Multi-resolution image parametrization in stepwise diagnostics of coronary artery disease

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4594 LNAI, Issue , 2007, Pages 119-129

  Kukar, Matjaž ^a   Šajn, Luka ^a   Grošelj, Ciril ^b   Grošelj, Jera ^b  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b UNIVERSITY MEDICAL CENTRE LJUBLJANA   (Slovenia)

Author keywords

Association rules; Coronary artery disease; Image parametrization; Machine learning; Medical diagnosis; Stepwise diagnostic process

Indexed keywords

ANGIOGRAPHY; ASSOCIATION RULES; DISEASE CONTROL; ELECTROCARDIOGRAPHY; LEARNING SYSTEMS;

CLINICAL SETTING; CORONARY ARTERY DISEASE; IMAGE PARAMETRIZATION; STEPWISE DIAGNOSTIC PROCESSES;

IMAGE PROCESSING;

EID: 35048892877     PISSN: 03029743     EISSN: 16113349     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-73599-1_13     Document Type: Conference Paper

References (19)

1. Allison, J.S., Heo, J., Iskandrian, A.E.: Artificial neural network modeling of stress single-photon emission computed tomographic imaging for detecting extensive coronary artery disease. J Nucl Cardiol 95(2), 178-181 (2005)
2. Bevk, M., Kononenko, I.: Towards symbolic mining of images with association rules: Preliminary results on textures. Intelligent Data Analysis (2006)
3. Diamond, G. A., Forester, J.S.: Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. New England Journal of Medicine 300, 1350 (1979)
4. General Electric. ECToolbox Protocol Operator's Guide (2001)
5. Ferreira, C.B.R., Borges, D.L.: Automated mammogram classification using a multi-resolution pattern recognition approach. In: SIBGRAPI01, vol. 00, pp. 76 (2001)
6. Gamberger, D., Lavrac, N., Krstacic, G.: Active subgroup mining: a case study in coronary heart disease risk group detection. Artif Intell Med 28(1), 27-57 (2003)
7. Garcia, E.V., Cooke, C.D., Folks, R.D., Santana, C.A., Krawczynska, E.G., De Braal, L., Ezquerra, N.E: Diagnostic performance of an expert system for the interpretation of myocardial perfusion spect studies. J Nucl Med 42(8), 1185-1191 (2001)
8. Grošelj, C., Kukar, M., Fettich, J., Kononenko, I.: Impact of machine learning to the diagnostic certainty of the patient's group with low coronary artery disease probability. In: Proc. Computer-Aided Data Analysis in Medicine, Bled, Slovenia, pp. 68-74 (1997)
9. Kukar, M.: Transductive reliability estimation for medical diagnosis. Artif. intell. med. 81-106 (2003)
10. Kukar, M., Kononenko, I., Grošelj, C., Kralj, K., Fettich, J.: Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine 16(1), 25-50 (1999)
11. Kurgan, L.A., Cios, K.J., Tadeusiewicz, R.: Knowledge discovery approach to automated cardiac spect diagnosis. Artif Intell Med 23(2), 149-169 (2001)
12. Lindahl, D., Palmer, J., Pettersson, J., White, T., Lundin, A., Edenbrandt, L.: Scintigraphic diagnosis of coronary artery disease: myocardial bull's-eye images contain the important information. Clinical Physiology 6(18) (1998)
13. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91-110 (2004)
14. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971-987 (2002)
15. Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53, 23-69 (2003)
16. Rushing, J.A., Ranganath, H.S., Hinke, T.H., Graves, S.J.: Using association rules as texture features. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(8), 845-858 (2001)
17. Šajn, L.: Multiresolution parameterization for texture classification and its application in analysis of scintigrafic images. PhD thesis, Faculty of Computer and Information Science, University of Ljubljana, in Slovene (2007)
18. Šajn, L., Kukar, M., Kononenko, I., Milčinski, M.: Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics. Computer Methods and Programs in Biomedicine 80(1), 47-55, 10 (2005)
19. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)