Improvement of the Image Sub-Segmentation for Identification and Differentiation of Atypical Regions

Abstract

A new sub-segmentation method has been proposed in 2009 which, in digital images, help us to identify the typical pixels, as well as the less representative pixels or atypical of each segmented region. This method is based on the Possibilistic Fuzzy c-Means (PFCM) clustering algorithm, as it integrates absolute and relative memberships. Now, the segmentation problem is related to isolate each one of the objects present in an image. However, and considering only one segmented object or region represented by gray levels as its only feature, the totality of pixels is divided in two basic groups, the group of pixels representing the object, and the others that do not represent it. In the former group, there is a sub-group of pixels near the most representative element of the object, the prototype, and identified here as the typical pixels, and a sub-group corresponding to the less representative pixels of the object, which are the atypical pixels, and generally located at the borders of the pixels representing the object. Besides, the sub-group of atypical pixels presents greater tones (brighter or towards the white color) or smaller tones (darker or towards black color). So, the sub-segmentation method offers the capability to identify the sub-region of atypical pixels, although without performing a differentiation between the brighter and the darker ones. Hence, the proposal of this work contributes to the problem of image segmentation with the improvement on the detection of the atypical sub-regions, and clearly recognizing between both kind of atypical pixels, because in many cases only the brighter or the darker atypical pixels are the ones that represent the object of interest in an image, depending on the problem to be solved. In this study, two real cases are used to show the contribution of this proposal; the first case serves to demonstrate the pores detection in soil images represented by the darker atypical pixels, and the second one to demonstrate the detection of microcalcifications in mammograms, represented in this case by the brighter atypical pixels.