Mixture Models

Diagnostic Enhancement of Screening Mammograms by Means of Local Texture Models

   Jiří Grim    Petr Somol    Michal Haindl    Jan Daneš

Abstract: 
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

Abstract: 
Demonstrations of Feature Selection Algorithms.

Mixtures and Classification