Cascaded techniques for improving emphysema classification in computed tomography images

Musibau A. Ibrahim, Ramakrishnan Mukundan


The previous studies demonstrated the effectiveness of the multi-fractal based method for the classification of histo-pathologicalcases by calculating the local singularity coefficients of an image using different intensity measures. This paper proposed toimprove the previous results by investigating the features derived from the combination of the alpha-histograms and the multifractaldescriptors in the classification of Emphysema in computed tomography (CT) images. The performances of the classifiersare 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-artmeasure for pulmonary Emphysema. The results also show that the proposed cascaded approach significantly improves theoverall classification accuracy.

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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