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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 the Brats2 image. (a) Original image. (b) GKFCM1 results. (c) GKFCM2 results. (d) FLICM results. (e) KWFLICM results. (f) MICO results. (g) ARKFCM1 results. (h) ARKFCM2 results. (i) ARKFCMw results.
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fig6: Segmentation results on the Brats2 image. (a) Original image. (b) GKFCM1 results. (c) GKFCM2 results. (d) FLICM results. (e) KWFLICM results. (f) MICO results. (g) ARKFCM1 results. (h) ARKFCM2 results. (i) ARKFCMw results.

Mentions: We experimented two T1-weighted axial slices (slices numbers 80 and 86, denoted, resp., as Brats1 and Brats2) with 240 × 240 pixels, respectively, from files pat266_1 and pat192_1 (available from MICCAI BRATS 2014 challenge, https://www.virtualskeleton.ch/BRATS/Start2014) (Figures 5 and 6). From black to white are, respectively, background, CSF, GM, and WM. It should be noted that clustering is carried out only for CSF, GM, and WM, with the pathology region being considered as background.


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 the Brats2 image. (a) Original image. (b) GKFCM1 results. (c) GKFCM2 results. (d) FLICM results. (e) KWFLICM results. (f) MICO results. (g) ARKFCM1 results. (h) ARKFCM2 results. (i) ARKFCMw results.
© Copyright Policy
Related In: Results  -  Collection

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

fig6: Segmentation results on the Brats2 image. (a) Original image. (b) GKFCM1 results. (c) GKFCM2 results. (d) FLICM results. (e) KWFLICM results. (f) MICO results. (g) ARKFCM1 results. (h) ARKFCM2 results. (i) ARKFCMw results.
Mentions: We experimented two T1-weighted axial slices (slices numbers 80 and 86, denoted, resp., as Brats1 and Brats2) with 240 × 240 pixels, respectively, from files pat266_1 and pat192_1 (available from MICCAI BRATS 2014 challenge, https://www.virtualskeleton.ch/BRATS/Start2014) (Figures 5 and 6). From black to white are, respectively, background, CSF, GM, and WM. It should be noted that clustering is carried out only for CSF, GM, and WM, with the pathology region being considered as background.

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