Multi-level emphysema diagnosis of HRCT lung images in an incremental framework

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5370 I, Issue , 2004, Pages 42-50

  Prasad, Mithun Nagendra ^a   Sowmya, Arcot ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; BIOLOGICAL ORGANS; COMPUTER AIDED DIAGNOSIS; DISEASE CONTROL; IMAGE ANALYSIS; ITERATIVE METHODS; MEDICAL IMAGING; RADIOLOGY; TEXTURES; TISSUE;

AUTOMATED TEXTURE-BASED SYSTEMS; CHRONIC RESPIRATORY DISORDERS; EMPHYSEMA; HIGH RESOLUTION COMPUTER TOMOGRAPHY (HRCT);

COMPUTERIZED TOMOGRAPHY;

EID: 5644257435     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.533943     Document Type: Conference Paper

References (20)

1. Prasad M, Sowmya A, Multi-Level Emphysema Diagnosis in HRCT Lung Images through Robust Multi-View and Meta-Learning, Asian Conference on Computer Vision 2004, Korea (to be presented).
2. 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.
3. 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.
4. M. Collins and Y. Singer Unsupervised Models for Named Entity Classication. In Proceedings of Empirical NLP and Very Large Corpora Conference, 1999.
5. D. Pierce, C. Cardie, Limitations of Co-Training for Natural Language Learning from Large Datasets. In Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP-2001).
6. De Sa, V.R., & Ballard, D. (1998) Category Learning through Multi-Modality Sensing. In Neural Computation 10(5).
7. A. Blum and T. Mitchell., Combining labelled and Unlabelled Data with Co-Training, Proceedings of the 1998 Conference on Computational Learning Theory 1998.
8. H.-U. Kauczor, K.R. Heitmann, C.P. Heussel, D. Marwede, T. Uthmann, M. Thelen., Automatic detection and quantification of ground glass opacities on HRCT scans of the lung: comparative study of a neural network, visual assessment and a density mask, European Radiology, (1997) 1463-1472.
9. A. H. Mir, M. Hanmandlu and S. N. Tandon. Texture analysis of CT images, IEEE Engineering in Medicine and Biology, vol. 14, Nov./Dec. 1995.
10. B. Raskutti, H. Ferra, A. Kowalczyk., Combining Clustering and Co-training to Enhance Text Classification Using Unlabelled Data, Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining, 2002.
11. S. Zelikovitz and H. Hirsh, Using {LSI} for Text Classification in the Presence of Background Text, Proceedings of the 10th International Conference on Information and Knowledge Management, 2001.
12. H. Hartley and J. Rao. Classification and estimation in analysis of variance problems. Review of International Statistical Institute, 36:141-147.
13. K. Nigam, A. McCallum, S. Thrun and T. Mitchell, Text Classification from labelled and Unlabelled Documents using EM, Machine Learning, 39(2/3). pp. 103-134. 2000.
14. I. Muslea, S. Minton, C. Knoblock., Active + Semi-Supervised Learning = Robust Multi-View Learning, Proceedings of the 19th International Conference on Machine Learning 2002, p. 435-442.
15. 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, October 2001.
16. I. Muslea, Active Learning with Multiple Views. Dissertation, 2002.
17. J. S. Milton and J. C. Arnold, Introduction to Probability and Statistics, Principles and Applications for Engineering and the Computing Sciences, 3rd Edition, 1995, McGraw-Hill International Editions Probability and Statistics Series.
18. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, San Fransisco: Morgan Laufmann, 1999.
19. J. R. Quinlan, C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann (1993).
20. K. Bowyer and P. Phillips, Overview of Work in Empirical Evaluation of Computer Vision Algorithms. In: Empirical Evaluation Techniques in Computer Vision (eds Bowyer, K. & Phillips, P.), pp. 1-11, IEEE Computer Society Press.