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 meta-heuristic 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
٢٢٠ medical images composed of ٩٠ retina images taken from diabetic patients, ٨٠ MRI
images as well as ٥٠ 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 ٩٤.٩٠%, sensitivity ٩٤.١٦% and specificity ٩٤% was separated in the target
area from the rest of image. The ٨٨.٧٩% 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.