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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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Some examples of images with intensity inhomogeneity; the columns from left to right: original images, inhomogeneity field, and corrected image; from top to bottom borrowed from [15–17], respectively.
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fig2: Some examples of images with intensity inhomogeneity; the columns from left to right: original images, inhomogeneity field, and corrected image; from top to bottom borrowed from [15–17], respectively.

Mentions: Intensity inhomogeneity often arises in real images like medical images caused by spatial variations in illumination, imperfections of imaging devices, and so forth. Figure 2 illustrates some examples of images with intensity inhomogeneity. It complicates image segmentation which is often regionbased and usually relies on the homogeneity of the image intensities in the ROI. Figure 3 shows two samples of this failure. The reason of complexity is the overlaps between the ranges of the intensities in the regions to be segmented. This makes it impossible to identify these regions based on the pixel intensity. Vovk et al. [14] have reviewed the methods of intensity inhomogeneity correction in MRIs.


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)

Some examples of images with intensity inhomogeneity; the columns from left to right: original images, inhomogeneity field, and corrected image; from top to bottom borrowed from [15–17], respectively.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: Some examples of images with intensity inhomogeneity; the columns from left to right: original images, inhomogeneity field, and corrected image; from top to bottom borrowed from [15–17], respectively.
Mentions: Intensity inhomogeneity often arises in real images like medical images caused by spatial variations in illumination, imperfections of imaging devices, and so forth. Figure 2 illustrates some examples of images with intensity inhomogeneity. It complicates image segmentation which is often regionbased and usually relies on the homogeneity of the image intensities in the ROI. Figure 3 shows two samples of this failure. The reason of complexity is the overlaps between the ranges of the intensities in the regions to be segmented. This makes it impossible to identify these regions based on the pixel intensity. Vovk et al. [14] have reviewed the methods of intensity inhomogeneity correction in MRIs.

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