A multilevel thresholding algorithm for image segmentation based on particle swarm optimization

Thresholding is a popular image segmentation method that converts gray-level image into binary image. The problem of thresholding has been quite extensively studied for many years in order to get an optimum threshold value. The multi-level thresholding becomes very computationally challenges. In this paper, a novel multilevel thresholding method based on particle swarm optimization (PSO) algorithm is proposed, or it seems to be the best tool, to maximize the Kapur and Otsu objective functions. We employed the properties of discriminate analysis using Kapur and Otsu methods to render the optimal thresholding technics more applicable and effective. The obtained result and the comparative study illustrate the algorithm's outstanding performances in segmenting both the grey level image and the MRI scans.