@article{417bff9c607c463ab079e852787d479c,
title = "A multiscale and multiblock fuzzy C-means classification method for brain MR images",
abstract = "Purpose: Classification of magnetic resonance (MR) images has many clinical and research applications. Because of multiple factors such as noise, intensity inhomogeneity, and partial volume effects, MR image classification can be challenging. Noise in MRI can cause the classified regions to become disconnected. Partial volume effects make the assignment of a single class to one region difficult. Because of intensity inhomogeneity, the intensity of the same tissue can vary with respect to the location of the tissue within the same image. The conventional hard classification method restricts each pixel exclusively to one class and often results in crisp results. Fuzzy C-mean (FCM) classification or soft segmentation has been extensively applied to MR images, in which pixels are partially classified into multiple classes using varying memberships to the classes. Standard FCM, however, is sensitive to noise and cannot effectively compensate for intensity inhomogeneities. This paper presents a method to obtain accurate MR brain classification using a modified multiscale and multiblock FCM. Methods: An automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with MR intensity correction is presented in this paper. We use a bilateral filter to process MR images and to build a multiscale image series by increasing the standard deviation of spatial function and by reducing the standard deviation of range function. At each scale, we separate the image into multiple blocks and for every block a multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels in order to overcome the effect of intensity inhomogeneity. The result from a coarse scale supervises the classification in the next fine scale. The classification method is tested with noisy MR images with intensity inhomogeneity. Results: Our method was compared with the conventional FCM, a modified FCM (MFCM) and multiscale FCM (MsFCM) method. Validation studies were performed on synthesized images with various contrasts, on the simulated brain MR database, and on real MR images. Our MsbFCM method consistently performed better than the conventional FCM, MFCM, and MsFCM methods. The MsbFCM method achieved an overlap ratio of 91 or higher. Experimental results using real MR images demonstrate the effectiveness of the proposed method. Our MsbFCM classification method is accurate and robust for various MR images. Conclusions: As our classification method did not assume a Gaussian distribution of tissue intensity, it could be used on other image data for tissue classification and quantification. The automatic classification method can provide a useful quantification tool in neuroimaging and other applications.",
keywords = "bilateral filter, fuzzy C-means (FCM), image classification, magnetic resonance images (MRI), multiblock, multiscale",
author = "Xiaofeng Yang and Baowei Fei",
note = "Funding Information: This research is supported in part by NIH grant R01CA156775 (PI: Fei), Coulter Translational Research Grant (PIs: Fei and Hu), Georgia Cancer Coalition Distinguished Clinicians and Scientists Award (PI: Fei), Emory Molecular and Translational Imaging Center (NIH P50CA128301), SPORE in Head and Neck Cancer (NIH P50CA128613), and Atlanta Clinical and Translational Science Institute (ACTSI) that is supported by the PHS Grant UL1 RR025008 from the Clinical and Translational Science Award program. TABLE I. Dice overlap ratios between the classification results and the ground truth for the brain data at different noise levels when intensity inhomogeneity is 20%. Noise 0% 3% 5% 7% 9% CSF 0.97±0.00 0.96±0.01 0.94±0.01 0.92±0.01 0.90±0.01 GM 0.97±0.00 0.96±0.00 0.94±0.00 0.93±0.00 0.91±0.01 WM 0.98±0.01 0.97±0.01 0.96±0.01 0.95±0.01 0.94±0.01 TABLE II. Dice overlap ratios between the classification results and the ground truth for the brain data at different noise levels when intensity inhomogeneity is 40%. Noise 0% 3% 5% 7% 