Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
International Journal of Hybrid Intelligent Systems
Web-Based Support Systems with Rough Set Analysis
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
A class of rough multiple objective programming and its application to solid transportation problem
Information Sciences: an International Journal
Generalized rough fuzzy c-means algorithm for brain MR image segmentation
Computer Methods and Programs in Biomedicine
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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.