Mitra, Sushmita ; Barman, Bishal Rough-Fuzzy Clustering: An Application to Medical Imagery In: Rough Sets and Knowledge Technology, Springer, Berlin.
Full text not available from this repository.
Official URL: https://doi.org/10.1007/978-3-540-79721-0_43
Related URL: http://dx.doi.org/10.1007/978-3-540-79721-0_43
Abstract
A novel application of rough-fuzzy clustering is presented for synthetic as well as CT scan images of the brain. It is observed that the algorithm generates good prototypes even in the presence of outliers. The rough-fuzzy clustering simultaneously handles overlap of clusters and uncertainty involved in class boundary, thereby yielding the best approximation of a given structure in unlabeled data. The number of clusters is automatically optimized in terms of various validity indices. A comparative study is made with related partitive algorithms. Experimental results demonstrate the diagnosis of the extent of brain infarction in CT scan images, and is validated by medical experts.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to Springer Nature. |
| Keywords: | Rough-fuzzy clustering; Cluster validation; Image segmentation; CT scan imaging. |
| ID Code: | 140157 |
| Deposited On: | 07 Sep 2025 05:06 |
| Last Modified: | 07 Sep 2025 05:06 |
Repository Staff Only: item control page
