Fuzzy Image Segmentation for Mass Detection in Digital Mammography: Recent Advances and Techniques

Fuzzy Image Segmentation for Mass Detection in Digital Mammography: Recent Advances and Techniques

Hajar Mohammedsaleh H. Alharbi, Paul Kwan, Ashoka Jayawardena, A. S. M. Sajeev
DOI: 10.4018/978-1-4666-1830-5.ch021
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Abstract

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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Background

According to the Oxford Dictionary, cancer is a disease that is caused by an uncontrolled division of abnormal cells in a certain part of the body (Oxford Dictionary, n.d.). The result of this unusual growth is normally in the form of a mass or tumor. Most types of cancer are named according to the part of the body in which the cancer first arises. Breast cancer can thus be considered an uncontrolled growth of cells in the breast tissue. A human female’s breast (illustrated in Figure 1) consists of lobules and ducts, which are surrounded by fatty and connective tissues. Lobules are glands that produce milk, while ducts connect lobules and carry milk to the nipple.

Figure 1.

A female human breast (© 2008, Don Bliss, National Cancer Institute, Public domain)

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