Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model

A neuro-fuzzy model for fast detection of candidate circumscribed masses in digitized mammograms is presented. The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl de...

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Bibliographic Details
Main Authors: Youssry, N., Abou-Chadi, F.E.Z., El-Sayad, A.M.
Format: Conference Proceeding
Language:English
Subjects:
Breast cancer
Breast tissue
Cancer detection
Feature extraction
Histograms
Image edge detection
Image segmentation
Lesions
Mammography
Neoplasms
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Description
Summary:A neuro-fuzzy model for fast detection of candidate circumscribed masses in digitized mammograms is presented. The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl deg/) are calculated and texture features are estimated for each co-occurrence matrix, then the features are used to train neuro-fuzzy models. The classification results reach 100% for abnormal cases and 80% for normal ones.
DOI:10.1109/NRSC.2003.1217380