TITLE:
Generalized α-Entropy Based Medical Image Segmentation
AUTHORS:
Samy Sadek, Sayed Abdel-Khalek
KEYWORDS:
α-Entropy; Cell Image; Entropic Image Segmentation
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.7 No.1,
January
21,
2014
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.