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A generalized Masi entropy based efficient multilevel thresholding method for color image segmentation

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Abstract

Multilevel thresholding for image segmentation is a crucial process in several applications such as feature extraction and pattern recognition. In this paper, a novel Masi entropy-based criterion for color satellite image multilevel thresholding is proposed. The proposed algorithm is based on Masi entropy which can deal with the additive/non-extensive information through the aid of a concordant entropic parameter ‘r’ which is extended in favor of multilevel based color satellite image segmentation. In addition, a comparative study between proposed Masi entropy-based color image multilevel thresholding and well known state-of-the-art entropies such as Kapur’s, Renyi’s and Tsallis entropy is presented. The simulation results of the proposed Masi entropy-based algorithm illustrate better performance for normal and color satellite image segmentation. Trials are conducted on various color test images to concrete the efficiency of the proposed algorithm. For segmentation purpose numerous fidelity parameters are computed such as structural similarity index (SSIM), feature similarity index (FSIM), misclassification error (ME), mean square error (MSE) and peak signal to noise ratio (PSNR).

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Acknowledgments

The authors wish to thank all reviewers and associate editor for their fruitful comments and suggestions for significant improvement of the manuscript. We thank Mr. Mohit Kumar, Assistant Professor (Muzaffarpur Institute of Technology, Muzaffarpur, Bihar) for editing the English text of a draft of this manuscript.

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  1. Department of Electronics & Communication Engineering, National Institute of Technology Patna, Patna, 800005, India

    Swapnil Shubham & Ashish Kumar Bhandari

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  1. Swapnil Shubham
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  2. Ashish Kumar Bhandari
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Correspondence to Ashish Kumar Bhandari.

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Shubham, S., Bhandari, A.K. A generalized Masi entropy based efficient multilevel thresholding method for color image segmentation. Multimed Tools Appl 78, 17197–17238 (2019). https://doi.org/10.1007/s11042-018-7034-x

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  • DOI: https://doi.org/10.1007/s11042-018-7034-x

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