doi: 10.7763/IJCTE.2010.V2.206
An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing
T.Logeswari1
,
M.Karnan2
- 1University,Kodaikanal, India.
- 2department of Computer Science and Engineering,Tamilnadu College of Engineering,Coimbatore, India.
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.
Keywords
- Image analysis
- segmentation
- HSOM
- tumor detection
How to 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. https://doi.org/10.7763/IJCTE.2010.V2.206
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).