Segmentation of Medical Images Based on Approximations in Rough Set Theory

This paper presents an image segmentation method based on rough set theory. The focus of this paper is to discuss how to approximate a region of interest (ROI) when we are given multiple types of expert knowledge. The method contains three steps including preprocessing. First, we derive discretized attribute values that describe the characteristics of a ROI. Secondly, using all attributes, we build up the basic regions (namely categories) in the image so that each region contains voxels that are indiscernible on all attributes. Finally, according to the given knowledge about the ROI, we construct an ideal shape of the ROI and approximate it by the basic categories. Then the image is split into three regions: a set of voxels that are - (1) certainly included in the ROI (Positive region), (2) certainly excluded from the ROI (Negative region), (3) possibly included in the ROI (Boundary region). The ROI is consequently represented by the positive region associated with some boundary regions. In the experiments we show the result of implementing a rough image segmentation system.