Automated 3D Breast Ultrasound Image Analysis PDF Download
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Author: Tao Tan
Publisher:
ISBN:
Category :
Languages : en
Pages : 124
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Book Description
Author: Tao Tan
Publisher:
ISBN:
Category :
Languages : en
Pages : 124
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Book Description
Author: Veronika Gazhonova
Publisher: Springer
ISBN: 3319419714
Category : Medical
Languages : en
Pages : 133
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Book Description
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.
Author: H.D.Cheng
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
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.
Author: Julia Schwaab
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Min Xian
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 71
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Book Description
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.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Richa Agarwal
Publisher:
ISBN:
Category :
Languages : en
Pages : 108
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Book Description
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.
Author: Aaron Fenster
Publisher: CRC Press
ISBN: 1003823998
Category : Science
Languages : en
Pages : 283
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Book Description
• 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
Author: D. Jude Hemanth
Publisher: Elsevier
ISBN: 0443140006
Category : Computers
Languages : en
Pages : 350
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Book Description
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
Author: Marc Lobbes
Publisher: Springer
ISBN: 303011063X
Category : Medical
Languages : en
Pages : 160
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Book Description
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.
