Feature subset selection using ICA for classifying emphysema in HRCT images

Proceedings - International Conference on Pattern Recognition

Volumn 4, Issue , 2004, Pages 515-518

  Prasad, Mithun Nagendra ^a   Sowmya, Arcot ^a   Koch, Inge ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION SCHEME; LINEAR DISCRIMINANT ANALYSIS (LDA); SUBSETS; VISUAL EVALUATION;

ALGORITHMS; IMAGE ANALYSIS; INDEPENDENT COMPONENT ANALYSIS; LEARNING SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING; SET THEORY;

FEATURE EXTRACTION;

EID: 10044266234     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2004.1333824     Document Type: Conference Paper

References (11)

1. Kinsella, M., N.L. Mueller, R.T. Abboud, N.J. Morrison, A. DyBuncio. Quantification of emphysema by computed tomography using a density mask program and correlation with pulmonary function tests, Chest 97 (1990) 315-321.
2. O. Friman, M. Borga, M. Lundberg, U. Tylén, H. Knutsson., Recognizing Emphysema - A Neural Network Approach, Proceedings of 16th International Conference on Pattern Recognition, August 2002.
3. R. Kohavi, G. H. John: Wrappers for Feature Subset Selection. Artificial Intelligence 97(1-2): 273-324, 97.
4. th edn., Pearson Prentice Hall 2002.
5. M.Bressan, J.Vitria. Feature Subset Selection in an ICA Space, in M.T. Escrig Monferrer, F. Toledo, E. Golobardes (Eds.), Topics in Artificial Intelligence, Sringer Verlag Series: Lecture Notes in Computer Science. Volume. 2504, 2002. pp. 196-206.
6. Hyvarinen, A and Oja, E. (1999) Independent Component Analysis: A Tutorial, internet available at http://www.cis.hut.fi/projects/ica
7. P. T. Chiu and A. Sowmya. Lung Boundary Detection and Low Level feature extraction and analysis from HRCT images, VISIM: Information Retrieval and Exploration of Large Medical Image Collections, 2001.
8. A. H. Mir, M. Hanmandlu and S. N. Tandon. Texture analysis of CT images, IEEE Engineering in Medicine and Biology, vol. 14, 1995.
9. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, San Fransisco: Morgan Laufmann, 99.
10. T. Mitchell, Machine Learning, McGraw Hill, 1997.
11. S. Basu, A. Banerjee, R. Mooney. Semi-supervised Clustering by Seeding, Proceedings of the 19th International Conference on Machine Learning, 2002.