Automatic Extraction of Edge Topography in Medical Images Using Ant Colony Optimization Algorithm and Image Processing Techniques

Edge detection in image processing is one of the main techniques used in segmentation, separation and detection of the special parts of the image. The presence of noise and structural anomaly due to the weak local contrast of the medical images are of the reasons that prevent the current operators from accurate detection of the edge in these images. In this paper, the metaheuristic colony algorithm has been used for edge detection in medical images. Rapid convergence to obtain the optimal solution along with the parameters resistant to initialization has increased the efficiency of the algorithm. In different parts of the image, especially the part with pathological damage, the edge is assumed as ant’s food. Receiving 222 medical images composed of 02 retina images taken from diabetic patients, 02 MRI images as well as 02 microscopic images taken from various medical databases and applying system to them in contrast to such known operators as Canny and Sobel, an acceptable level of accuracy 09. 02%, sensitivity 09. 61% and specificity 09% was separated in the target area from the rest of image. The 00. 90% Kappa coefficient indicates the high reliability factor of system in terms of performance. The use of the current combination method for processing of the images has increased the accuracy even in images with high brightness, rendering the F-Measure significant. The accurate extraction of pathological parts from medical images allows the specialist to determine the disease progression stage, and suggest an appropriate treatment in accordance with the disease growth.