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A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity.

Rastgarpour M, Shanbehzadeh J - Comput Math Methods Med (2014)

Bottom Line: It has considerable effects on segmentation accuracy.The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy.The results confirm its effectiveness for medical image segmentation.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, P.O. Box 14515/775, Tehran 1477893855, Iran.

ABSTRACT
Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

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Level set segmentation of the nucleus fluorescence micrograph. (a), (c): manual initialization; (b), (d): final segmentation after 126 and 200 iterations, respectively, with λ = 0.003,  μ = 1,  and  ν = 1; (e): initialization by the GKFCM; and (f): final segmentation after 70 iterations.
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fig6: Level set segmentation of the nucleus fluorescence micrograph. (a), (c): manual initialization; (b), (d): final segmentation after 126 and 200 iterations, respectively, with λ = 0.003,  μ = 1,  and  ν = 1; (e): initialization by the GKFCM; and (f): final segmentation after 70 iterations.

Mentions: Figure 6 illustrates result of the LSEBFE model on the nucleus fluorescence micrograph. In this case, segmentation is difficult due to the weak and irregular boundaries and inhomogeneous foreground and background. Ideal initializing is challenging again. Figure 6 proves that a GKFCM clustering has the best performance for level set initialization.


A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity.

Rastgarpour M, Shanbehzadeh J - Comput Math Methods Med (2014)

Level set segmentation of the nucleus fluorescence micrograph. (a), (c): manual initialization; (b), (d): final segmentation after 126 and 200 iterations, respectively, with λ = 0.003,  μ = 1,  and  ν = 1; (e): initialization by the GKFCM; and (f): final segmentation after 70 iterations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3926326&req=5

fig6: Level set segmentation of the nucleus fluorescence micrograph. (a), (c): manual initialization; (b), (d): final segmentation after 126 and 200 iterations, respectively, with λ = 0.003,  μ = 1,  and  ν = 1; (e): initialization by the GKFCM; and (f): final segmentation after 70 iterations.
Mentions: Figure 6 illustrates result of the LSEBFE model on the nucleus fluorescence micrograph. In this case, segmentation is difficult due to the weak and irregular boundaries and inhomogeneous foreground and background. Ideal initializing is challenging again. Figure 6 proves that a GKFCM clustering has the best performance for level set initialization.

Bottom Line: It has considerable effects on segmentation accuracy.The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy.The results confirm its effectiveness for medical image segmentation.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, P.O. Box 14515/775, Tehran 1477893855, Iran.

ABSTRACT
Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

Show MeSH