Evidential Segmentation of Microscopic Color Images with Pixel Classification Posterior Probabilities

A tool for diagnosis assistance by automatic segmentation of microscopic cellular images is introduced. This method is based on an automatic segmentation technique combining (with the Dempster-Shafer rule) the results obtained by Support Vector Machines (SVM) applied within different color spaces. This combination is performed by integrating uncertainties and redundancies for each color space. Those uncertainties are computed as a posteriori probabilities according to the SVM obtained results. An improvement of the final segmentation quality is performed by taking into account the inconsistencies of several pixel classifications.