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Fuzzy local intensity clustering (FLIC) model for automatic medical image segmentation

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Abstract

Intensity inhomogeneity is one of the main challenges in automatic medical image segmentation. In this paper, fuzzy local intensity clustering (FLIC), which is based on the combination of level set algorithm and fuzzy clustering, is proposed to mitigate the effect of intensity variation and noise contamination. For the FLIC method, the segmentation and bias modification are carried out in a fully automatic and simultaneous manner through the local clustering of intensity and selection of the initial contour by the fuzzy method. Besides, the local entropy is integrated into the FLIC function to improve the contour evolution. Experimental results on inhomogeneous medical images indicate the superiority of the FLIC model over the other state-of-the-art segmentation methods in terms of accuracy, robustness, and computational time.

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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

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  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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Correspondence to Mohammad Rahmanimanesh.

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Appendix A

Appendix A

See Figs. 18, 19, 20, 21, 22, 23, and 24.

Fig. 18
figure 18

Results of FLIC on medical images. First column: original image, second: FRFCM result, third: LIC result, fourth: FLIC result (final segmentation results in a red curve)

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Fig. 19
figure 19

Results of FLIC on noisy medical images. First column: original image, noisy image with Gaussian noise (0.03), second: FRFCM result as initial contour, third: FLIC result (final segmentation results in a red curve)

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Fig. 20
figure 20

Results of FLIC on noisy medical images. First column: original image, second: noisy image with Gaussian noise (0.10), third: FRFCM result as initial contour, fourth: FLIC result (final segmentation results in a red curve)

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Fig. 21
figure 21

Results of FLIC on noisy medical images. First column: original image, second: noisy image (salt and pepper 2%), third: FRFCM result as initial contour, fourth: FLIC result (final segmentation results in red curve)

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Fig. 22
figure 22

Results of FLIC on noisy medical images. First column: original image, second: noisy image (salt and pepper 10%), third: FRFCM result as initial contour, fourth: FLIC result (final segmentation results in a red curve)

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Fig. 23
figure 23

Results of FLIC on noisy medical images. First column: original image, second: noisy image (salt and pepper 20%), third: FRFCM result as initial contour, fourth: FLIC result (final segmentation results in a red curve)

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Fig. 24
figure 24

Segmentation of FLIC in color images. First column: original image, second: FLIC segmentation results

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Khosravanian, A., Rahmanimanesh, M., Keshavarzi, P. et al. Fuzzy local intensity clustering (FLIC) model for automatic medical image segmentation. Vis Comput 37, 1185–1206 (2021). https://doi.org/10.1007/s00371-020-01861-1

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