A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

Abstract

Edge detection in medical imaging is a significant task for object recognition of human organs and is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an efficient approach based on generalized Hill entropy to find a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: firstly, a global threshold calculated by means of generalized Hill entropy is used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The final edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared to Canny and Tsallis entropy using sets of medical images corrupted by various types of noise. We used Pratt's Figure Of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicated that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four different types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images.