Abstract
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.
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Lu, Y., Wang, J., Kong, J., Zhang, B., Zhang, J. (2006). An Integrated Algorithm for MRI Brain Images Segmentation. In: Beichel, R.R., Sonka, M. (eds) Computer Vision Approaches to Medical Image Analysis. CVAMIA 2006. Lecture Notes in Computer Science, vol 4241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11889762_12
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DOI: https://doi.org/10.1007/11889762_12
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