A novel two phases Petri net image segmentation based on rough sets and roughness entropy

Abstract

Petri net expresses concurrent events, which is very helpful for the segmentation and connection of the subsets with the image contour subsets. Combined with rough sets or roughness entropy theory for context related connections, the unexpected constraints of Petri net aiming to control precising image segmentation can be very challenging, especially in cases of sensitivity to vague and indistinct features and other inherent complicating aspects of image medical inhomogeneity. Our main contribution in this paper is to propose a two phases Petri net approach achieving forward or backward correction on multiple boundary choices, based on a rough set and roughness entropy, with the aim of more accurate and efficient medical image segmentation. The first phase in our method is to obtain an approximate contour using rough sets and roughness entropy. The second phase is to achieve a more exact contour match via unexpected constraints of a Petri net – a technique offering advantage for parallel and multiple choices applications. Experiments demonstrate that our proposed method can handle vague, uncertain and intensity inhomogeneous segmentation problems, especially where there are several subset contours needed to achieve good image segmentation.