TY - CHAP
T1 - Fuzzy Image Segmentation for Mass Detection in Digital Mammography
T2 - Recent Advances and Techniques
AU - Alharbi, Hajar Mohammedsaleh H
AU - Kwan, Paul H
AU - Jayawardena, Ashoka
AU - Sajeev, Abudulkadir
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Pattern Recognition and Data Mining
KW - Image Processing
KW - Neural, Evolutionary and Fuzzy Computation
UR - http://trove.nla.gov.au/work/163586506
UR - https://www.scopus.com/pages/publications/84898126912
U2 - 10.4018/978-1-4666-1830-5.ch021
DO - 10.4018/978-1-4666-1830-5.ch021
M3 - Chapter
SN - 9781466618329
SN - 9781466618312
SN - 9781466618305
T3 - Premier Reference Source
SP - 378
EP - 402
BT - Multidisciplinary Computational Intelligence Techniques: Applications in Business, Engineering and Medicine
A2 - Ali, Shawkat
A2 - Abbadeni, Noureddine
A2 - Batouche, Mohamed
PB - Information Science Reference
CY - Hershey, United States of America
ER -