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Abstract-Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization.

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Abstract-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-Means clustering algorithm for the Segmentation is presented

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