9% CSF 0.95±0.01 0.94±0.01 0.93±0.01 0.92±0.01 0.91±0.01 GM 0.96±0.00 0.95±0.00 0.94±0.00 0.93±0.00 0.91±0.01 WM 0.97±0.01 0.97±0.01 0.96±0.01 0.95±0.01 0.94±0.02 TABLE III. The Dice overlap ratios between the classification results and the ground truth for the brain data at different noise levels when a 68% intensity inhomogeneity field is applied. Noise 0% 3% 5% 7% 9% CSF 0.92±0.01 0.90±0.01 0.86±0.01 0.83±0.01 0.80±0.02 GM 0.92±0.01 0.90±0.01 0.86±0.01 0.82±0.01 0.78±0.02 WM 0.95±0.02 0.95±0.02 0.93±0.03 0.92±0.03 0.91±0.03 FIG. 1. Schematic flow chart of the proposed multiscale and multiblock FCM method. FIG. 2. Multicale with multiblocks. The scale space is composed of a stack of the images filtered at different scales where l = 0 is the original image. Image at every scale is divided into blocks. FIG. 3. Bilateral and AD filter processing for a synthesized noisy image. ( a ) Image a 1 is the original noisy image. a 2 is the original image labeled with three classes (Class 1, 2, and 3). a 3 and a 5 are AD filtered results at scale 3 and scale 5, and a 4 and a 6 are bilateral filtered results at scale 3 and scale 5. ( b ) The signal profile along the center line of the noisy image and the original image without noise. ( c ) and ( d ) Signal profiles at the scales 3 ( c ) and 5 ( d ) of the original noisy image, with AD filtering, and with bilateral filtering, respectively. FIG. 4. Comparison of the classification results using the four methods for synthesized images. The first column contains the original images with the designed contrast and bias level. The 2nd, 3rd, 4th, and 5th columns are the classification results using the FCM, MFCM, MsFCM and MsbFCM methods, respectively. FIG. 5. Quantitative evaluation of the classification results in synthesized images without bias field. Dice overlap ratios (Left) and error overlap ratios (Right) for Classes 1, 2, and 3 are shown from top to bottom, respectively. The Y axis is the overlap ratio between the ground truth and the classification results. The X -axis represents the contrast levels. FIG. 6. Quantitative evaluation of the classification results in synthesized images with 35% bias field. Dice overlap ratios (Left) and error overlap ratios (Right) for Classes 1, 2, and 3 are shown from top to bottom, respectively. The Y axis is the overlap ratio between the ground truth and the classification results. The X -axis represents the contrast levels. FIG. 7. Classification results of brain MR images with different intensity inhomogeneity. The original MR image with 9% noise and 20% intensity inhomogeneity (a 1 ) and with 9% noise and 40% intensity inhomogeneity (b 1 ) are smoothed after the bilateral filter processing (a 2 and b 2 ). a 3 and b 3 are the ground truth of the classification. a 4 and b 4 , a 5 and b 5 , a 6 and b 6 , and a 7 and b 7 are the classification results using the MsbFCM, FCM, MFCM, and MsFCM methods, respectively. FIG. 8. Dice overlap ratios (Left) and error overlap ratios (Right) of the four methods, i.e., FCM, MFCM, MsFCM, and MsbFCM. The images were obtained from the McGill brain database with different noise and 20% intensity inhomogeneity. The results for CSF, GM, and WM are shown from top to bottom, respectively. The Y axis is the overlap ratio between the ground truth and the classification results. The X -axis represents noise levels. FIG. 9. Dice overlap ratios of three tissue types when different numbers of subblocks are used for the classification. FIG. 10. Classification of real brain MR images. a 1 and b 1 are the original MR images. a 2 and b 2 are the images after bilateral filtering. a 3 and b 3 are the classified results of the MsbFCM method. a 4 and b 4 are the manual segmentation results. a 5 and b 5 , a 6 and b 6 , and a 7 and b 7 are the classification results using the FCM, MFCM, and MsFCM methods, respectively. ",
year = "2011",
month = jun,
doi = "10.1118/1.3584199",
language = "English (US)",
volume = "38",
pages = "2879--2891",
journal = "Medical physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "6",
}