IMPROVING CLUSTERING RESULTS USING RE-EVALUATION OF BOUNDARY DATA

Image segmentation is preliminary stage in diagnosis tools and the accurate segmentation of brain images is crucial for a correct diagnosis by these tools. Due to inhomogeneity, low contrast, noise and inequality of content with semantic; brain MRI image segmentation is a challenging job. A post processing algorithm for improvement of clustering accuracy is proposed. The proposed algorithm re-evaluates boundary data to reduce clustering error. Proposed algorithm quantitatively evaluated by applying the mentioned algorithm on two recently reported clustering algorithms. The proposed algorithm improved clustering results and gives comparable results when user interaction is applied to the clustering algorithms.