Fuzzy expert systems
The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
Pattern recognition and image analysis
Pattern recognition and image analysis
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
A hybrid approach to MR imaging segmentation using unsupervised clustering and approximate reducts
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Matching 2d image segments with genetic algorithms and approximation spaces
Transactions on Rough Sets V
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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.