Image segmentation using bi-level thresholding

Automatic thresholding is an important technique in the image segmentation process. The basic idea of automatic thresholding is to select an optimal gray-level threshold value automatically for separating object of interest in an image from the background based on their gray-level distribution. In this work, four image thresholding methods namely, Between class variance (Otsu´s), Total class variance (Hou´s), Posterior maximum entropy (kapur´s) and Minimum error Thresholding are performed for image segmentation. The Pixel based performance measures such as False positive rate, False negative rate, Misclassified pixels and Average absolute error are calculated for above methods. The Posterior maximum entropy method gives better result than the other methods.