In this paper, a cascade classification scheme was proposed to improve computational efficiency in lung parenchyma quantification in HRCT images. Proposed cascade classification scheme includes four steps: cost-based class-specific feature selection, class-specific classifier training, classifier ordering, cascade feature extraction and classification. In the first step, feature sets were determined by sequential forward floating selection (SFFS) using performance improvement to extraction cost ratio criterion. Then classifiers were trained to classify specific class from all of other classes. Using accuracies of those classifiers, the order of classification was determined; from the highest accuracy to lowest accuracy. To quantify new images, feature extraction and classification were sequentially repeated. The impact of using the proposed cascade classification scheme is evaluated in terms of computational cost and classification accuracy. For automated classification, support vector machine (SVM) was implemented. To assess the performance and crossvalidation of the system, ten-folding method was used. In the experimental results, the computational cost was reduced by 46% and the overall accuracy was 92.04% which is not significantly different in a comparison of conventional method. This work shows that, in our classification problem, using the proposed cascade classification scheme can reduce the computational cost in the feature extraction while maintaining the classification accuracy.

Date Published: 27 February 2009
PDF: 9 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603A (27 February 2009);

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)