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Level set method for automated 3D brain tumor segmentation using symmetry analysis and kernel induced fuzzy clustering

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Abstract

Automatic brain tumor segmentation in magnetic resonance images (MRIs) is an essential stage for treatment planning. However, MR image segmentation is challenging owing to non-uniformity in the intensity distribution, tumor shape, size, and location variation. The paper proposes a new level set method that is called Fuzzy Kernel Level Set (FKLS) for 3D brain tumor segmentation in MR images. To avoid computational complexity, fast bounding box based on symmetry analysis is used to extract the volume of interest (VOI) in brain MRIs. Then, a level set method is proposed based on fuzzy c-means clustering and kernel mapping. A kernel function is used to transfer the image into another domain, where the new proposed functional is minimized. To assess the proposed FKLS method, a synthetic image and natural brain MR images from BraTS 2017 are segmented. Experimental results show that our method is superior to the state-of-the-art segmentation methods regarding the segmentation accuracy based on Dice, Jaccard, Sensitivity, and Specificity metrics. The mean values of these metrics are 97.62% \(\pm\)(0.94%), 95.41% \(\pm\)(1.8%), 98.79% \(\pm\) (0.63%), and 99.85% \(\pm\) (0.09%), respectively.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

    Asieh Khosravanian, Mohammad Rahmanimanesh, Parviz Keshavarzi & Saeed Mozaffari

  2. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Kamran Kazemi

Authors
  1. Asieh Khosravanian
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  2. Mohammad Rahmanimanesh
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  3. Parviz Keshavarzi
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  4. Saeed Mozaffari
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  5. Kamran Kazemi
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Correspondence to Asieh Khosravanian or Mohammad Rahmanimanesh.

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Khosravanian, A., Rahmanimanesh, M., Keshavarzi, P. et al. Level set method for automated 3D brain tumor segmentation using symmetry analysis and kernel induced fuzzy clustering. Multimed Tools Appl 81, 21719–21740 (2022). https://doi.org/10.1007/s11042-022-12445-7

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  • DOI: https://doi.org/10.1007/s11042-022-12445-7

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