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    Cascaded techniques for improving emphysema classification in computed tomography images

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    12659191_Y15_AIR.pdf (643.4Kb)
    Author
    Ibrahim, M.A.
    Mukundan, R.
    Date
    2015
    Permanent Link
    http://hdl.handle.net/10092/11996

    The previous studies demonstrated the effectiveness of the multi-fractal based method for the classification of histo-pathological cases by calculating the local singularity coefficients of an image using different intensity measures. This paper proposed to improve the previous results by investigating the features derived from the combination of the alpha-histograms and the multifractal descriptors in the classification of Emphysema in computed tomography (CT) images. The performances of the classifiers are measured by using the classification accuracy (error matrix) and the area under the receiver operating characteristic curve (AUC). And further, the experimental results compared well with the local binary patterns (LBP) approach, a state-of-the-art measure for pulmonary Emphysema. The results also show that the proposed cascaded approach significantly improves the overall classification accuracy.

    Subjects
    Emphysema classification
     
    Multi-fractal analysis
     
    Histogram comparison
     
    Statistical self-similarity
     
    Field of Research::08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080106 - Image Processing
     
    Field of Research::11 - Medical and Health Sciences::1103 - Clinical Sciences::110320 - Radiology and Organ Imaging
     
    Field of Research::01 - Mathematical Sciences::0102 - Applied Mathematics::010202 - Biological Mathematics
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    • Engineering: Journal Articles [932]
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