基于蚁群优化的疟疾寄生虫边缘检测

Damandeep Kaur, G. K. Walia
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引用次数: 4

摘要

蚁群优化算法(Ant colony optimization, ACO)是一种基于蚁群自然行为的优化算法。在这种情况下,蚂蚁保留了在地面觅食的信息素。蚁群分析法最初用于检测受疟疾影响的血样显微图像的边缘。采用蚁群算法的边缘检测方法维护信息素矩阵,该信息素矩阵确定在每个图像像素位置提供的边缘信息。蚂蚁的运动,被分配到在图像上的运动。因此,图像强度值的变化决定了蚂蚁的运动。研究了一种蚁群边缘检测方法。
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Edge detection of Malaria parasites using ant colony optimization
Ant colony optimization (ACO) is algorithm used for optimization motivated by the natural behaviour of species of ants. In this ants retain pheromone for foraging at the ground. ACO has been originated to detect the edges of microscopic images of blood samples which are affected by malaria disease. The edge detection approach of ACO is used to maintain pheromone matrix which determines the information of edges provided at every image pixel position, based on no. of ants movement that are dispatched to be in motion on the image. Thus, changes in the intensity values of images determine the movement of the ants. The results have been taken to study an approach for Ant Colony Edge Detection method.
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