Feature Selection for Computer-Aided Polyp Detection using MRMR

Yang X., Tek B., Beddoe G., Slabaugh G.

Conference on Medical Imaging 2010 - Computer-Aided Diagnosis, California, United States Of America, 16 - 18 February 2010, vol.7624 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 7624
  • Doi Number: 10.1117/12.844165
  • City: California
  • Country: United States Of America
  • Keywords: CAD, Adaboost, Minimum Redundancy Maximum Relevance (MRMR), bagging, MUTUAL INFORMATION, CT COLONOGRAPHY
  • Istanbul Technical University Affiliated: No


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.