An Integrated Algorithm for MRI Brain Images Segmentation
This paper presents an integrated algorithm for MRI (Magnetic Resonance Imaging) brain tissues segmentation. The method is composed of four stages. Noise in the MRI images is first reduced by a versatile wavelet-based filter. Then, the watershed algorithm is applied to brain tissues as an initial segmenting method. Because the result of classical watershed algorithm on grey-scale textured images such as tissue images is over-segmentation. The third stage is a merging process for the over-segmentation regions using fuzzy clustering algorithm (Fuzzy C-Means). But there are still some regions which are not divided completely due to the low contrast in them, particularly in the transitional regions of gray matter and white matter, or cerebrospinal fluid and gray matter. We exploited a method base on Minimum Covariance Determinant (MCD) estimator to detect the regions needed segmentation again, and then partition them by a supervised k-Nearest Neighbor (kNN) classifier. This integrated approach yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.
KeywordsGray Matter Integrate Algorithm Transitional Region Fuzzy Cluster Algorithm Watershed Algorithm
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- 4.Lorenz, C., Krahnstoever, N.: 3D statistical shape models for medical image segmentation [J]. In: Proceedings of the Second International Conference on 3-D Digital Imaging and Modeling (3DIM) 1999, pp. 394–404 (1999)Google Scholar
- 8.Clarke, L., Velthuizen, R., Camacho, M., Heine, J., Vaidyanathan, M., Hall, L., Thatcher, R., Silbiger, M.: MRI segmentation: methods and application. Magn. Reson. Imaging 13(3), 343–368 (1995)Google Scholar
- 9.Liew, A. W.-C., Yan, H.: An Adaptive Spatial Fuzzy Clustering Algorithm for 3-D MR Image Segmentation, IEEE Transaction on Medical Imaging, vol 22, No 9 (2003). Google Scholar
- 13.Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transaction on Pattern Analysis And Machine Intelligence 13(6) (1991)Google Scholar
- 18.Cocosco, C.A., Zijdenbos, A.P., Evans, A.C.: A fully automatic and robust brain MRI tissue classification method. IEEE Transaction on Medical Image Analysis 7, 513–527 (2003)Google Scholar
- 21.Zijdenbos, A., Dawant, B.: Brain segmentation and white matter lesion detection in MR images. Crit. Rev. Biomed. Eng. 22(5–6), 401–465 (1994)Google Scholar
- 22.Mount, D., Arya, S.: ANN: Library for approximate nearest neighbor searching (1998), http://www.cs.umd.edu/_mount/ANN/