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Unsupervised MS Lesion Detection And Classification Method on Brain MRI Images
|關鍵字:||自動目標物產生過程;Automatic target generation process;純度像素索引;限制能量最小化法;正交子空間投影法;pure pixel index;constrained energy minimization;orthogonal subspace projection||出版社:||通訊工程研究所||引用:|| T. B, Dyrby, E. Rostrup, W. Baare, et. al. Segmentation of age-related white matter changes in a clinical multi-center study. NeuroImage, 41: 335–345, 2008.  http://www.wisegeek.org/what-are-white-matter-lesions.htm  L. T. Westlye, K.B.Walhovd, A.M. Dale, et. al. Increased sensitivity to effects of normal aging and Alzheimer''s disease on cortical thickness by adjustment for local variability in gray/white contrast: A multi-sample MRI study. NeuroImage, 47: 1545–1557, 2009.  G. A. Wright, “Magnetic resonance image,” IEEE Signal Processing Mag., pp. 56–66, Jan. 1997.  G. Sebastiani and P. Barone, “Mathematical principles of basic magnetic resonance image in medicine,” Signal Process, vol. 25, pp. 227–250, 1991.  D. S. David, and G. B. William, Magnetic Resonance Imaging, vol. 1, 3rd, Mosby Inc.,1999.  C. I. 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Chang (1994) Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection. IEEE Transactions on Geoscience and Remote Sensing, 32 (1994), 779–785.  L. Clarke, R. Vethuizen, M. Camacho, J. Heine, M.Vaidyanathan, L. O. Hall, R. W. Thatcher and M. L. Silbiger, “MRI segmentation: methods and applications,” Magnetic Resonance Imaging, 13(3): 343-368, 1995.||摘要:||
The classification generally requires a set of training samples, which can be carried out in a supervised or an unsupervised manner depending upon how training samples are produced a priori using prior knowledge or a posteriori obtained directly from the data. Unsupervised methods which do not assume any prior scene knowledge can be learned to help classification process are obviously more challenging than the supervised ones. In order for segmentation to perform classification, a set of training samples is required. In this thesis we present two unsupervised target detection methods, automatic target generation process (ATGP) and pure pixel index (PPI), to find training samples. Then we apply constrained energy minimization (CEM) method to classify multiple sclerosis (MS) lesion for MR brain image. This CEM method is perfectly used due to the fact that it requires the least amount of information about subsample target of interest without making assumptions on signal model and noise/background statistics. After that the orthogonal subspace projection (OSP) method is also applied to classify the rest of tissues. Experimental results show that these approaches have great promise in MR brain image classification.
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