Development of an automatic classification system for differentiation of obstructive lung disease using HRCT

Journal of Digital Imaging

Volumn 22, Issue 2, 2009, Pages 136-148

  Kim, Namkug ^a,b   Seo, Joon Beom ^a   Lee, Youngjoo ^b   Lee, June Goo ^b   Kim, Song Soo ^c   Kang, Suk Ho ^b  

^a University of Ulsan College of Medicine   (South Korea)

^b Seoul National University   (South Korea)

^c Chungnam National University College of Medicine   (South Korea)

Author keywords

Bayesian classifier; Classifier optimization; Emphysema; Machine learning; Obstructive lung disease; Shape analysis; Support vector machine; Texture analysis

Indexed keywords

BAYESIAN CLASSIFIER; EMPHYSEMA; MACHINE LEARNING; OBSTRUCTIVE LUNG DISEASE; SHAPE ANALYSIS; SUPPORT VECTOR MACHINE; TEXTURE ANALYSIS;

AUTOMATIC INDEXING; BAYESIAN NETWORKS; BIOLOGICAL ORGANS; CLASSIFIERS; COMPUTERIZED TOMOGRAPHY; DIAGNOSTIC RADIOGRAPHY; IMAGE RETRIEVAL; PIXELS; PULMONARY DISEASES; ROBOT LEARNING; STATISTICAL TESTS; TEXTURES;

SUPPORT VECTOR MACHINES;

ARTICLE; BAYES THEOREM; BRONCHIOLITIS OBLITERANS; CHRONIC OBSTRUCTIVE LUNG DISEASE; CLASSIFIER; CONTROLLED STUDY; HIGH RESOLUTION COMPUTER TOMOGRAPHY; HUMAN; IMAGE ANALYSIS; LUNG EMPHYSEMA; MACHINE LEARNING; MAJOR CLINICAL STUDY; PRIORITY JOURNAL;

BAYES THEOREM; BRONCHIOLITIS OBLITERANS; DIAGNOSIS, DIFFERENTIAL; HUMANS; LUNG DISEASES, OBSTRUCTIVE; OBSERVER VARIATION; PULMONARY EMPHYSEMA; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX; TOMOGRAPHY, X-RAY COMPUTED;

EID: 62949102804     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-008-9147-7     Document Type: Article

References (24)

1. SJ Copley AU Wells NL Müller MB Rubens NP Hollings JR Cleverley DG Milne DM Hansell 2002 Thin-section CT in obstructive pulmonary disease: discriminatory value Radiology 223 3 812 819
2. K Chan TW Lee PA Sample MH Goldbaum RN Weinreb TJ Sejnowski 2002 Comparison of machine learning and traditional classifiers in glaucoma diagnosis IEEE Trans Biomed Eng 49 9 963 974
3. L Wei Y Yang RM Nishikawa Y Jiang 2005 A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications IEEE Trans Med Imaging 24 3 371 380
4. F Chabat GZ Yang DM Hansell 2003 Obstructive lung diseases: texture classification for differentiation at CT Radiology 228 3 871 877
5. Y Xu EJ van Beek Y Hwanjo J Guo G McLennan EA Hoffman 2006 Computer-aided classification of interstitial lung diseases Via MDCT: 3D Adaptive Multiple Feature Method (3D AMFM) Acad Radiol 13 8 969 978
6. RM Rangayyan NR Mudigonda JE Desautels 2000 Boundary modeling and shape analysis methods for classification of mammographic masses Med Biol Eng Comput 38 487 496
7. M Yamagishi H Koba A Nakagawa A Honma K Yokokawa T Saitoh H Harada H Watanabe Y Mori S Katoh 1991 Qualitative assessment of centrilobular emphysema using computed tomography Nippon Igaku Hoshasen Gakkai Zasshi 51 3 203 212
8. S Iwano T Nakamura Y Kamioka T Ishigaki 2005 Computer-aided diagnosis: a shape classification of pulmonary nodules imaged by high-resolution CT Comput Med Imaging Graph 29 7 565 570
9. Gonzalez RC, Woods RE: Digital Image Processing, 2nd edition. New Jersey: Prentice Hall, 2002, pp. 532-534
10. E Amaldi V Kann 1998 On the approximation of minimizing non zero variables or unsatisfied relations in linear systems Theor Comput Sci 209 237 260
11. Kittler J: Feature set search algorithms. Pattern recognition and signal processing. Sijtho and Noordho 41-60, 1978
12. S Handrick B Naimipour JD Furst DS Raicu 2006 Binning strategies evaluation for tissue classification in computed tomography images Proc SPIE Med Imaging 6144 1476 1486
13. Vapnik VN: The Nature of Statistical Learning Theory, New York: Springer-Verlag, 2006
14. N Friedman D Geiger M Goldszmidt 1997 Bayesian network classifiers Mach Learn 29 131 163
15. Mitchell T: Machine Learning, New York: McGraw-Hill, 1997
16. Joachims T, Schölkopf B, Burges C, Smola A: Making large-scale SVM learning practical. Advances in Kernel methods - support vector learning, Cambridge, MA: MIT-Press, 1999
17. BE Boser IM Guyon VN Vapnik 1992 A training algorithm for optimal margin classifiers Proc Fifth Annu Workshop Comput Learning Theor 5 144 152
18. CJC Burges 1998 A tutorial on support vector machines for pattern recognition Data Mining Knowledge Discovery 2 2 121 167
19. ST Dumais 1998 Using SVMs for text categorization IEEE Intell Syst 13 4 21 23
20. Burges CJC, Schölkopf B: Improving the accuracy and speed of support vector learning machine, Cambridge, MA: MIT Press, 1997
21. TS Furey N Cristianini N Duffy DW Bednarski M Schummer D Haussler 2000 Support vector machine classification and validation of cancer tissue samples using microarray expression data Bioinformatics 16 906 914
22. CHQ Ding I Dubchak 2001 Multi-class protein fold recognition using support vector machines and neural networks Bioinformatics 16 349 358
23. A Zien G Rätsch S Mika B Schölkopf T Lengauer KR Müller 2000 Engineering support vector machine kernels that recognize translation initiation sites Bioinformatics 16 799 807
24. Schurmann J: Pattern classification: a unified view of statistical and neural approaches, New York: Wiley, NY, 1996