WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 11, 2014
Performance Analysis of Feature Extraction and Selection of Region of Interest by Segmentation in Mammogram Images between the Existing Meta-heuristic Algorithms and Monkey Search Optimization (MSO)
Authors: ,
Abstract: In medical image processing, feature selection and extraction is an important task for performing image classification and recognition which is performed through the image segmentation process. This paper proposes a different approach; Monkey Search Optimization (MSO) which is based on Metaheuristic Algorithm is presented for selecting region of interest in mammogram image. Monkey Search Optimization (MSO) algorithm is considered as a new algorithm searching for optimum solution based on the foraging behavior of monkeys. Pectoral region removed image is given as input for feature extraction. The proposed algorithm can be implemented for various applications as the time consumption for the process is reduced greatly. In this paper the proposed algorithm is compared with few other meta-heuristics algorithms such as Ant Colony Optimization (ACO), Artificial Bee Colony Optimization (ABC) and Particle Swarm Optimization (PSO); from the results that the proposed approach can be considered to be an appropriate algorithm for image segmentation. Results are presented based on simulation made with the implementation in MATLAB which is tested on the images of MIAS database.
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Pages: 72-82
WSEAS Transactions on Information Science and Applications, ISSN / E-ISSN: 1790-0832 / 2224-3402, Volume 11, 2014, Art. #8