Images sub-segmentation with the PFCM clustering algorithm

In this work we propose a method for sub-segmentation of images using the PFCM clustering algorithm. The sub-segmentation consists of finding, within the clusters found using the segmentation process, those data less representative, or atypical data, belonging to the clusters. These data represent, in many cases, the zones of interest during image analysis. Two different examples are used in order to show the results, and the advantages of identifying those elements of data forced to belong to a cluster, of which they are the less representative and, therefore may contain information of great interest in particular applications.