I. Levner and H. Zhang, “Classification-Driven Watershed Segmentation,” EEE Transactions on Image Processing, Vol. 16, No. 5, 2007, pp. 1437-1445. http://dx.doi.org/10.1109/TIP.2007.894239

has been cited by the following article:

• 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.