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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51916


    Title: Automated classification of multi-spectral MR images using Linear Discriminant Analysis
    Authors: Lin,GC;Wang,WJ;Wang,CM;Sun,SY
    Contributors: 電機工程學系
    Keywords: ARTIFICIAL NEURAL-NETWORKS;FUZZY C-MEANS;SEGMENTATION;RECOGNITION;ALGORITHM;MODEL
    Date: 2010
    Issue Date: 2012-03-28 10:10:21 (UTC+8)
    Publisher: 國立中央大學
    Abstract: Magnetic resonance imaging (MRI) is a valuable instrument in medical science owing to its capabilities in soft tissue characterization and 3D visualization. A potential application of MRI in clinical practice is brain parenchyma classification. This work proposes a novel approach called "Unsupervised Linear Discriminant Analysis (ULDA)" to classify and segment the three major tissues, i.e. gray matter (GM), white matter (WM) and cerebral spinal fluid (CSF), from a multi-spectral MR image of the human brain. The ULDA comprises two processes, namely Target Generation Process (TGP) and Linear Discriminant Analysis (LDA) classification. TGP is a fuzzy-set process that generates a set of potential targets from unknown information, and applies these targets to train the optimal division boundary by LDA, such that three tissues GM, WM and CSF are separated. Finally, two sets of images, namely computer-generated phantom images and real MR images are used in the experiments to evaluate the effectiveness of ULDA. Experiment results reveal that UDLA segments a multi-spectral MR image much more effectively than either FMRIB's Automated Segmentation Tool (FAST) or Fuzzy C-means (FC). (C) 2009 Elsevier Ltd. All rights reserved.
    Relation: COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
    Appears in Collections:[電機工程學系] 期刊論文

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