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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 variant medical images (a) origional image, (b) initialization by GKFCM, (c) the result of CV model [31], (d) the result of localizing region-based active contours model [33], (e) the result of FTC model [11], (f) final segmentation of proposed method.
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fig9: Level set segmentation of variant medical images (a) origional image, (b) initialization by GKFCM, (c) the result of CV model [31], (d) the result of localizing region-based active contours model [33], (e) the result of FTC model [11], (f) final segmentation of proposed method.

Mentions: Figure 9 and Table 2 reveal this comparison qualitatively and quantitatively. Figure 9 illustrates the success of new kernel-based fuzzy level set segmentation among the famous level-set-based algorithms. In this figure, there are the original image, initialization by GKFCM, final segmentation by CV model (GKFCM_CV) [31], localizing region-based active contours (GKFCM_Lankton) [33], FTC model (GKFCM_FTC) [11], and proposed method, from left to right, respectively. Table 2 deals with this comparison in terms of speed and accuracy, respectively. Table 2 shows that the proposed method is closer than other region-based level set algorithms by spending more time (similar to part 1 of third experiment).


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 variant medical images (a) origional image, (b) initialization by GKFCM, (c) the result of CV model [31], (d) the result of localizing region-based active contours model [33], (e) the result of FTC model [11], (f) final segmentation of proposed method.
© Copyright Policy
Related In: Results  -  Collection

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

fig9: Level set segmentation of variant medical images (a) origional image, (b) initialization by GKFCM, (c) the result of CV model [31], (d) the result of localizing region-based active contours model [33], (e) the result of FTC model [11], (f) final segmentation of proposed method.
Mentions: Figure 9 and Table 2 reveal this comparison qualitatively and quantitatively. Figure 9 illustrates the success of new kernel-based fuzzy level set segmentation among the famous level-set-based algorithms. In this figure, there are the original image, initialization by GKFCM, final segmentation by CV model (GKFCM_CV) [31], localizing region-based active contours (GKFCM_Lankton) [33], FTC model (GKFCM_FTC) [11], and proposed method, from left to right, respectively. Table 2 deals with this comparison in terms of speed and accuracy, respectively. Table 2 shows that the proposed method is closer than other region-based level set algorithms by spending more time (similar to part 1 of third experiment).

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