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Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering.

Elazab A, Wang C, Jia F, Wu J, Li G, Hu Q - Comput Math Methods Med (2015)

Bottom Line: The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions.The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs.Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

View Article: PubMed Central - PubMed

Affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China; Faculty of Computers and Information, Mansoura University, Elgomhouria Street, Mansoura 35516, Egypt.

ABSTRACT
An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

No MeSH data available.


Related in: MedlinePlus

Segmentation results on a T1-weighted axial slice (number 100) from SBD with 7% noise and 20% grayscale nonuniformity. (a) Original image. (b) Ground truth. (c) GKFCM1 results. (d) GKFCM2 results. (e) FLICM results. (f) KWFLICM results. (g) MICO results. (h) RSCFCM results. (i) ARKFCM1 results. (j) ARKFCM2 results. (k) ARKFCMw results.
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fig2: Segmentation results on a T1-weighted axial slice (number 100) from SBD with 7% noise and 20% grayscale nonuniformity. (a) Original image. (b) Ground truth. (c) GKFCM1 results. (d) GKFCM2 results. (e) FLICM results. (f) KWFLICM results. (g) MICO results. (h) RSCFCM results. (i) ARKFCM1 results. (j) ARKFCM2 results. (k) ARKFCMw results.

Mentions: The first experiment is to segment a T1-weighted axial slice (number 100) with 217 × 181 pixels corrupted with 7% noise and 20% grayscale nonuniformity into WM, GM, and CSF. Figure 2 shows the segmentation results while Table 1 summarizes the JS and average running times.


Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering.

Elazab A, Wang C, Jia F, Wu J, Li G, Hu Q - Comput Math Methods Med (2015)

Segmentation results on a T1-weighted axial slice (number 100) from SBD with 7% noise and 20% grayscale nonuniformity. (a) Original image. (b) Ground truth. (c) GKFCM1 results. (d) GKFCM2 results. (e) FLICM results. (f) KWFLICM results. (g) MICO results. (h) RSCFCM results. (i) ARKFCM1 results. (j) ARKFCM2 results. (k) ARKFCMw results.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4697674&req=5

fig2: Segmentation results on a T1-weighted axial slice (number 100) from SBD with 7% noise and 20% grayscale nonuniformity. (a) Original image. (b) Ground truth. (c) GKFCM1 results. (d) GKFCM2 results. (e) FLICM results. (f) KWFLICM results. (g) MICO results. (h) RSCFCM results. (i) ARKFCM1 results. (j) ARKFCM2 results. (k) ARKFCMw results.
Mentions: The first experiment is to segment a T1-weighted axial slice (number 100) with 217 × 181 pixels corrupted with 7% noise and 20% grayscale nonuniformity into WM, GM, and CSF. Figure 2 shows the segmentation results while Table 1 summarizes the JS and average running times.

Bottom Line: The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions.The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs.Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

View Article: PubMed Central - PubMed

Affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China; Faculty of Computers and Information, Mansoura University, Elgomhouria Street, Mansoura 35516, Egypt.

ABSTRACT
An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

No MeSH data available.


Related in: MedlinePlus