Detection of Breast Cancer Microcalcifications in Mammograms - AbuBaker, Ayman;Qahwaji, Rami
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Breast cancer is the second leading cause of cancer deaths in women, exceeded only by lung cancer. Earlier detection, through non-invasive mammography scanning is extremely important for reducing the death rates from breast cancer. In this book we introduce an automated Computer Aided Diagnosis (CAD) system that can accurately detect cancer cells at early stages and reduce the workload of radiologists. The CAD system integrates image processing and machine learning technologies to simulate the way radiologists detect Microcalcifications in mammograms. The CAD system is tested on Benchmark data…mehr

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Produktbeschreibung
Breast cancer is the second leading cause of cancer deaths in women, exceeded only by lung cancer. Earlier detection, through non-invasive mammography scanning is extremely important for reducing the death rates from breast cancer. In this book we introduce an automated Computer Aided Diagnosis (CAD) system that can accurately detect cancer cells at early stages and reduce the workload of radiologists. The CAD system integrates image processing and machine learning technologies to simulate the way radiologists detect Microcalcifications in mammograms. The CAD system is tested on Benchmark data sets and reliable performance is achieved. This book will be useful for professionals interested in medical imaging technologies and also the development of CAD systems. It will be also relevant to academics and research students dealing with imaging, machine learning and medical research.
Autorenporträt
Rami Qahwaji and Ayman AbuBaker received their PhD from school of Informatics in 2002 and 2008 respectively. Qahwaji is Professor in Visual Computing at Bradford University. AbuBaker is Asst. Prof. in Faculty of Computer Engineering, Applied Science University. Their research interests include: 2D/3D image processing, satellite and medical imaging.