Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

Data and Knowledge Engineering

Volumn 68, Issue 12, 2009, Pages 1370-1382

  Ribeiro, Marcela X ^a   Bugatti, Pedro H ^a   Traina Jr , Caetano ^a   Marques, Paulo M A ^a   Rosa, Natalia A ^a   Traina, Agma J M ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Association rules; Associative classifier; Computer aided diagnosis; Content based image retrieval; Discretization; Feature selection

Indexed keywords

ASSOCIATION RULE MINING; ASSOCIATIVE CLASSIFIERS; CAD SYSTEM; CBIR SYSTEM; COMPUTER-AIDED DIAGNOSIS SYSTEM; CONTENT BASED IMAGE RETRIEVAL; DISCRETIZATIONS; FEATURE SELECTION; FEATURE VECTORS; IMAGE DIAGNOSIS; LOW-LEVEL FEATURES; MEDICAL IMAGE ANALYSIS; MEDICAL SYSTEMS; SIMILARITY QUERY;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; CLASSIFIERS; COMPUTATIONAL METHODS; CONTENT BASED RETRIEVAL; FEATURE EXTRACTION; IMAGE ANALYSIS; INFORMATION RETRIEVAL; LEARNING SYSTEMS;

COMPUTER AIDED DIAGNOSIS;

EID: 71749094114     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2009.07.002     Document Type: Article

References (21)

1. P.H. Bugatti, M.X. Ribeiro, A.J.M. Traina, C.T. Jr, Content-based retrieval of medical images by continuous feature selection, in: The 21th IEEE International Symposium on Computer-Based Medical Systems, Jyvaskyla, Finland, 2008, pp. 272-277.
2. M.X. Ribeiro, A.J.M. Traina, C.T. Jr., N.A. Rosa, P.M.A. Marques, How to improve medical image diagnosis through association rules: The idea method, in: The 21th IEEE International Symposium on Computer-Based Medical Systems, Jyvaskyla, Finland, 2008, pp. 266-271.
3. Holte R.C. Very simple classification rules perform well on most commonly used datasets. Machine Learning 11 (1993) 63-91
4. M. Malcok, Y. Aslandogan, A. Yesildirek, Fractal dimension and similarity search in high-dimensional spatial databases, in: IEEE Intl. Conf. on Information Reuse and Integration, Waikoloa, Hawaii, USA, 2006, pp. 380-384.
5. Y. Rubner, C. Tomasi, Perceptual Metrics for Image Database Navigation the Kluwer Intl. Series in Engineering and Computer Science, Kluwer Academic Publishers, 2001.
6. K. Kira, L.A. Rendell, A practical approach for feature selection, in: Ninth Intl. Conf. on Machine Learning, Aberdeen, Scotland,1992, pp. 249-256.
7. I. Kononenko, Estimating attributes: analysis and extension of relief, in: European Conf. on Machine Learning, Catania, Italy, 1994, pp. 171-182.
8. C. Cardie, Using decision trees to improve case-based learning, in: 10th Intl. Conf. on Machine Learning, 1993, pp. 25-32.
9. R. Quinlan, C4.5: Programs for Machine Learning, San Mateo, CA, 1992, pp. 1-302.
10. R. Agrawal, T. Imielinski, A.N. Swami, Mining association rules between sets of items in large databases, in: Proceedings of the 1993 ACM SIGMOD ICMD, Washington, D.C., 1993, pp. 207-216.
11. M.X. Ribeiro, A.G.R. Balan, J.C. Felipe, A.J.M. Traina, C. Traina Jr., Mining statistical association rules to select the most relevant medical image features, in: First Intl. Workshop on Mining Complex Data, Houston, 2005, pp. 91-98.
12. M.-L. Antonie, O.R. Zaane, A. Coman, Associative classifiers for medical images, in: LNAI 2797, MMCD, Springer-Verlag, 2003, pp. 68-83.
13. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Intl. Conf. on VLDB, Santiago de Chile, Chile, 1994, pp. 487-499.
14. X. Wang, M. Smith, R. Rangayyan, Mammographic information analysis through association-rule mining, in: IEEE CCGEI, 2004, pp. 1495-1498.
15. Pan H., Li J., and Wei Z. Mining interesting association rules in medical images. Advance Data Mining and Medical Applications (2005), Springer-Verlag 598-609
16. Ordonez C., Ezquerra N., and Santana C.A. Constraining and summarizing association rules in medical data. Knowledge and Information Systems 9 3 (2006) 259-283
17. Baeza-Yates R.A., and Ribeiro-Neto B.A. Modern Information Retrieval (1999), Addison-Wesley, Wokingham, UK
18. Traina J., Caetano, Traina A.J.M., Faloutsos C., and Seeger B. Fast indexing and visualization of metric datasets using slim-trees. IEEE Transactions on Knowledge and Data Engineering 14 2 (2002) 244-260
19. Haralick R.M., Shanmugam K., and Distein I. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3 (1973) 610-621
20. Otsu N. A thresholding selection method from gray-level histogram. IEEE Transactions on Systems, Man and Cybernetics 9 (1979) 62-66
21. John G.H., and Langley P. Estimating continuous distributions in Bayesian classifiers (1995), Morgan Kaufman, San Mateo pp. 338-345