Multilevel Thresholding based Image Segmentation using Optimization Algorithm
Pratikshan Malakar, Debasmita Ghosh, Kaushik Shaw, Puja Pandey, Shyandeep Das, Supriya Dhabal · 2020
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 paper the performances of the Grasshopper Optimization Algorithm and the Whale Optimization Algorithm are evaluated for image segmentation.The performances of the proposed methods are compared with Cuckoo Search Algorithm along with Kapur's entropy criterion.The quality of segmentation is measured by various image quality metrices which indicates Whale Optimization as the best performing algorithm.