Cascaded techniques for improving emphysema classification in computed tomography images (2015)
Type of ContentJournal Article
PublisherUniversity of Canterbury. Computer Science and Software Engineering
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.
CitationIbrahim, M.A., Mukundan, R. (2015) Cascaded techniques for improving emphysema classification in computed tomography images. Artificial Intelligence Research, 4(2), pp. 112-118.
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KeywordsEmphysema classification; Multi-fractal analysis; Histogram comparison; Statistical self-similarity
ANZSRC Fields of Research46 - Information and computing sciences::4603 - Computer vision and multimedia computation::460306 - Image processing
11 - Medical and Health Sciences::1103 - Clinical Sciences::110320 - Radiology and Organ Imaging
49 - Mathematical sciences::4901 - Applied mathematics::490102 - Biological mathematics
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