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General Information
Prof. Mehmet Sahinoglu
Faculty at Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2010 Vol.2(4): 591-595 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2010.V2.207

An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map

T. Logeswari and M. Karnan
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

Index Terms—Image analysis, Segmentation, HSOM, Fuzzy C-Means, Tumor detection

T. Logeswari is a Research Scholar, with the Dept of Computer Science, Mother Theresa women’s University, Kodaikkanal, India (email: saralogu4uin@gmail.com ).
M.Karnan is with the Department of Computer Science and Engineering, Tamilnadu College of Engineering, Coimbatore, India (email: drmkarnan@gmail.com).


Cite: T. Logeswari and M. Karnan, "An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map," International Journal of Computer Theory and Engineering vol. 2, no. 4, pp. 591-595, 2010.

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