Improved implementation of brain MRI image segmentation using Ant Colony System

Author

Karnan, M. ; Logheshwari, T.

Author_Institution

Dept. of Comput. Sci., Mother Theresa Univ., Kodaikanal, India

fYear

2010

fDate

28-29 Dec. 2010

Firstpage

1

Lastpage

4

Abstract

Ant Colony Optimization (ACO) metaheuristic is a recent population-based approach inspired by the observation of real ants colony and based upon their collective foraging behavior. In This paper, the proposed technique ACO hybrid with Fuzzy segmentation. In the first step, the MRI brain image is Segmented Aco Hybrid with Fuzzy method to extract the suspicious region. In the second step deals with similarity between proposed segmented algorithms and Radiologist report. The tumor position and pixel similarity of the Aco Hybrid with Fuzz techniques are measured with Radiologist report.

Keywords

biomedical MRI; brain; fuzzy systems; image matching; image segmentation; medical image processing; object detection; optimisation; patient diagnosis; radiology; tumours; Aco hybrid segmentation; MRI brain image; ant colony system; brain MRI image segmentation; collective foraging behavior; fuzzy method; fuzzy segmentation; pixel similarity; population based approach; radiologist report; tumor position; Brain; Cancer; Classification algorithms; Image segmentation; Magnetic resonance imaging; Pixel; Tumors; ACO; HSOM; MRI Brain Image analysis; fuzzy C-Mean; tumor detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on

Conference_Location

Coimbatore

Print_ISBN

978-1-4244-5965-0

Electronic_ISBN

978-1-4244-5967-4

Type

conf

DOI

10.1109/ICCIC.2010.5705897

Filename

5705897