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In this paper, we investigate the classification of masses with texture features. We propose an improved level set method to find the boundary of a mass, based on the initial contour provided by radiologists. After the boundary of a mass is found, texture features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the surrounding area of the boundary of the mass. The extracted texture...
In this article, we present a new mass description dedicated to differentiate between different mass shapes in mammography. This discrimination aims to reach a better mammography classification rate to be used by radiologists as a second opinion to make the final decision about the malignancy probability of radiographic breast images. Therefore, we used a geometrical feature which is perimeter measurement...
Female breast cancer is the major cause of death in occidental countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. We propose a methodology to distinguish Mass and Non-Mass tissues on mammograms. It is based on the computation of geostatistical measures (Moran's Index and Geary's Coefficient) over a multiresolution image representation...
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