文章基本信息

标题：Generalized α-Entropy Based Medical Image Segmentation
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作者：Samy Sadek ; Sayed Abdel-Khalek
期刊名称：Journal of Software Engineering and Applications
印刷版ISSN：1945-3116
电子版ISSN：1945-3124
出版年度：2014
卷号：7
期号：1
页码：62-67
DOI：10.4236/jsea.2014.71007
出版社：Scientific Research Publishing
摘要：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.
关键词：<i>α</i>-Entropy; Cell Image; Entropic Image Segmentation