A comparative performance study of several global thresholding techniques for segmentation

A comparative performance study of five global thresholding algorithms for image segmentation was investigated. An image database with a wide variety of histogram distribution was constructed. The histogram distribution was changed by varying the object size and the mean difference between object and background. The performance of five algorithms was evaluated using the criterion functions such as the probability of error, shape, and uniformity measures Attempts also have been made to evaluate the performance of each algorithm on the noisy image. Computer simulation results reveal that most algorithms perform consistently well on images with a bimodal histogram. However, all algorithms break down for a certain ratio of population of object and background pixels in an image, which in practice may arise quite frequently. Also, our experiments show that the performances of the thresholding algorithms discussed in this paper are data-dependent. Some analysis is presented for each of the five algorithms based on the performance measures.