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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 one extracted via an automated segmentation algorithm, can be employed in their computation. The features measure the degree of spiculation of a mass and the local fuzziness of the mass margins. The...
A method for detection of masses in mammograms is presented. This method follows the general scheme of: (1) preprocessing of the image to increase the signal-to-noise ratio of the lesions being detected, (2) segmentation of all potential lesions, and (3) elimination of false-positive findings. An algorithm for enhancement of mammograms is proposed which has the objective of improving the segmentation...
A dataset of 57 breast mass mammographic images, each with 22 features computed, was used in this investigation. The extracted features relate to edge-sharpness, shape, and texture. The novelty of this paper is the adaptation and application of genetic programming (GP). To refine the pool of features available to the GP classifier, we used five feature-selection methods, including three statistical...
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