Using complex Gabor filters to detect and localize edges and bars
Advances in machine vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Computerized detection of breast masses in digitized mammograms
Computers in Biology and Medicine
Computers in Biology and Medicine
Classification of benign and malignant masses based on Zernike moments
Computers in Biology and Medicine
Fast opposite weight learning rules with application in breast cancer diagnosis
Computers in Biology and Medicine
Breast mass contour segmentation algorithm in digital mammograms
Computer Methods and Programs in Biomedicine
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In this paper, four new features for the analysis of breast masses are presented. These features were designed to be insensitive to the exact shape of the contour of the masses, so that an approximate contour, such as the one extracted via an automated segmentation algorithm, can be employed in their computation. Two of the features, Sp"S"I and Sp"G"O, measure the degree of spiculation of a mass and its likelihood of being spiculated. One of these features, Sp"G"O, is a measure of the relative gradient orientation of pixels that correspond to possible spicules. The other feature, Sp"S"I, is based on a comparison of mutual information measures between selected components of the mammographic images. The last two features, Fz"1 and Fz"2, measure the local fuzziness of the mass margins based on points defined automatically. The features were tested for characterization (i.e. discrimination between circumscribed and spiculated masses) and diagnosis (i.e. discrimination between benign and malignant masses) of breast masses using a set of 319 masses and three different classifiers. In the characterization experiments the features produced a result of approximately 89% correct classification. In the diagnosis experiments, the performance achieved was approximately 81% of correct classification.