An Improved Canny Edge Detection Algorithm Based on Type-2 Fuzzy Sets

Abstract Canny's edge detection algorithm is a classical and robust method for edge detection in gray-scale images. The two significant features of this method are introduction of NMS (Non-Maximum Suppression) and double thresholding of the gradient image. Due to poor illumination, the region boundaries in an image may become vague, creating uncertainties in the gradient image. In this paper, we have proposed an algorithm based on the concept of type-2 fuzzy sets to handle uncertainties that automatically selects the threshold values needed to segment the gradient image using classical Canny's edge detection algorithm. The results show that our algorithm works significantly well on different benchmark images as well as medical images (hand radiography images).