Abstract :
Edge detection in medical imaging is a signi cant task for object recognition
of human organs and is considered a pre-processing step in medical image segmentation
and reconstruction. This article proposes an ecient approach based on generalized Hill
entropy to nd a good solution for detecting edges under noisy conditions in medical
images. The proposed algorithm uses a two-phase thresholding: rstly, 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 nal 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 Prattʹs 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 di erent types of noise analyzed, and thus it can be considered as a very interesting
edge detection algorithm on noisy medical images
