Cascaded techniques for improving emphysema classification in computed tomography images

Type of content
Journal Article
Thesis discipline
Degree name
Publisher
University of Canterbury. Computer Science and Software Engineering
Journal Title
Journal ISSN
Volume Title
Language
Date
2015
Authors
Ibrahim, M.A.
Mukundan, R.
Abstract

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.

Description
Citation
Ibrahim, M.A., Mukundan, R. (2015) Cascaded techniques for improving emphysema classification in computed tomography images. Artificial Intelligence Research, 4(2), pp. 112-118.
Keywords
Emphysema classification, Multi-fractal analysis, Histogram comparison, Statistical self-similarity
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
Fields of Research::46 - Information and computing sciences::4603 - Computer vision and multimedia computation::460306 - Image processing
Field of Research::11 - Medical and Health Sciences::1103 - Clinical Sciences::110320 - Radiology and Organ Imaging
Fields of Research::49 - Mathematical sciences::4901 - Applied mathematics::490102 - Biological mathematics
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