Segmenting object with ambiguous boundary using information theoretic rough sets

In image segmentation, image is divided into regions of similar pixels that satisfy a defined notion of similarity. The complexity of image segmentation is further increased when the separation between neighboring regions is ambiguous. In this paper, we propose an approach that uses the information theoretic rough sets concept (ITRS) to model the ambiguous boundary of the object for further segmentation. The advantage of this approach is incorporating the prior knowledge of the object for effective extraction despite its ambiguous boundary. This approach starts with an assumption that seed points of the regions are available. It then computes the probability of association of the pixels with the seed points. Rough sets theory is invoked on this probability or likelihood map to identify positive, negative, and boundary states of the object. Optimal threshold for the boundary region is determined using histogram based segmentation algorithm for final object extraction. The main contribution relies on the application of ITRS in categorizing the object by combining both the prior and image information. The proposed approach, ITRS segmentation, is compared with different image segmentation methods on simulated brain images, and the result is encouraging with its state-of-the-art performance.