An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing

Author

Logeswari, T. ; Karnan, M.

Author_Institution

Dept of Comput. Sci., Mother Teresa Women´s Univ., Kodaikanal, India

fYear

2010

fDate

9-10 Feb. 2010

Firstpage

243

Lastpage

247

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

biomedical MRI; image classification; image segmentation; medical image processing; self-organising feature maps; tumours; MRI brain image; brain tumor detection; hierarchical self organizing map; image classification; medical image segmentation; patients database; soft computing; vector quantization; Biomedical imaging; Brain; Image databases; Image segmentation; Magnetic resonance imaging; Neoplasms; Organizing; Phase noise; Tumors; Vector quantization; HSOM; Image analysis; segmentation; tumor detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Acquisition and Processing, 2010. ICSAP '10. International Conference on

Conference_Location

Bangalore

Print_ISBN

978-1-4244-5724-3

Electronic_ISBN

978-1-4244-5725-0

Type

conf

DOI

10.1109/ICSAP.2010.55

Filename

5432723