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.
Index Terms—Image analysis, segmentation, HSOM, tumor detection.
T.Logeswari is a Research Scholar, with the Dept of computer Science, Mother Teresa Women’s University,Kodaikanal, 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).
[PDF]
Cite: T.Logeswari and M.Karnan, "An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing,"
International Journal of Computer Theory and Engineering vol. 2, no. 4, pp. 586-590, 2010.
