Image Segmentation Methods for Non-destructive testing Applications

TL;DR

This paper introduces new image segmentation methods for non-destructive testing using hidden Markov random fields and cuckoo search variants, optimizing segmentation quality and efficiency.

Contribution

It proposes novel segmentation algorithms combining HMRFs with five cuckoo search variants and evaluates their performance on NDT images.

Findings

Cuckoo search variants effectively optimize segmentation.

Selected CS variant improves segmentation accuracy.

Method reduces misclassification error in NDT images.

Abstract

In this paper, we present new image segmentation methods based on hidden Markov random fields (HMRFs) and cuckoo search (CS) variants. HMRFs model the segmentation problem as a minimization of an energy function. CS algorithm is one of the recent powerful optimization techniques. Therefore, five variants of the CS algorithm are used to compute a solution. Through tests, we conduct a study to choose the CS variant with parameters that give good results (execution time and quality of segmentation). CS variants are evaluated and compared with non-destructive testing (NDT) images using a misclassification error (ME) criterion.