Various types of features, e.g., geometric features, texture features, projection features etc., have been introduced for polyp detection and differentiation tasks via computer aided detection and diagnosis (CAD) for computed tomography colonography (CTC). Although these features together cover more information of the data, some of them are statistically highly-related to others, which made the feature set redundant and burdened the computation task of CAD. In this paper, we proposed a new dimension reduction method which combines hierarchical clustering and principal component analysis (PCA) for false positives (FPs) reduction task. First, we group all the features based on their similarity using hierarchical clustering, and then PCA is employed within each group. Different numbers of principal components are selected from each group to form the final feature set. Support vector machine is used to perform the classification. The results show that when three principal components were chosen from each group we can achieve an area under the curve of receiver operating characteristics of 0.905, which is as high as the original dataset. Meanwhile, the computation time is reduced by 70% and the feature set size is reduce by 77%. It can be concluded that the proposed method captures the most important information of the feature set and the classification accuracy is not affected after the dimension reduction. The result is promising and further investigation, such as automatically threshold setting, are worthwhile and are under progress.

Date Published: 28 February 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86702A (28 February 2013); doi: 10.1117/12.2006965

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)