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4th International Conference on Computer Vision/Computer Graphics Collaboration Techniques (MIRAGE 2009)

作者： Kukar, Matjaz Sajn, Luka Univ Ljubljana Fac Comp & Informat Sci SI-1001 Ljubljana Slovenia

ISBN: (纸本)9783642018107

Coronary artery disease has been described as one of the curses of the western world, as it is one of the most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics is typically performed in a stepwise manner. The four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and ECG (electrocardiogram) at rest, (2) sequential ECG testing during the controlled exercise, (3) myocardial perfusion scintigraphy, and finally (4) coronary angiography, that is considered as the "gold standard" reference method. Our study focuses on improving diagnostic performance of the third diagnostic level. Myocardial scintigraphy is non invasive;it results in a series of medical images that are relatively inexpensively obtained. In clinical practice, these images are manually described (parameterized) by expert physicians. In the paper we present an innovative alternative to manual image evaluation - an automatic image parameterization in multiple resolutions, based on texture description with specialized association rules. Extracted image parameters are combined into more informative composite parameters by means of principle component analysis, and finally used to build automatic classifiers with machine learning methods. Our experiments with synthetic datasets show that association-rule-based multi-resolution image parameterization equals or surpasses other state-of-the-art methods for finding multiple informative resolutions. Experimental results in coronary artery disease diagnostics confirm these results as our approach significantly improves the clinical results in terms of quality of image parameters as well as diagnostic performance.

关键词： machine learning coronary artery disease medical diagnostics multi-resolution image parameterization association rules principal component analysis

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Modern parameterization and explanation techniques in diagnostic decision support system: A case study in diagnostics of coronary artery disease

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2011年第2期52卷 77-90页

作者： Kukar, Matjaz Kononenko, Igor Groselj, Ciril Univ Ljubljana Fac Comp & Informat Sci SI-1001 Ljubljana Slovenia Univ Med Ctr Ljubljana Dept Nucl Med SI-1000 Ljubljana Slovenia

Objective: Coronary artery disease has been described as one of the curses of the western world, as it is one of its most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics are typically performed in a sequential manner. The four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and electrocardiogram at rest, (2) sequential electrocardiogram testing during the controlled exercise, (3) myocardial perfusion scintigraphy, and (4) finally coronary angiography, that is considered as the "gold standard" reference method. Our study focuses on improving diagnostic performance of the third, virtually non-invasive, diagnostic level. Methods and materials: Myocardial scintigraphy results in a series of medical images that are obtained by relatively inexpensive means. In clinical practice, these images are manually described (parameterized) by expert physicians. In the paper we present an innovative alternative to manual image evaluation an automatic image parameterization on multiple resolutions, based on texture description with specialized association rules. Extracted image parameters are combined into more informative composite parameters by means of principal component analysis, and finally used to build automatic classifiers with machine learning methods. Results: Our experiments with synthetic datasets show that association-rule-based multi-resolution image parameterization works very well for scintigraphic images of the heart. In coronary artery disease diagnostics we confirm these results as our approach significantly improves on clinical results in terms of diagnostic performance. We improve diagnostic accuracy by 17%, specificity by 12% and sensitivity by 22%. We also significantly improve the number of reliably diagnosed patients by 19% for positive diagnoses, and 16%

关键词： Machine learning multi-resolution image parameterization Association rules Principal component analysis Coronary artery disease diagnostics

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

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2011年第3期104卷 E75-E86页

作者： Sajn, Luka Kukar, Matjaz Univ Ljubljana Fac Comp & Informat Sci SI-1001 Ljubljana Slovenia

The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician's judgment and may assist in decisions on cost effectiveness of tests. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

关键词： Machine learning Coronary artery disease Medical diagnostics multi-resolution image parameterization Association rules Principal component analysis

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