Image segmentation is one of the most difficult and challenging tasks in many image processing. Several general-purpose algorithms and techniques have been developed in medical application. Image processing describes the analysis images and obtaining desired segmentation results. Many researchers have used various types of techniques to review the images. The goal of image segmentation is to partition an image into more meaningful and easier use to analyze the various features of that image. Segmentation techniques are involving detection, recognition, and measurement of features. The segmentation algorithm is based on color and gray value images. Among all the segmentation methods, the fundamental approach to segment an image is based on the intensity levels and is called a threshold-based. One of the widely used techniques is thresholding. Thresholding is the simplest approaches to separate object from the background and it is widely used for medical image processing. Thresholding technique creates a grayscale image into a binary image. Thresholdng and edge detection are an important technique in image processing. Thresholding techniques can be classified into two thresholdings. These are global thresholding and local thresholding. The local thresholding method is divideded the original image into small sub images. It determines a threshold value for each of subimages. Global thresholding method determines a single threshold value in the whole images. The thresholding values are depending upon the spatial coordinates. In this paper, we analyze an efficient segmentation of for three different thresholding methods. These methods are Otsu’s, Feng’s and Sauvola methods. The three thresholding algorithms have been simulated in MATLAB.