An Automatic System for Classification of Breast Cancer Lesions in Ultrasound Images PDF Download

Author: Behnam Karimi
Publisher:
ISBN:
Category :
Languages : en
Pages :

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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: 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: Alberto Tagliafico
Publisher: Springer
ISBN: 3319286315
Category : Medical
Languages : en
Pages : 156

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Book Description
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.

Author: Acr
Publisher:
ISBN: 9781559030168
Category : Breast
Languages : en
Pages : 689

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Author: Marek Gzik
Publisher: Springer Nature
ISBN: 3030991121
Category : Technology & Engineering
Languages : en
Pages : 341

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Book Description
This book presents the latest developments in the field of biomedical engineering and includes practical solutions and strictly scientific considerations. The development of new methods of treatment, advanced diagnostics or personalized rehabilitation requires close cooperation of experts from many fields, including, among others, medicine, biotechnology and finally biomedical engineering. The latter, combining many fields of science, such as computer science, materials science, biomechanics, electronics not only enables the development and production of modern medical equipment, but also participates in the development of new directions and methods of treatment. The presented monograph is a collection of scientific papers on the use of engineering methods in medicine. The topics of the work include both practical solutions and strictly scientific considerations expanding knowledge about the functioning of the human body. We believe that the presented works will have an impact on the development of the field of science, which is biomedical engineering, constituting a contribution to the discussion on the directions of development of cooperation between doctors, physiotherapists and engineers. We would also like to thank all the people who contributed to the creation of this monograph—both the authors of all the works and those involved in technical works.

Author: Dina Abdelhafiz
Publisher:
ISBN:
Category : Breast
Languages : en
Pages : 0

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Book Description
Breast cancer is the second leading cause of cancer deaths among women in the USA. Mammography is the preferred screening tool for breast cancer and accounts for the greatest contribution to the early detection of breast cancer. The detection of breast masses in mammogram (MG) images using deep learning (DL) systems is a challenging task due to the varying sizes, shapes, and textures of masses. In this thesis, we propose a novel DL network called residual attention UNet (RAU-Net), the network pays attention to small lesions, and shows superior performance compared to the other state-of-the-arts DL models in detecting and segmenting masses, especially for heterogeneously dense and dense MG images. The proposed RAU-Net model achieves a mean dice coefficient index of 0.98 and mean intersection over union of 0.94. We propose a DL residual network for classification of MG images into benign and malignant that achieved accuracy of 0.95, and AUC of 0.98. We also propose a one-shot multi-input Siamese network that learns features from previous and current year MG images of the same patient to give a better assessment for current year MG images. The detection of mass tumors in dense tissues and, more generally, in dense breasts is often considered more challenging due to the similar visual aspects of normal and abnormal dense tissues. In this thesis, we present a training algorithm that we used to train various kinds of U-Net networks such as RCNN-UNet, AU-Net, RAU-Net,and UNet++ to generate density attention masks that automatically pays attention and gives more weight to tumors in dense regions of MG images. To train and test our models, we collected and pre-processed MG images that come with different resolutions from public repositories and MG images from UCONN health center. In conclusion, we proposed DL systems for lesion detection, segmentation, and classification in mammography that can aid radiologists and serve as a second eye for them.

Author: Abdulkader Helwan
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659689765
Category :
Languages : en
Pages : 96

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Book Description
This book aims to develop an intelligent breast cancer identification (ICBIS) system based on image processing techniques and neural network classifier. Recently, many researchers have developed image recognition systems for classifying breast cancer tumors using different image processing and classification techniques. The challenge is the extraction of the real features that distinguish the benign and malignant tumor. The classifications of breast cancer images have been performed using the shape and texture characteristics of the images. The asymmetry, roundness, intensity levels and more are the exact shape and texture features that distinguish the two types of breast tumors. Image processing techniques are used in order to detect tumor and extract the region of interest from the mammogram. The following data processing operations have been done for detection of images: thresholding, filtering and adjustments, canny edge detection, and some morphological operations. Shape and texture features are then extracted using GLCM (Gray-Level Co-Occurrence Matrix) algorithm in order to accurately classify the mammograms into normal, benign, and malignant tumors.

Author: 簡政良
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.