Rough representation of a region of interest in medical images Original Research Article

This paper introduces the rough representation of a region of interest (ROI) in medical images. The main advantage of this method is its ability to represent inconsistency between the knowledge-driven shape and image-driven shape of a ROI using rough approximations. The method consists of three steps including preprocessing. First, we derive discretized attribute values that describe the characteristics of a ROI. Next, using all attributes, we build up the basic regions in the image so that each region includes 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.