Multilevel Thresholding based Image Segmentation using Optimization Algorithm

The process of image segmentation is very important in the context of image analysis. Segmentation makes it easier to analyze a portion of the image one has to deal with. The application of image segmentation is immense in the areas of machine vision, medical imaging, locating objects in satellite images, object detection, recognition tasks and various other fields of science and engineering. In recent times nature-inspired algorithms are being used for both bi-level and multilevel thresholding based image segmentation. In this paperthe performances of the Grasshopper Optimization Algorithm and the Whale Optimization Algorithm are evaluatedfor image segmentation.The performances of the proposed methods are compared with Cuckoo Search Algorithmalong with Kapur’s entropy criterion.The quality of segmentation is measured by variousimage quality metrices which indicates Whale Optimization as the best performing algorithm.