Fig 1.
Analysis of the neighborhood similarities.
(a) Initial image; (b-d) are the intensities information of points marked with red plus, green plus and blue plus, respectively; (e) the inner-relationship in (b).
Fig 2.
Illustration of (1st column) three 3-Tesla brain MR images, (2nd column) segmentation results of the proposed method, (3rd column) bias corrected images, and (4th column) their estimated bias fields.
Fig 3.
Illustration of (1st column) three 3-Tesla brain MR images with skull, (2nd column) segmentation results of the proposed method, (3rd column) bias corrected images, and (4th column) their estimated bias fields.
Fig 4.
Segmentation results on the 92th transaxial image of a synthetic image data set.
The first column shows the initial images with parameters: noise level 1%, 3%, 5% and 7% (from top to the bottom), respectively. The images have the same intensity inhomogeneity level: 30%. The second column to the right column show the segmentation results of improved LBF method, improved LGD method, Zhang's method, MICO and our method, respectively.
Fig 5.
Details of the segmentation results on the 92th transaxial image of a synthetic image data set with parameters: noise level 5% and the intensity inhomogeneity level: 30%.
The second column to the right column show the segmentation results of improved LBF method, improved LGD method, Zhang's method, MICO and our method, respectively.
Fig 6.
Segmentation results on the 92th transaxial image of a synthetic image data set.
The first column shows the initial images with parameters: intensity inhomogeneity level 40%, 60%, 80% and 100% (from top to the bottom), respectively. The images have the same noise level: 3%. The second column to the right column show the segmentation results of improved LBF method, LGD method, Zhang's method, MICO and our method, respectively.
Fig 7.
Details of the segmentation results on the 92th transaxial image of a synthetic image data set with parameters: noise level 3% and the intensity inhomogeneity level: 80%.
The second column to the right column show the segmentation results of improved LBF method, LGD method, Zhang's method, MICO and our method, respectively.
Table 1.
Js values (mean ± standard deviation) for the segmentation results on simulated T1-weighted brain MR images. (%).
Fig 8.
Segmentation results on clinical brain MR images.
(a) is the 18th image of 1_24♯, (b)-(g) show the ground truth, the segmentation results of improved LBF method, improved LGD method, Zhang's method, MICO and our method, respectively. (g) is the 30th image of 2_4♯. (i)-(n) are the ground truth, the segmentation results of improved LBF method, improved LGD method, Zhang's method, MICO and our method, respectively.
Table 2.
Js values (mean ± standard deviation) for the segmentation results on simulated T1-weighted brain MR images. (%).
Fig 9.
Illustration of two 3-T intensity inhomogeneity corrupted brain MR images and one 7-T brain MR images (1st column).
The second column to right column show the results of improved LBF, improved LGD, Zhang's method, MICO and our method, respectively. The odd rows show the bias field corrected images. The even rows show the corresponding estimated bias fields.
Table 3.
CV values for the bias corrected images. (%).
Table 4.
CPU time (in second) for the segmentation.
Fig 10.
3D segmentation results of WM, GM and CSF on T1-weighted 1mm brain MR images from BrainWeb with parameters: noise level 3% and intensity inhomogeneity level: 80%.
(b)-(d) show the results of the 3rd iteration, 5th iteration and 7th iteration, respectively.
Fig 11.
3D segmentation results of WM, GM on T1-weighted clinical brain MR image.
Left column shows the ground truth and the right column shows the results of our method.