Online Feature Selection for Classifying Emphysema in HRCT Images

Feature subset selection, applied as a preprocessing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. In the classic formulation of the feature selection problem, it is assumed that all the features are available at the beginning. However, in many real world problems, there are scenarios where not all features are present initially and must be integrated as they become available. In such scenarios, online feature selection provides an efficient way to sort through a large space of features. It is in this context that we introduce online feature selection for the classification of emphysema, a smoking related disease that appears as low attenuation regions in High Resolution Computer Tomography (HRCT) images. The technique was successfully evaluated on 61 HRCT scans and compared with different online feature selection approaches, including hill climbing, best first search, grafting, and correlation-based feature selection. ...

[1]  Arcot Sowmya,et al.  Multi-level classification of emphysema in HRCT lung images , 2009, Pattern Analysis and Applications.

[2]  James Theiler,et al.  Online feature selection for pixel classification , 2005, ICML.

[3]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[4]  Volume Assp,et al.  ACOUSTICS. SPEECH. AND SIGNAL PROCESSING , 1983 .

[5]  Arcot Sowmya,et al.  Feature subset selection using ICA for classifying emphysema in HRCT images , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[6]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[7]  Hans Knutsson,et al.  Recognizing emphysema - a neural network approach , 2002, Object recognition supported by user interaction for service robots.

[8]  Pramod K. Singh.  Emphysema detection in JPEG compressed HRCT lung images , 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..

[9]  William Mendenhall,et al.  Introduction to Probability and Statistics , 1961, The Mathematical Gazette.

[10]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[11]  James Theiler,et al.  Online Feature Selection using Grafting , 2003, ICML.

[12]  W. J. DeCoursey,et al.  Introduction: Probability and Statistics , 2003 .

[13]  A. H. Mir,et al.  Texture analysis of CT images , 1995 .

[14]  N J Morrison,et al.  Quantitation of emphysema by computed tomography using a "density mask" program and correlation with pulmonary function tests. , 1990, Chest.

[15]  P. Nurmi,et al.  Online feature selection for contextual time series data ( Extended abstract ) , 2022 .

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

[17]  E. Hoffman,et al.  Computer recognition of regional lung disease patterns. , 1999, American journal of respiratory and critical care medicine.

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

[19]  Qionghai Dai,et al.  Relevance feedback learning with feature selection in region-based image retrieval , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[20]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[21]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[22]  Björn Stenger,et al.  Tracking Using Online Feature Selection and a Local Generative Model , 2007, BMVC.

[23]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[24]  Joseph A. Wolkan,et al.  Introduction to probability and statistics , 1994 .