Optimal Multilevel Thresholding for Image Segmentation Based On Nature Inspired Algorithm

Abstract

Background: Multilevel Thresholding (MLT) is a prominent image segmentation research field that can effectively handle problems while collecting meaningful information from a digital image. In most applications, such as Remote sensing, medical diagnosis, and pattern recognition, the desired region extracted from the raw images is more used for further processing. Therefore, extracting the desired region from a test image is essential and a significant pre-processing step in most applications. One of the challenging and primary stages of medical image examination is identifying the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and various other factors. Any such condition related to the skin or brain sometimes advances to cancer and becomes life-threatening. So, an efficient automatic image segmentation approach is required at the initial stage of medical image analysis. An efficient thresholding method is essential for colour images with more information content capability to increase the processing speed by performing segmentation before any high-level processing. MLT is one of the convenient and efficient methods that partition an image with the help of the number of threshold values selected by analyzing the test image before segmentation. Modern-day MLT problems require the application of optimization techniques to generate the optimal threshold values by optimizing a specific objective function. newlinePurpose: Following a thorough analysis of the numerous MLT methodologies suggested by various researchers, it has been found that the optimization algorithm and the employed objective function significantly impact thresholding performance. Therefore, an appropriate optimization algorithm with a suitable objective function is always the requirement for reliable segmentation. Over the past few decades, several nature-inspired optimization algorithms have been developed and effectively used in various optimization problems, including MLT problems with