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A thresholding method based on interval-valued intuitionistic fuzzy sets: an application to image segmentation

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Abstract

This paper proposes a new fuzzy approach for the segmentation of images. L-interval-valued intuitionistic fuzzy sets (IVIFSs) are constructed from two L-fuzzy sets that corresponds to the foreground (object) and the background of an image. Here, L denotes the number of gray levels in the image. The length of the membership interval of IVIFS quantifies the influence of the ignorance in the construction of the membership function. Threshold for an image is chosen by finding an IVIFS with least entropy. Contributions also include a comparative study with ten other image segmentation techniques. The results obtained by each method have been systematically evaluated using well-known measures for judging the segmentation quality. The proposed method has globally shown better results in all these segmentation quality measures. Experiments also show that the results acquired from the proposed method are highly correlated to the ground truth images.

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Acknowledgements

This work was supported by UGC-BSR-Research fellowship in Mathematical Sciences—2013–2014. This work is also supported by the Engineering Faculty of the University of Malaya under Grant No. UM.C/625/1/HIR/MOHE/ENG/42. The authors wish to thank all reviewers and associate editor for their fruitful comments and suggestions for significant improvement of the manuscript.

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Authors and Affiliations

  1. Department of Mathematics, Gandhigram Rural Institute - Deemed University, Gandhigram, Tamil Nadu, 624302, India

    V. P. Ananthi & P. Balasubramaniam

  2. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    P. Raveendran

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  1. V. P. Ananthi
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  2. P. Balasubramaniam
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  3. P. Raveendran
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Correspondence to P. Balasubramaniam.

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Ananthi, V.P., Balasubramaniam, P. & Raveendran, P. A thresholding method based on interval-valued intuitionistic fuzzy sets: an application to image segmentation. Pattern Anal Applic 21, 1039–1051 (2018). https://doi.org/10.1007/s10044-017-0622-y

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  • DOI: https://doi.org/10.1007/s10044-017-0622-y

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