메뉴 건너뛰기




Volumn 104, Issue 3, 2011, Pages

Image processing and machine learning for fully automated probabilistic evaluation of medical images

Author keywords

Association rules; Coronary artery disease; Machine learning; Medical diagnostics; Multi resolution image parameterization; Principal component analysis

Indexed keywords

CORONARY ARTERY DISEASE; MACHINE-LEARNING; MEDICAL DIAGNOSTICS; MULTIRESOLUTION IMAGES; PRINCIPAL COMPONENTS;

EID: 80053074478     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2010.06.021     Document Type: Article
Times cited : (33)

References (42)
  • 2
    • 35048892877 scopus 로고    scopus 로고
    • Multi-resolution image parametrization in sequential diagnostics of coronary artery disease
    • Springer, Berlin, Heidelberg, R. Bellazzi, A. Abu-Hanna, J. Hunter (Eds.)
    • Kukar M., Šajn L., GroŠelj C., GroŠelj J. Multi-resolution image parametrization in sequential diagnostics of coronary artery disease. Artificial Intelligence in Medicine 2007, Springer, Berlin, Heidelberg, pp. 119-129. R. Bellazzi, A. Abu-Hanna, J. Hunter (Eds.).
    • (2007) Artificial Intelligence in Medicine , pp. 119-129
    • Kukar, M.1    Šajn, L.2    GroŠelj, C.3    Grošelj, J.4
  • 3
    • 70350236906 scopus 로고    scopus 로고
    • Improving probabilistic interpretation of medical diagnoses with multi-resolution image parameterization: a case study
    • Springer, C. Combi, Y. Shahar, A. Abu-Hanna (Eds.)
    • Kukar M., Šajn L. Improving probabilistic interpretation of medical diagnoses with multi-resolution image parameterization: a case study. 12th Conference on Artificial Intelligence in Medicine 2009, 136-145. Springer. C. Combi, Y. Shahar, A. Abu-Hanna (Eds.).
    • (2009) 12th Conference on Artificial Intelligence in Medicine , pp. 136-145
    • Kukar, M.1    Šajn, L.2
  • 5
    • 11844267162 scopus 로고    scopus 로고
    • Artificial neural network modeling of stress single-photon emission computed tomographic imaging for detecting extensive coronary artery disease
    • 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. The American Journal of Cardiology 2005, 95(2):178-181.
    • (2005) The American Journal of Cardiology , vol.95 , Issue.2 , pp. 178-181
    • Allison, J.S.1    Heo, J.2    Iskandrian, A.E.3
  • 6
    • 0034159892 scopus 로고    scopus 로고
    • Predictions of coronary artery stenosis by artificial neural network
    • Mobley B.A., Schechter E., Moore W.E. Predictions of coronary artery stenosis by artificial neural network. Artificial Intelligence in Medicine 2000, 18(3):187-203.
    • (2000) Artificial Intelligence in Medicine , vol.18 , Issue.3 , pp. 187-203
    • Mobley, B.A.1    Schechter, E.2    Moore, W.E.3
  • 7
    • 0346218243 scopus 로고    scopus 로고
    • WeAidU-a decision support system for myocardial perfusion images using artificial neural networks
    • Ohlsson M. WeAidU-a decision support system for myocardial perfusion images using artificial neural networks. Artificial Intelligence in Medicine 2004, 30:49-60.
    • (2004) Artificial Intelligence in Medicine , vol.30 , pp. 49-60
    • Ohlsson, M.1
  • 9
    • 0038500689 scopus 로고    scopus 로고
    • Active subgroup mining: a case study in coronary heart disease risk group detection
    • Gamberger D., Lavrač N., Krstačič G. Active subgroup mining: a case study in coronary heart disease risk group detection. Artificial Intelligence in Medicine 2003, 28(1):27-57.
    • (2003) Artificial Intelligence in Medicine , vol.28 , Issue.1 , pp. 27-57
    • Gamberger, D.1    Lavrač, N.2    Krstačič, G.3
  • 16
    • 33646077220 scopus 로고    scopus 로고
    • Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis
    • Peng Y., Yao B., Jiang J. Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis. Artificial Intelligence in Medicine 2006, 37(1):43-53.
    • (2006) Artificial Intelligence in Medicine , vol.37 , Issue.1 , pp. 43-53
    • Peng, Y.1    Yao, B.2    Jiang, J.3
  • 18
    • 0027682531 scopus 로고
    • Inductive Bayesian learning in medical diagnosis
    • Kononenko I. Inductive Bayesian learning in medical diagnosis. Applied Artificial Intelligence 1993, 7:317-337.
    • (1993) Applied Artificial Intelligence , vol.7 , pp. 317-337
    • Kononenko, I.1
  • 19
    • 0034922742 scopus 로고    scopus 로고
    • Machine learning for medical diagnosis: history, state of the art and perspective
    • Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 2001, 3:89-109.
    • (2001) Artificial Intelligence in Medicine , vol.3 , pp. 89-109
    • Kononenko, I.1
  • 20
    • 0031105969 scopus 로고    scopus 로고
    • Feature subset selection for classification of histological images
