Improved skin lesions detection using color space and artificial intelligence techniques

Background: Automatic skin lesion image identification is of utmost importance to develop a fully automatized computer-aided skin analysis system. This will be helping the medical practitioners to provide skin lesions disease treatment more efficiently and effectively.Material and method: In this article, two image processing techniques for accurate detection of skin lesions have been proposed. In first technique, the optimization of edge detection has been carried out by using a branch of artificial intelligence called nature inspired algorithm. Ant colony optimization (ACO) is used to increase effectiveness of edge detection in skin lesion. The second technique deals with the color space-based split-and-merge process in combination with global thresholding segmentation and edge smoothing operations.Result: The performance of both techniques has been measured by entropy performance evaluation parameter. The results show remarkable improvement in output images obtained by Canny edge detection technique optimized by ACO in comparison with ACO-Sobel, ACO-Prewitt and Edge Smoothing-Color Space techniques.Conclusion: ACO-Canny Edge detection technique shows far better effieciency for skin lesion detection as compared to ACO-Sobel, ACO-Prewitt and Edge Smoothing Color Space technique.