Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies
Applied Soft Computing
Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
Information Sciences: an International Journal
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Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Among them, the clustering methods have been extensively investigated and used. In this paper, a clustering based approach using a Self Organizing Map (SOM) algorithm is proposed for medical image segmentation. This paper describe segmentation method consists of two phases. In the first phase, the MRI brain image is acquired from patient database. In that film artifact and noise are removed. In the second phase (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. A new unsupervised MR image segmentation method based on fuzzy C-Mean clustering algorithm for the Segmentation is presented