Modified OTSU Algorithm Using Quartered Search Method

In order to reduce the computational complexity of the OTSU algorithm, in this paper, improved OTSU algorithm using quartered search method has been proposed. When compute the optimal segmentation threshold, divide the whole gray level into four regions, and then determine the region of the optimal segmentation threshold according to the Monotonicity of maximum classbetween variance. The proposed algorithm avoids calculating the maximum class-between variance traversing the whole grayscale, so we can find the optimal segmentation threshold only traversing the quarter region of gray level; it overcomes the shortcoming of huge calculation by using Otsu algorithm to search optimal threshold value. Experimental results show that this method can find reliable optimal threshold value, and improve the processing speed. It is easy for improved Otsu algorithm to meet real-time processing requirements. Introduction Image Segmentation is one of the basic techniques of image processing and computer vision. It is a key step for image analysis, comprehension and description. Among all the segmentation techniques, thresholding segmentation method [1, 2] is the most popular algorithm and is widely used in image segmentation field. The basic idea of thresholding is to automatically select one or several optimal gray-level thresholds for separating objects of interest in an image from background based on their gray-level distribution. Basically thresholding methods can be categorized into two categories: global thresholding and local thresholding [3]. Commonly used global thresholding methods are iterative method, Otsu method [4], maximum entropy method [5, 6], and so on. Otsu’s method is a typical method of global thresholding, which selects the threshold using the maximum of between-class variance. Even in the case of unimodal histogram images, it can still provide a satisfactory result. For multi-object and non-uniform illumination images, however, Otsu’s approach misses many lower brightness objects [7]. Based on the gray-level histogram of an image, Otsu’s thresholding method takes the variance between clusters as the criterion to select the optimal threshold [8]. This method has been cited as an effective thresholding technique and widely used in real thresholding tasks. However, there are always uncertain disturbing factors in practical applications. These disturbing factors probably make one-dimensional (1D) histogram fail to possess obvious valley between two peaks [9, 10]. As a result, Otsu method cannot obtain optimal segmentation results. But the main drawback of the OTSU algorithm is its large amount of calculation. The OTSU algorithm calculates the maximum class-between variance traversing the whole grayscale, so it leads to the shortcoming of huge calculation by using Otsu algorithm to search optimal threshold value, and the computational complexity of the algorithm reaches O (L2) because it calculates a variance for each gray value in the gray level. Thus it is hardly for Otsu to meet real-time processing requirements. So the segmentation method needs to be improved. The rest of this paper is organized as follows. Section 2 introduces the segmentation process in general, and thresholding technique in particular; it also reviews Otsu method of thresholding. In Section 3, modified OTSU algorithm using quartered search method is presented. While section 4 is concerned with the experimental results carried out. Section 5 is about the main conclusions of this study.