Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multiresolution image parametrization for improving texture classification
EURASIP Journal on Advances in Signal Processing
Multi-resolution Image Parametrization in Stepwise Diagnostics of Coronary Artery Disease
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
An Efficient Explanation of Individual Classifications using Game Theory
The Journal of Machine Learning Research
WeAidU-a decision support system for myocardial perfusion images using artificial neural networks
Artificial Intelligence in Medicine
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
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Coronary artery disease is one of its most important causes of early mortality in western world. Therefore, clinicians seek to improve diagnostic procedures in order to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics is often performed in a sequential manner, where the four diagnostic steps typically consist of evaluation of (1) signs and symptoms of the disease and electrocardiogram (ECG) at rest, (2) sequential ECG 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 and probabilistic interpretation of scintigraphic images obtained from the penultimate step. We use automatic image parameterization on multiple resolutions, based on spatial 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 neural networks and naive Bayes learning methods. Experiments show that our approach significantly increases diagnostic accuracy, specificity and sensitivity with respect to clinical results.