Improving computer aided disease detection using knowledge of disease appearance

Studies in Health Technology and Informatics

Volumn 129, Issue , 2007, Pages 1324-1328

  Zrimec, Tatjana ^a   Wong, James S ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

computer aided diagnosis; honeycombing detection; lung diagnosis; lung HRCT; medical imaging

Indexed keywords

BIOLOGICAL ORGANS; COMPUTER AIDED DIAGNOSIS; PATTERN RECOGNITION;

ACCURATE QUANTIFICATIONS; COMPUTER AIDED DETECTION; COMPUTER VISION TECHNIQUES; DIFFUSE LUNG DISEASE; DISEASE DETECTION; LUNG HRCT; PATTERN DETECTION; TEXTURE DESCRIPTORS;

MEDICAL IMAGING;

ARTICLE; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DECISION TREE; HUMAN; LUNG; LUNG DISEASE; RADIOGRAPHY;

BAYES THEOREM; DECISION TREES; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; LUNG; LUNG DISEASES; TOMOGRAPHY, X-RAY COMPUTED;

EID: 35748944570     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (11)

1. Webb WR, Müller NL, Naidich DP. High-Resolution CT of the Lung. 3rd ed. Lippincott Williams & Wilkins, Philadelphia, 2001
2. Uppaluri R, Hoffman EA, Sonka M, Hartley PG. Computer Recognition of Regional Lung Disease Patterns. American Journal of Respiratory and Critical Care Medicine 1999:160(2): 648-654
3. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang CT, Suzuki K, Doi K. Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Medical Physics 2003:30(9): 2440-2454
4. Rudrapatna M, Sowmya A, Zrimec T, Wilson P, Kossoff G, Wong J, Busayarat S, Misra A, Lucas P, LMIK-Learning Medical Image Knowledge: An Internet-based medical image knowledge acquisition framework, Electronic Imaging Science and Technology-IS&T/SPIE's 16th Annual Symposium, San Jose, CA. 2004
5. Papasoulis J, LMIK-Anatomy and Lung Measurements using Active Contour Snakes, Undergraduate thesis, supervisor: T. Zrimec, Computer Science and Engineering, University of New South Wales, Sydney, Australia, 2003
6. Haralick RM, "Statistical and Structural Approaches to Texture," Proceedings of the IEEE 67, pp. 786-804, 1979
7. Hall, MA. Correlation-based feature selection for discrete and numeric class machine learning. In: ICML '00: Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc. 2000; pp. 359-366
8. Witten, IH, Frank E. Data Mining: Practical machine learning tools and techniques. 2nd edition, Morgan Kaufmann, San Francisco, 2005
9. Zrimec T, Busayarat S, Wilson P. A 3D model of the human lung with lung regions characterization. ICIP 2004 Proc. IEEE Int. Conf. on Image Processing, 2004, pp. 1149-1152
10. Zrimec T, Busayarat S, 3D modelling and visualisation of the human lung, Proc. 2nd Int. Symp. on 3D Data Processing, Visualization and Transmission, Thessaloniki, Greece, September 2004
11. Adams R, Bischof L. Seeded region growing. Pattern Analysis and Machine Intelligence, IEEE Transactions on 1994: 16(6): 641-647.