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MRT letter: Segmentation and texture‐based classification of breast mammogram images
Author(s) -
Naveed Nawazish,
Jaffar M. Arfan,
Choi TaeSun
Publication year - 2011
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.21070
Subject(s) - artificial intelligence , mammography , breast cancer , segmentation , pattern recognition (psychology) , computer science , wiener filter , filter (signal processing) , computer vision , noise (video) , support vector machine , texture (cosmology) , image (mathematics) , cancer , medicine
Breast cancer is the most common cancer diagnosed among women. In this article, support vector machine is used to classify digital mammogram images into malignant and benign. Wiener filter is used to handle the possible quantum noise, which is more likely to occur in mammograms. Stack‐based connected component method is proposed for background removal, and the image is enhanced using retinax method. Seeded region growing algorithm is used to remove the pectoral muscle part of the mammogram. We have extracted 13 different multidomains' features for classification. Results show the superiority of the proposed algorithm in terms of sensitivity, specificity, and accuracy. We have used MIAS database of mammography for experimentation. Microsc. Res. Tech., 2011. © 2011 Wiley Periodicals, Inc.