Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
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
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
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
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
Image processing and machine learning for fully automated probabilistic evaluation of medical images
Computer Methods and Programs in Biomedicine
An Expert Support System for Breast Cancer Diagnosis using Color Wavelet Features
Journal of Medical Systems
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Clinicians strive to improve established diagnostic procedures, especially those that allow them to reach reliable early diagnoses. Diagnostics is frequently performed in a stepwise manner which consists of several consecutive tests (steps). The ultimate step in this process is often the "gold standard" reference method. In stepwise testing, results of each diagnostic test can be interpreted in a probabilistic manner by using prior (pre-test) probability and test characteristics (sensitivity and specificity). By using Bayes' formula on these quantities, the posterior (post-test) probability is calculated. If the post-test probability is sufficiently high (or low) to confirm (or exclude) the presence of a disease, diagnostic process is stopped. Otherwise, it proceeds to the next step in sequence. Our case study focuses on improving probabilistic interpretation of scintigraphic images obtained from the penultimate step in coronary artery disease diagnostics. We use 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 principle component analysis, and finally used to build automatic classifiers with machine learning methods. Experiments show that the proposed approach significantly increases the number of reliable diagnoses as compared to clinical results in terms.