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A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation

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Abstract

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.

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Acknowledgements

The authors are grateful to Dr. Sandeep Singh Pawar (Advance Diagnostic Centre, Ludhiana, Punjab) for providing the clinical data and the interpretations of the present work.

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This research received no specific grant from any funding agency.

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Authors and Affiliations

  1. Department of Electronics & Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India

    Taranjit Kaur & Barjinder Singh Saini

  2. Department of Computer Science and Engineering, UIET, Sector 25, Panjab University, Chandigarh, Chandigarh, India

    Savita Gupta

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  1. Taranjit Kaur
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  2. Barjinder Singh Saini
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  3. Savita Gupta
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Correspondence to Taranjit Kaur.

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This research has been approved by the Research Advisory Committee of the Institute. Also, all of the procedures performed during the image acquisition process comply with the ethical standards of the diagnostic centre from which the image data have been taken.

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Kaur, T., Saini, B.S. & Gupta, S. A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation. Australas Phys Eng Sci Med 41, 41–58 (2018). https://doi.org/10.1007/s13246-017-0609-4

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