Machine Learning

Diagnostic Enhancement of Screening Mammograms by Means of Local Texture Models

   Jiří Grim    Petr Somol    Michal Haindl    Jan Daneš

Statistically based preprocessing of screening mammograms is proposed with the aim to in-crease the diagnostic conspicuity of mammographic lesions. We estimate first the local statis-tical texture model of a single mammogram as a joint probability density of grey levels in a suitably chosen search window. The probability density in the form of multivariate Gaussian mixture is estimated from data obtained by pixel-wise scanning the mammogram with the search window. In the second phase we evaluate the estimated density at each position of the window and display the corresponding log-likelihood value as grey level at window center. Light grey levels correspond to the typical parts of the image and the dark values reflect unusual places. The resulting log-likelihood image closely correlates with fine structural details of the original mammogram and facilitates diagnostic interpretation of suspect abnormalities.

Feature Selection Algorithms

   Petr Somol    Pavel Pudil

Demonstrations of Feature Selection Algorithms.
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