    • Jelonek J., Stefanowski J. Feature subset selection for classification of histological images. Artificial Intelligence in Medicine 1997, 9(3):227-239.
    • (1997) Artificial Intelligence in Medicine , vol.9 , Issue.3 , pp. 227-239
    • Jelonek, J.1    Stefanowski, J.2
  • 21
    • 0001413186 scopus 로고    scopus 로고
    • Feature subset selection using a genetic algorithm
    • Yang J., Honavar V. Feature subset selection using a genetic algorithm. IEEE Intelligent Systems 1998, 380-385.
    • (1998) IEEE Intelligent Systems , pp. 380-385
    • Yang, J.1    Honavar, V.2
  • 22
    • 34548104873 scopus 로고    scopus 로고
    • Towards symbolic mining of images with association rules: preliminary results on textures
    • Bevk M., Kononenko I. Towards symbolic mining of images with association rules: preliminary results on textures. Intelligent Data Analysis 2006, 10(4):379-393.
    • (2006) Intelligent Data Analysis , vol.10 , Issue.4 , pp. 379-393
    • Bevk, M.1    Kononenko, I.2
  • 26
    • 43949092781 scopus 로고    scopus 로고
    • Multiresolution image parametrization for improving texture classification
    • Šajn L., Kononenko I. Multiresolution image parametrization for improving texture classification. EURASIP Journal on Advances in Signal Processing 2008, 2008(1):1-12.
    • (2008) EURASIP Journal on Advances in Signal Processing , vol.2008 , Issue.1 , pp. 1-12
    • Šajn, L.1    Kononenko, I.2
  • 27
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • 0920-5691
    • Lowe D.G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2004, 60(2):91-110. 0920-5691.
    • (2004) International Journal of Computer Vision , vol.60 , Issue.2 , pp. 91-110
    • Lowe, D.G.1
  • 28
    • 0000325341 scopus 로고
    • The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science
    • Pearson K. Principal Components Analysis 1901, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, p. 559.
    • (1901) Principal Components Analysis , pp. 559
    • Pearson, K.1
  • 31
    • 0031765718 scopus 로고    scopus 로고
    • Scintigraphic diagnosis of coronary artery disease: myocardial bull's-eye images contain the important information
    • 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 1998, 60(18).
    • (1998) Clinical Physiology , vol.60 , Issue.18
    • Lindahl, D.1    Palmer, J.2    Pettersson, J.3    White, T.4    Lundin, A.5    Edenbrandt, L.6
  • 32
    • 0141990695 scopus 로고    scopus 로고
    • Theoretical and empirical analysis of ReliefF and RReliefF
    • Robnik-Šikonja M., Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 2003, 53:23-69.
    • (2003) Machine Learning , vol.53 , pp. 23-69
    • Robnik-Šikonja, M.1    Kononenko, I.2
  • 33
    • 0002593344 scopus 로고
    • Multi-interval discretization of continuous-valued attributes for classification learning
    • Morgan Kaufmann, New York, San Mateo, R. Bajcsy (Ed.)
    • Fayyad U.M. Multi-interval discretization of continuous-valued attributes for classification learning. Proc. International Joint Conferences on Artificial Intelligence 1993, 1022-1027. Morgan Kaufmann, New York, San Mateo. R. Bajcsy (Ed.).
    • (1993) Proc. International Joint Conferences on Artificial Intelligence , pp. 1022-1027
    • Fayyad, U.M.1
  • 36
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 2006, 7:1-30.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 40
    • 0005662389 scopus 로고
    • The probability of a correct diagnosis
    • Kluwer Academic Publishers, Dordrecht, The Netherlands, J. Candell-Riera, D. Ortega-Alcalde (Eds.)
    • Olona-Cabases M. The probability of a correct diagnosis. Nuclear Cardiology in Everyday Practice 1994, 348-357. Kluwer Academic Publishers, Dordrecht, The Netherlands. J. Candell-Riera, D. Ortega-Alcalde (Eds.).
    • (1994) Nuclear Cardiology in Everyday Practice , pp. 348-357
    • Olona-Cabases, M.1
  • 41
    • 0000489573 scopus 로고
    • Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease
    • Pollock B.H. Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease. Cardiovascular Review Reports 1983, 4:367-375.
    • (1983) Cardiovascular Review Reports , vol.4 , pp. 367-375
    • Pollock, B.H.1
  • 42
    • 85146422424 scopus 로고
    • A practical approach to feature selection
    • Morgan Kaufmann, Aberdeen, UK, D. Sleeman, P. Edwards (Eds.)
    • Kira K., Rendell L. A practical approach to feature selection. Proc. Intern. Conf. on Machine Learning 1992, 249-256. Morgan Kaufmann, Aberdeen, UK. D. Sleeman, P. Edwards (Eds.).
    • (1992) Proc. Intern. Conf. on Machine Learning , pp. 249-256
    • Kira, K.1    Rendell, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.