Medical case retrieval from a committee of decision trees

IEEE Transactions on Information Technology in Biomedicine

Volumn 14, Issue 5, 2010, Pages 1227-1235

  Quellec, Gwénolé ^a,b   Lamard, Mathieu ^b,c   Bekri, Lynda ^b,d   Cazuguel, Guy ^a,b   Roux, Christian ^a,b   Cochener, Béatrice ^b,c,d  

^a ECOLE DES MINES DE NANTES   (France)

^b INSERM   (France)

^c UNIVERSITÉ DE BRETAGNE OCCIDENTALE   (France)

^d BREST UNIVERSITY HOSPITAL   (France)

Author keywords

CAD; content based image retrieval (CBIR); decision trees (DTs); information retrieval; medical databases

Indexed keywords

CAD; CONTENT-BASED IMAGE RETRIEVAL; CONTENT-BASED INFORMATION RETRIEVAL; DATA SETS; DECISION SUPPORT TOOLS; DIABETIC RETINOPATHY; DIGITAL CONTENTS; MEDICAL CASE; MEDICAL DATA SETS; MEDICAL DATABASE; SEMANTIC INFORMATION; SINGLE IMAGES;

CONTENT BASED RETRIEVAL; DECISION SUPPORT SYSTEMS; DECISION TREES; DIAGNOSIS; EYE PROTECTION; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; MAMMOGRAPHY; MEDICAL APPLICATIONS; MEDICAL COMPUTING;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER ASSISTED DIAGNOSIS; DECISION SUPPORT SYSTEM; DECISION TREE; DIABETIC RETINOPATHY; EYE FUNDUS; FACTUAL DATABASE; HUMAN; IMAGE PROCESSING; INFORMATION RETRIEVAL; MAMMOGRAPHY; METHODOLOGY; SIGNAL PROCESSING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; DECISION SUPPORT SYSTEMS, CLINICAL; DECISION TREES; DIABETIC RETINOPATHY; DIAGNOSIS, COMPUTER-ASSISTED; FUNDUS OCULI; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MAMMOGRAPHY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 77956378054     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2010.2053716     Document Type: Article

References (30)

