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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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Segmentation results of various medical images by proposed method. The columns: (a) original image, (b) initial segmentation by GKFCM, (c) segmentation result, (d) bias field and (e) bias corrected image.
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fig7: Segmentation results of various medical images by proposed method. The columns: (a) original image, (b) initial segmentation by GKFCM, (c) segmentation result, (d) bias field and (e) bias corrected image.

Mentions: The second experiment evaluates the new kernel-based fuzzy level set in inhomogeneous medical images. Figure 7 illustrates the success of new method in various modalities of medical imaging including MR images of the brain and breast (first and last rows, resp.), CT images of blood vessels and heart (second and third rows, resp.). It implicitly shows that the contour of GKFCM is near to ROI but not optimal contour of ROI.


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)

Segmentation results of various medical images by proposed method. The columns: (a) original image, (b) initial segmentation by GKFCM, (c) segmentation result, (d) bias field and (e) bias corrected image.
© Copyright Policy
Related In: Results  -  Collection

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

fig7: Segmentation results of various medical images by proposed method. The columns: (a) original image, (b) initial segmentation by GKFCM, (c) segmentation result, (d) bias field and (e) bias corrected image.
Mentions: The second experiment evaluates the new kernel-based fuzzy level set in inhomogeneous medical images. Figure 7 illustrates the success of new method in various modalities of medical imaging including MR images of the brain and breast (first and last rows, resp.), CT images of blood vessels and heart (second and third rows, resp.). It implicitly shows that the contour of GKFCM is near to ROI but not optimal contour of ROI.

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