JSEA  Vol.7 No.1 , January 2014
Generalized α-Entropy Based Medical Image Segmentation

In 1953, Rènyi introduced his pioneering work (known as α-entropies) to generalize the traditional notion of entropy. The functionalities of α-entropies share the major properties of Shannons entropy. Moreover, these entropies can be easily estimated using a kernel estimate. This makes their use by many researchers in computer vision community greatly appealing. In this paper, an efficient and fast entropic method for noisy cell image segmentation is presented. The method utilizes generalized α-entropy to measure the maximum structural information of image and to locate the optimal threshold desired by segmentation. To speed up the proposed method, computations are carried out on 1D histograms of image. Experimental results show that the proposed method is efficient and much more tolerant to noise than other state-of-the-art segmentation techniques.

Cite this paper
S. Sadek and S. Abdel-Khalek, "Generalized α-Entropy Based Medical Image Segmentation," Journal of Software Engineering and Applications, Vol. 7 No. 1, 2014, pp. 62-67. doi: 10.4236/jsea.2014.71007.
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