Modified Moth-Flame Optimization Algorithm-Based Multilevel Minimum Cross Entropy Thresholding for Image Segmentation

Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this a...

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Published inInternational journal of swarm intelligence research Vol. 11; no. 4; pp. 123 - 139
Main Authors Khairuzzaman, Abdul Kayom Md, Chaudhury, Saurabh
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.10.2020
Subjects
Algorithms
Entropy (Information theory)
Image quality
Image segmentation
Multilevel
Optimization
Optimization techniques
Thresholds
Online AccessGet full text
ISSN1947-9263
1947-9271
DOI10.4018/IJSIR.2020100106

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Abstract Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this article, a modification is proposed to the Moth-Flame Optimization (MFO) algorithm and then it is applied to multilevel thresholding for image segmentation. Cross entropy is used as the objective function to select the optimal thresholds. A set of benchmark test images are used to evaluate the proposed technique. The Mean Structural SIMilarity (MSSIM) index is used to measure the quality of the segmented images. The results of the proposed technique are compared with the original MFO, PSO, BFO, and WOA. Experimental results and analysis suggest that the proposed technique outperforms other techniques in terms of segmentation quality images and stability. Moreover, computation time required for multilevel thresholding is also reduced to a manageable level.
AbstractList Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this article, a modification is proposed to the Moth-Flame Optimization (MFO) algorithm and then it is applied to multilevel thresholding for image segmentation. Cross entropy is used as the objective function to select the optimal thresholds. A set of benchmark test images are used to evaluate the proposed technique. The Mean Structural SIMilarity (MSSIM) index is used to measure the quality of the segmented images. The results of the proposed technique are compared with the original MFO, PSO, BFO, and WOA. Experimental results and analysis suggest that the proposed technique outperforms other techniques in terms of segmentation quality images and stability. Moreover, computation time required for multilevel thresholding is also reduced to a manageable level.
Author Khairuzzaman, Abdul Kayom Md
Chaudhury, Saurabh
AuthorAffiliation National Institute of Technology, Silchar, India
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SubjectTerms Algorithms
Entropy (Information theory)
Image quality
Image segmentation
Multilevel
Optimization
Optimization techniques
Thresholds
Title Modified Moth-Flame Optimization Algorithm-Based Multilevel Minimum Cross Entropy Thresholding for Image Segmentation
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