An Efficient Feature Selection Algorithm for Computer-Aided Polyp Detection

Jiang Li, Jianhua Yao, and Ronald M. Summers, National Institutes of Health; Amy K. Hara, Mayo Clinic

We present an efficient feature selection algorithm for computer aided detection (CAD) computed tomographic (CT) colonography. The algorithm 1) determines an appropriate piecewise linear network (PLN) model based on a learning theorem for the given data set, 2) applies the orthonormal least square (OLS) procedure to the PLN model utilizing a Modified Schmidt procedure, and 3) uses a floating search algorithm to select features that minimize the output variance. The undesirable ``nesting effect" is prevented by the floating search approach, and the piecewise linear OLS procedure makes this algorithm very computationally efficient because the Modified Schmidt procedure only requires one data pass during the whole searching process. The selected features are compared to those selected by other methods, through cross-validation with a committee of support vector machines (SVMs).

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