An Efficient Feature Selection Algorithm for Computer-aided Polyp Detection

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 by cross validation, (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 obtained by other methods, through cross validation with support vector machines (SVMs).

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  Pedro Larrañaga,et al.  Feature Subset Selection by Bayesian network-based optimization , 2000, Artif. Intell..

[3]  S. Billings,et al.  Piecewise linear identification of non-linear systems , 1987 .

[4]  J. Malley,et al.  Colonic polyps: complementary role of computer-aided detection in CT colonography. , 2002, Radiology.

[5]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[6]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[7]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[8]  Thomas G. Dietterich,et al.  Learning with Many Irrelevant Features , 1991, AAAI.

[9]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[10]  Jack Sklansky,et al.  On Automatic Feature Selection , 1988, Int. J. Pattern Recognit. Artif. Intell..

[11]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[12]  Ronald M. Summers,et al.  3D colonic polyp segmentation using dynamic deformable surfaces , 2004, SPIE Medical Imaging.

[13]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[14]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[15]  Michael T. Manry,et al.  Feature Selection Using a Piecewise Linear Network , 2006, IEEE Transactions on Neural Networks.

[16]  Ronald M. Summers,et al.  Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models , 2004, IEEE Transactions on Medical Imaging.

[17]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[18]  Sheng Chen,et al.  Orthogonal least squares methods and their application to non-linear system identification , 1989 .

[19]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[20]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[21]  Richard A. Olshen,et al.  CART: Classification and Regression Trees , 1984 .

[22]  Xia Hong,et al.  Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks , 2001, IEEE Trans. Fuzzy Syst..

[23]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[24]  Sheng Chen,et al.  Sparse modeling using orthogonal forward regression with PRESS statistic and regularization , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  Igor V. Tetko,et al.  Neural Network Studies, 2. Variable Selection , 1996, J. Chem. Inf. Comput. Sci..

[26]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[27]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[28]  Ronald M. Summers,et al.  Optimizing the support vector machines (SVM) committee configuration in a colonic polyp CAD system , 2005, SPIE Medical Imaging.

[29]  M.T. Manry,et al.  Optimal pruning of feedforward neural networks based upon the Schmidt procedure , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[30]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.