate segmentation in TRUS images proposed that include three main stages. At first morphological smoothing and sticks filtering are used for noise removing. In second step, for finding a point in prostate region, SOFM algorithm is enlisted and in the last step, the boundary of prostate extracting accompanying active contour is employed. For validation of proposed method, a number of experiments are conducted. The results obtained by our algorithm show the promise of the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SOFM" title="SOFM">SOFM</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=GVF%20contour" title=" GVF contour"> GVF contour</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/29731/using-self-organizing-feature-maps-for-automatic-prostate-segmentation-in-trus-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29731.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">2786</span> Reliable Soup: Reliable-Driven Model Weight Fusion on Ultrasound Imaging Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuge%20Lei">Shuge Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Haonan%20Hu"> Haonan Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dasheng%20Sun"> Dasheng Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Huabin%20Zhang"> Huabin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kehong%20Yuan"> Kehong Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Dai"> Jian Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Tong"> Yan Tong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20ultrasound%20image%20classification" title="breast ultrasound image classification">breast ultrasound image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20attribution" title=" feature attribution"> feature attribution</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability%20assessment" title=" reliability assessment"> reliability assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability%20optimization" title=" reliability optimization"> reliability optimization</a> </p> <a href="https://publications.waset.org/abstracts/176773/reliable-soup-reliable-driven-model-weight-fusion-on-ultrasound-imaging-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176773.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">85</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ultrasound%203D%20images&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ultrasound%203D%20images&page=3">3</a></li> <li class="page-item"><a class="page-link" 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