An efficient method for noisy cell image segmentation using generalized α-entropy

In 1953, a functional extension by A. Rènyi to generalize traditional Shannon's entropy known as α-entropies was proposed. 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 highly 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.