DocumentCode :
2403495
Title :
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
Link To Document :
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