Masi entropy based multilevel thresholding for image segmentation

Abstract

A new multilevel thresholding based image segmentation technique is developed which utilizes Masi entropy as an objective function. Thresholding is an important image segmentation technique. It may be divided into two types such as bi-level and multilevel thresholding. Bi-level thresholding uses a single threshold to classify an image into two classes: object and the background. For an image containing a single object in a distinct background, bi-level thresholding can be successfully used for segmentation. But in case of complex images containing multiple objects, bi-level thresholding often fails to give satisfactory segmentation. In such cases, multilevel thresholding is generally preferred over bi-level thresholding. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are generally used to optimize the threshold searching process to reduce the computational complexity involved in multilevel thresholding. In this paper, Particle Swarm Optimization (PSO) along with Masi entropy is proposed for multilevel thresholding based image segmentation. The proposed technique is evaluated using a set of standard test images. The proposed technique is compared with the recently proposed Dragonfly Algorithm (DA) based technique that uses Kapur’s entropy as objective function. The proposed technique is also compared with PSO based technique that uses minimum cross entropy (MCE) as objective function. The quality of the segmented images is measured using Mean Structural SIMilarity (MSSIM) index and Peak Signal-to-Noise Ratio (PSNR). The experimental results suggest that the proposed technique outperforms Kapur’s entropy and gives very competitive result when compared with the MCE based technique. Further, computational complexity of multilevel thresholding is also greatly reduced.