An ameliorated teaching–learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur’s entropy and Otsu’s between class variance

•An ameliorated teaching–learning-based optimization algorithm is presented for multi-threshold image segmentation problem.•Two random numbers are utilized to determine the learning approaches of a learner in both teacher and learner phases of DI-TLBO, which further improves its global optimization...

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Vydáno v:	Information sciences Ročník 533; s. 72 - 107
Hlavní autoři:	Wu, Bo, Zhou, Jianxin, Ji, Xiaoyuan, Yin, Yajun, Shen, Xu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.09.2020
Témata:	Image segmentation Kapur’s entropy Multilevel thresholding Otsu’s threshold Teaching-learning-based optimization Kapur’s entropy Otsu’s threshold Multilevel thresholding Image segmentation Teaching-learning-based optimization
ISSN:	0020-0255, 1872-6291
On-line přístup:	Získat plný text
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Popis
Shrnutí:	•An ameliorated teaching–learning-based optimization algorithm is presented for multi-threshold image segmentation problem.•Two random numbers are utilized to determine the learning approaches of a learner in both teacher and learner phases of DI-TLBO, which further improves its global optimization ability.•Two new phases, self-feedback learning phase as well as mutation and crossover phase, are introduced in DI-TLBO algorithm.•DI-TLBO-based method possesses superior performance for multi-threshold image segmentation . In this paper, multi-threshold image segmentation approaches using an improved teaching–learning-based optimization algorithm (DI-TLBO) are presented and the proposed DI-TLBO-based methods obtain satisfactory segmentation results. This work is presented as follows. Firstly, two random numbers are introduced to determine the learning methods of the learner in the teacher phases and the learner phases of DI-TLBO. Randomness of the learning methods further improves global optimization ability of DI-TLBO. Self-feedback learning phase and mutation-crossover phase are also introduced into DI-TLBO algorithm, which makes DI-TLBO achieve better exploration ability. The comparative results of DI-TLBO with other evolutionary algorithms (EAs) on a set of benchmarks functions demonstrate that DI-TLBO acquires better solution accuracy than other EAs. Then the proposed DI-TLBO algorithm is applied to solve multi-level threshold image segmentation problems modeled by Otsu’s between class variance function and Kapur’s entropy function. Experiments comparing DI-TLBO-based methods with other EAs based approaches on standard test images show that DI-TLBO-based methods possess superior performance in terms of both solution accuracy and stability of segmentation results. Finally, the proposed DI-TLBO-based methods are successfully applied in casting X-ray image segmentation for multi-level threshold. Although the defects in high resolution X-ray image (3072×2400) are easy to be ignored and omitted when being detected artificially, all the defects are segmented perfectly using the proposed DI-TLBO-based methods.
ISSN:	0020-0255 1872-6291
DOI:	10.1016/j.ins.2020.05.033