1. A. Aamodt, "Case-based reasoning: Foundational issues, methodological variations, and system approaches", AI Commun., vol. 7, no. 1, pp. 39-59, Mar. 1994.
2. I. Bichindaritz and C. Marling, "Case-based reasoning in the health sciences: What's next?" Artif. Intell. Med., vol. 36, no. 2, pp. 127-135, Jan. 2006.
3. C. P. Wilkinson, F. L. Ferris, 3rd, R. E. Klein, P. P. Lee, C. D. Agardh, M. Davis, D. Dills, A. Kampik, R. Pararajasegaram, and J. T. Verdaguer, "Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales", Ophthalmology, vol. 110, no. 9, pp. 1677-1682, Sep. 2003.
4. M. D. Davis, M. R. Fisher, R. E. Gangnon, F. Barton, L. M. Aiello, E. Y. Chew, F. L. Ferris, and G. L. Knatterud, "Risk factors for high-risk proliferative diabetic retinopathy and severe visual loss: Early treatment diabetic retinopathy study report 18", Invest Ophthalmol. Vis. Sci., vol. 39, no. 2, pp. 233-252, Feb. 1998.
5. J.-M. Cauvin, C. L. Guillou, B. Solaiman, M. Robaszkiewicz, P. L. Beux, and C. Roux, "Computer-assisted diagnosis system in digestive endoscopy", IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 4, pp. 256-262, Dec. 2003.
6. C. Nastar, "Indexation d'images par le contenu: un état de l'art", presented at CORESA, Issy-les-Moulineaux, France, Mar. 1997.
7. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, "Content-based image retrieval at the end of the early years", IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
8. H. Müller, N. Michoux, D. Bandon, and A. Geissbuhler, "A review of content-based image retrieval systems in medical applications-Clinical benefits and future directions", Int. J. Med. Inf., vol. 73, no. 1, pp. 1-23, Feb. 2004.
9. G. D. Tourassi, R. Vargas-Voracek, D. M. Catarious, and C. E. Floyd, "Computer-assisted detection of mammographic masses: A template matching scheme based on mutual information", Med. Phys., vol. 30, no. 8, pp. 2123-2130, 2003.
10. H. Alto, R. M. Rangayyan, and J. E. L. Desautels, "Content-based retrieval and analysis of mammographic masses", J. Electron. Imag., vol. 14, no. 2, pp. 023016-1-023016-17, 2005.
11. H. Shao, W.-C. Cui, and H. Zhao, "Medical image retrieval based on visual contents and text information", in Proc. IEEE SMC, Oct. 2004, vol. 1, pp. 1098-1103.
12. C. L. Bozec, E. Zapletal, M. Jaulent, D. Heudes, and P. Degoulet, "Towards content-based image retrieval in a HIS-integrated PACS", in Proc. AMIA, 2000, pp. 477-481.
13. S. Antani, L. R. Long, and G. R. Thoma, "A biomedical information system for combined content-based retrieval of spine X-ray images and associated text information", in Proc. ICVGIP 2002, pp. 242-247.
14. J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
15. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth, 1984.
16. Y. Freund and R. Schapire, "Experiments with a new boosting algorithm", in Proc. ICML, pp. 148-156, Jul. 1996.
17. J. R. Quinlan, "Learning with continuous classes", in Proc. 5th Aust. Joint Conf. Artif. Intell., 1992, pp. 343-348.
18. G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, and C. Roux, "Wavelet optimization for content-based image retrieval in medical databases", Med. Image Anal., vol. 14, no. 2, pp. 227-241, Apr. 2010.
19. D. Taubman and M. Marcellin, JPEG2000. Image Compression Fundamentals, Standards and Practice (The International Series in Engineering and Computer Science), Norwell, MA: Kluwer, 2001.
20. G. van de Wouwer, P. Scheunders, and D. van Dyck, "Statistical texture characterization from discrete wavelet representations", IEEE Trans. Image Process., vol. 8, no. 4, pp. 592-598, Apr. 1999.
21. M. N. Do and M. Vetterli, "Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance", IEEE Trans. Image Process., vol. 11, no. 2, pp. 146-158, Feb. 2002.
22. J. C. Bezdek, "Fuzzy mathemathics in pattern classification", Ph. D. dissertation, Applied Math. Center, Cornell Univ., Ithaca, NY, 1973.
23. A. Strehl, "Relationship-based clustering and cluster ensembles for high dimensional data mining", Doctoral dissertation [electronic resource], The University of Texas at Austin, Austin, TX, 2002.
24. L. Breiman, "Random forests", Mach. Learning, vol. 45, no. 1, pp. 5-32, Oct. 2001.
25. T. Dietterich, "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization", Mach. Learning, vol. 40, no. 2, pp. 139-157, Aug. 2000.
26. H. Guo and H. L. Viktor, "Learning from imbalanced data sets with boosting and data generation: The databoost-im approach", SIGKDD Explor Newsl, vol. 6, no. 1, pp. 30-39, Jun. 2004.
27. M. Heath, K. Bowyer, D. Kopans, W. P. Kegelmeyer, R. Moore, K. Chang, and S. Munishkumaran, "Current status of the digital database for screening mammography", in Digital Mammography. Norwell, MA: Kluwer, 1998, pp. 457-460.
28. G. Quellec, M. Lamard, P. M. Josselin, G. Cazuguel, B. Cochener, and C. Roux, "Optimal wavelet transform for the detection of microaneurysms in retina photographs", IEEE Trans. Med. Imag., vol. 27, no. 9, pp. 1230-1241, Sep. 2008.
29. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Boston, MA: Kluwer, 1989.
30. D. R. Wilson and T. R. Martinez, "Improved heterogeneous distance functions", J. Artif. Intell. Res., vol. 6, no. 1, pp. 1-34, 1997.