Download or read book Automated 3D Breast Ultrasound Image Analysis written by Tao Tan and published by . This book was released on 2014 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book 3D Automated Breast Volume Sonography written by Veronika Gazhonova and published by Springer. This book was released on 2016-11-26 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces an exciting new method for breast ultrasound diagnostics – automated whole-breast volume scanning (3D ABVS). Scanning technique is described in detail, with guidance on scanning positions and protocols. Imaging findings are then illustrated and discussed for normal breast variants, the different forms of breast cancer, fibroadenomas, cystic disease, benign and malignant male breast disorders, mastitis, breast implants, and postoperative breast scars. In order to aid appreciation of the benefits of 3D ABVS, comparisons with findings on X-ray mammography and conventional 2D hand-held US are presented. Readers will be especially impressed by the convincing demonstration of the advantages of the new method for diagnosis of breast cancer in women with dense glandular tissue. In enabling readers to learn how to perform and interpret 3D ABVS, this book will be of great value for all who are embarking on its use. It will also serve as a welcome reference for radiologists, oncologists, and ultrasonographers who already have some familiarity with the technique.
Download or read book Automated breast cancer detection and classification using ultrasound images A survey written by H.D.Cheng and published by Infinite Study. This book was released on with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast.
Download or read book Computer aided Image Quality Assessment in Automated 3D Breast Ultrasound Images written by Julia Schwaab and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computer aided Image Quality Assessment in Automated 3D Breast Ultrasound Images written by and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Breast Ultrasound Image Segmentation A Survey written by Min Xian and published by Infinite Study. This book was released on with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is one of the leading causes of cancer death among women worldwide. In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning.
Download or read book Computer Aided Detection for Breast Lesion in Ultrasound and Mammography written by Richa Agarwal and published by . This book was released on 2020 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM). Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis.Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging.In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used. The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screening.
Download or read book 3D Ultrasound written by Aaron Fenster and published by CRC Press. This book was released on 2023-12-22 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: • Provides descriptions of mechanical, tracking, and array approaches for generating 3D ultrasound images • Details the applications of 3D ultrasound for diagnostic application and in image-guided intervention and surgery • Explores the cutting-edge use of machine learning in detection, diagnosis, monitoring, and guidance for a variety of clinical applications
Download or read book State of the Art in Digital Mammographic Image Analysis written by K. W. Bowyer and published by World Scientific. This book was released on 1994 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a detailed assessment of the state of the art in automated techniques for the analysis of digital mammogram images. Topics covered include a variety of approaches for image processing and pattern recognition aimed at assisting the physician in the task of detecting tumors from evidence in mammogram images. The chapters are written by recognized experts in the field and are revised versions of papers selected from those presented at the “First International Workshop on Mammogram Image Analysis” held in San Jose as part of the 1993 Biomedical Image Processing conference.
Download or read book A Benchmark for Breast Ultrasound Image Segmentation BUSIS written by Min Xian and published by Infinite Study. This book was released on with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Computer-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been proposed in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which result in discrepancy in performance comparison.
Download or read book Contrast Enhanced Mammography written by Marc Lobbes and published by Springer. This book was released on 2019-04-29 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive guide to contrast-enhanced mammography (CEM), a novel advanced mammography technique using dual-energy mammography in combination with intravenous contrast administration in order to increase the diagnostic performance of digital mammography. Readers will find helpful information on the principles of CEM and indications for the technique. Detailed attention is devoted to image interpretation, with presentation of case examples and highlighting of pitfalls and artifacts. Other topics to be addressed include the establishment of a CEM program, the comparative merits of CEM and MRI, and the roles of CEM in screening populations and monitoring of response to neoadjuvant chemotherapy. CEM became commercially available in 2011 and is increasingly being used in clinical practice owing to its superiority over full-field digital mammography. This book will be an ideal source of knowledge and guidance for all who wish to start using the technique or to learn more about it.
