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M. Masi Entropy- and Grey Wolf Optimizer-Based Multilevel Thresholding Approach for Image Segmentation

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Abstract

Image thresholding is one of most effective segmentation approaches, and multilevel thresholding approach is widely applied for segmentation. Multilevel thresholding segments the input image into different regions through multiple thresholds. In multilevel thresholding, the main goal is to search optimal threshold values for segmenting the given image into appropriate regions. In this paper, M. Masi entropy-based objective function is designed to find optimal threshold values which can be used to divide the input image into multiple regions. But the search of optimal threshold values can be categorized as NP-hard combinatorial optimization problem because the time complexity of the searching procedure increases exponentially as levels of thresholding increase. To solve this problem, grey wolf optimizer (GWO) is applied on M. Masi entropy-based objective function for searching optimal threshold values. GWO belongs to evolutionary computing techniques category that is inspired from social leadership hierarchy and intelligent hunting mechanism of grey wolves. So in this paper, a new M. Masi entropy-based multilevel thresholding approach using grey wolf optimizer (GWO) has been proposed. This approach is called GWO-based MMET approach. In the proposed approach, the optimum threshold values at different levels are searched by the minimization of objective function based on M. Masi entropy using GWO. The proposed approach is evaluated on benchmark images of standard databases, and experimental results are compared with PSO-based M. Masi entropy (PSO-based MMET), PSO-based cross-entropy (PSO-based CET) and DA-based Kapur entropy (DA-based KET) algorithms using segmented image quality evaluation parameters like peak signal-to-noise ratio (PSNR), uniformity, structure similarity (SSIM) index, mean structure similarity (MSSIM) index. Results show that the performance of proposed approach is quite promising.

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Authors and Affiliations

  1. BAM Khalsa College, Garhshankar, Hoshiarpur, Punjab, 144527, India

    Baljit Singh Khehra

  2. Department of Mathematics, Punjabi University, Patiala, Punjab, 147002, India

    Arjan Singh & Lovepreet Kaur

  3. Department of Computer Science and Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, 140407, India

    Lovepreet Kaur

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  1. Baljit Singh Khehra
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Khehra, B.S., Singh, A. & Kaur, L. M. Masi Entropy- and Grey Wolf Optimizer-Based Multilevel Thresholding Approach for Image Segmentation. J. Inst. Eng. India Ser. B 103, 1619–1642 (2022). https://doi.org/10.1007/s40031-022-00740-8

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