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Fuzzy Image Segmentation for Mass Detection in Digital Mammography: Recent Advances and Techniques |
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| Author(s) |
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Alharbi, Hajar Mohammedsaleh H |
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| Editor |
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Editor(s): Shawkat Ali, Noureddine Abbadeni and Mohamed Batouche |
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| Publisher |
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Information Science Reference |
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Hershey, United States of America |
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| DOI |
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10.4018/978-1-4666-1830-5.ch021 |
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| Abstract |
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In the last decade, many computer-aided diagnosis (CAD) systems that utilize a broad range of diagnostic techniques have been proposed. Due to both the inherently complex structure of the breast tissues and the low intensity contrast found in most mammographic images, CAD systems that are based on conventional techniques have been shown to have missed malignant masses in mammographic images that would otherwise be treatable. On the other hand, systems based on fuzzy image processing techniques have been found to be able to detect masses in cases where conventional techniques would have failed. In the current chapter, recent advances in fuzzy image segmentation techniques as applied to mass detection in digital mammography are reviewed. Image segmentation is an important step in CAD systems since the quality of its outcome will significantly affect the processing downstream that can involve both detection and classification of benign versus malignant masses. |
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Multidisciplinary Computational Intelligence Techniques: Applications in Business, Engineering and Medicine, p. 378-402 |
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