An improved OTSU method based on Genetic Algorithm
Image segmentation is required to be studied in detail some particular features (areas of interest) of a digital image. It forms an important and exigent part of image processing and requires an exhaustive and robust search technique for its implementation. In the present work we have studied working of image thresholding based on genetic algorithm. The proposed variant in this paper is tested on a set of images and the results are compared with original Otsu method. Introduction Threshold selection plays an important role in image segmentation; relevant experts have widely studied on the threshold automatic selection. The most useful methods in practice are mainly as minimum error method, iterative method, entropy method and Otsu method. [1,2,3] The original Otsu method might be as fast as possible, but it takes exhaustive optimization method and extends the calculation time. It's not good enough to solve the situation of foreground and background crossing in gray level statistics. The Otsu method mainly include: ant colony algorithm, BP neural network, genetic algorithm, expert system, etc. The genetic algorithm [4] uses the probabilistic optimization algorithm, in which the global space is searched to find the optimal solution. In this paper, taking the advantage of genetic algorithm and Otsu method, the author put forward a method to automatic select the optimal threshold for image using improved Otsu algorithm with genetic algorithm. Related Work Otsu method is an adaptive threshold selection method, this method was proposed by Japanese scholar Nobuyuki Otsu in 1979. The principle of the method is that the image is divided into two classes, the target and the background by searching an optimal threshold in the range of image gray level. Its evaluation criterion is the between class variance of the two classes. The larger the variance, the greater the difference between the target and the background and the better the segmentation results. The smaller the variance, the more probability the two classes to be divided and the worse the segmentation results. [5] Suppose that the gray level is LL(used to be 256) and the number of pixels of gray level at ii is nnii, then the number of total pixels of an image is NN nnii LL 1 ii=0 the probability that each gray level appears is ppii nnii NN The Otsu method selects gray level k as the segmentation threshold, and divides the image into two classes: the background class named WW1 which gray level ranges from 0 to kk, 4th International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2016) Copyright (c) 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY NC license (http:/creativecommons.org/licenses/by nc/4.0/ Advances in Computer Science Research, volume 71