Generalized α-Entropy Based Medical Image Segmentation

Samy Sadek, Sayed Abdel-Khalek

Department of Mathematics, Faculty of Science, Sohag University, Sohag, Egypt.
Mathematics Department, Faculty of Science, Taif Univesity, Taif, KSA.

DOI: 10.4236/jsea.2014.71007                

Abstract

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 Shannon’s 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.

Keywords

α-Entropy; Cell Image; Entropic Image Segmentation

Conflicts of Interest

The authors declare no conflicts of interest.

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