Download or read book Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images written by D. Jude Hemanth and published by Elsevier. This book was released on 2023-11-16 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images comprehensively examines the wide range of AI-based mammogram analysis methods for medical applications. Beginning with an introductory overview of mammogram data analysis, the book covers the current technologies such as ultrasound, molecular breast imaging (MBI), magnetic resonance (MR), and Positron Emission mammography (PEM), as well as the recent advancements in 3D breast tomosynthesis and 4D mammogram. Deep learning models are presented in each chapter to show how they can assist in the efficient processing of breast images. The book also discusses hybrid intelligence approaches for early-stage detection and the use of machine learning classifiers for cancer detection, staging and density assessment in order to develop a proper treatment plan. This book will not only aid computer scientists and medical practitioners in developing a real-time AI based mammogram analysis system, but also addresses the issues and challenges with the current processing methods which are not conducive for real-time applications. - Presents novel ideas for AI based mammogram data analysis - Discusses the roles deep learning and machine learning techniques play in efficient processing of mammogram images and in the accurate defining of different types of breast cancer - Features dozens of real-world case studies from contributors across the globe
Download or read book Breast Imaging written by Debra M. Ikeda and published by Core Requisites. This book was released on 2016-11-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its 3rd Edition, this bestselling volume in the popular Requisites series, by Drs. Debra M. Ikeda and Kanae K. Miyake, thoroughly covers the fast-changing field of breast imaging. Ideal for residency, clinical practice and certification and MOC exam study, it presents everything you need to know about diagnostic imaging of the breast, including new BI-RADS standards, new digital breast tomosynthesis (DBT) content and videos, ultrasound, and much more. Compact and authoritative, it provides up-to-date, expert guidance in reading and interpreting mammographic, ultrasound, DBT, and MRI images for efficient and accurate detection of breast disease. Features over 1,300 high-quality images throughout. Summarizes key information with numerous outlines, tables, ''pearls, '' and boxed material for easy reference. Focuses on essentials to pass the boards and the MOC exam and ensure accurate diagnoses in clinical practice. Expert ConsultT eBook version included with purchase. This enhanced eBook experience allows you to search all of the text, figures, and references from the book on a variety of devices and features DBT videos. All-new Breast Imaging-Reporting and Data System (BI-RADS) recommendations for management and terminology for mammography, elastography in ultrasound, and MRI. Step-by-step guidance on how to read new 3D tomosynthesis imaging studies with example cases, including limitations, pitfalls, and 55 new DBT videos. More evidence on the management of high risk breast lesions. Correlations of ultrasound, mammography, and MRI with tomosynthesis imaging. Detailed basis of contrast-enhanced MRI studies. Recent nuclear medicine techniques such as FDG PET/CT, NaF PET.
Download or read book 2013 ACR BI RADS Atlas written by Acr and published by . This book was released on 2014-01-31 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mammographic Image Analysis written by Ralph Highnam and published by Springer Science & Business Media. This book was released on 1999 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key contribution of the approach to x-ray mammographic image analysis developed in this monograph is a representation of the non-fatty compressed breast tissue that we show can be derived from a single mammogram. The importance of the representation, called hint, is that it removes all those changes in the image that are due only to the particular imaging conditions (for example, the film speed or exposure time), leaving just the non-fatty 'interesting' tissue. Normalising images in this way enables them to be enhanced and matched, and regions in them to be classified more reliably, because unnecessary, distracting variations have been eliminated. Part I of the monograph develops a model-based approach to x-ray mammography, Part II shows how it can be put to work successfully on a range of clinically-important tasks, while Part III develops a model and exploits it for contrast-enhanced MRI mammography. The final chapter points the way forward in a number of promising areas of research.
Download or read book A Fully Automatic Segmentation Method for Breast Ultrasound Images written by Juan Shan and published by . This book was released on 2011 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a challenging task. This research focuses on developing a novel, effective, and fully automatic lesion segmentation method for breast ultrasound images. By incorporating empirical domain knowledge of breast structure, a region of interest is generated. Then, a novel enhancement algorithm (using a novel phase feature) and a newly developed neutrosophic clustering method are developed to detect the precise lesion boundary. Neutrosophy is a recently introduced branch of philosophy that deals with paradoxes, contradictions, antitheses, and antinomies. When neutrosophy is used to segment images with vague boundaries, its unique ability to deal with uncertainty is brought to bear. In this work, we apply neutrosophy to breast ultrasound image segmentation and propose a new clustering method named neutrosophic l-means. We compare the proposed method with traditional fuzzy c-means clustering and three other well-developed segmentation methods for breast ultrasound images, using the same database. Both accuracy and time complexity are analyzed. The proposed method achieves the best accuracy (TP rate is 94.36%, FP rate is 8.08%, and similarity rate is 87.39%) with a fairly rapid processing speed (about 20 seconds). Sensitivity analysis shows the robustness of the proposed method as well. Cases with multiple-lesions and severe shadowing effect (shadow areas having similar intensity values of the lesion and tightly connected with the lesion) are not included in this study.
Download or read book Digital Breast Tomosynthesis written by Alberto Tagliafico and published by Springer. This book was released on 2016-05-03 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive description of the screening and clinical applications of digital breast tomosynthesis (DBT) and offers straightforward, clear guidance on use of the technique. Informative clinical cases are presented to illustrate how to take advantage of DBT in clinical practice. The importance of DBT as a diagnostic tool for both screening and diagnosis is increasing rapidly. DBT improves upon mammography by depicting breast tissue on a video clip made of cross‐sectional images reconstructed in correspondence with their mammographic planes of acquisition. DBT results in markedly reduced summation of overlapping breast tissue and offers the potential to improve mammographic breast cancer surveillance and diagnosis. This book will be an excellent practical teaching guide for beginners and a useful reference for more experienced radiologists.
