Multilevel Threshold Based Gray Scale Image Segmentation using Cuckoo Search

TL;DR

This paper introduces a multilevel thresholding image segmentation method that employs Cuckoo Search to optimize threshold selection, improving segmentation quality as measured by MSE and PSNR.

Contribution

It proposes a novel combination of multilevel thresholding with Cuckoo Search for optimal segmentation in gray scale images.

Findings

Cuckoo Search effectively finds optimal thresholds for segmentation.

The method improves segmentation quality based on MSE and PSNR metrics.

The approach demonstrates better results compared to traditional methods.

Abstract

Image Segmentation is a technique of partitioning the original image into some distinct classes. Many possible solutions may be available for segmenting an image into a certain number of classes, each one having different quality of segmentation. In our proposed method, multilevel thresholding technique has been used for image segmentation. A new approach of Cuckoo Search (CS) is used for selection of optimal threshold value. In other words, the algorithm is used to achieve the best solution from the initial random threshold values or solutions and to evaluate the quality of a solution correlation function is used. Finally, MSE and PSNR are measured to understand the segmentation quality.