Share Email Print
cover

Proceedings Paper

Feature selection for computer-aided polyp detection using MRMR
Author(s): Xiaoyun Yang; Boray Tek; Gareth Beddoe; Greg Slabaugh
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In building robust classifiers for computer-aided detection (CAD) of lesions, selection of relevant features is of fundamental importance. Typically one is interested in determining which, of a large number of potentially redundant or noisy features, are most discriminative for classification. Searching all possible subsets of features is impractical computationally. This paper proposes a feature selection scheme combining AdaBoost with the Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative features. A fitness function is designed to determine the optimal number of features in a forward wrapper search. Bagging is applied to reduce the variance of the classifier and make a reliable selection. Experiments demonstrate that by selecting just 11 percent of the total features, the classifier can achieve better prediction on independent test data compared to the 70 percent of the total features selected by AdaBoost.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241B (9 March 2010); doi: 10.1117/12.844165
Show Author Affiliations
Xiaoyun Yang, Medicsight PLC (United Kingdom)
Boray Tek, Medicsight PLC (United Kingdom)
Gareth Beddoe, Medicsight PLC (United Kingdom)
Greg Slabaugh, Medicsight PLC (United Kingdom)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

© SPIE. Terms of Use
Back to Top