Finding Orientated Line Patterns in Digital Mammographic Images
Author(s) -
Reyer Zwiggelaar,
T.C. Parr,
Chris Taylor
Publication year - 1996
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.10.70
Subject(s) - mammography , computer science , artificial intelligence , computer vision , digital mammography , orientation (vector space) , line (geometry) , pattern recognition (psychology) , medicine , mathematics , breast cancer , geometry , cancer
In mammography the presence of subtle abnormalities such as stellate patterns and architectual distortions indicates possible malignancy. Radiologists do not always detect these abnormalities in screening mammograms; this has led to interest in computer‐aided mammographic interpretation where the radiologist is presented with computer‐generated ’prompts’ for abnormalities. A first step in this process is the detection of the “orientation”, “scale” and “strength” of linear structures in the mammograms. We discuss several generic methods for extracting this information from images and compare their per formance using synthetic images intended to simulate the appearance of mam mograms. We show significant differences in performance between the different methods. We also show results obtained for real mammograms.
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