Edge Detection of Medical Images Using Modified Ant Colony Optimization Algorithm based on Weighted Heuristics
Автор: Puneet Rai
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 3 vol.6, 2014 года.
Бесплатный доступ
Ant Colony Optimization (ACO) is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.
Ant Colony Optimization, Weighted Heuristics, Edge Detection, Pheromone
Короткий адрес: https://sciup.org/15013267
IDR: 15013267
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