Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
An Information Fusion Framework for Robust Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Nonparametri information fusion for motion estimation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Wall Motion Classification of Stress Echocardiography Based on Combined Rest-and-Stress Data
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Bayesian chain classifiers for multidimensional classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Hi-index | 0.00 |
Coronary Heart Disease can be diagnosed by measuring and scoring regional motion of the heart wall in ultrasound images of the left ventricle (LV) of the heart. We describe a completely automated and robust technique that detects diseased hearts based on detection and automatic tracking of the endocardium and epicardium of the LV. The local wall regions and the entire heart are then classified as normal or abnormal based on the regional and global LV wall motion. In order to leverage structural information about the heart we applied Bayesian Networks to this problem, and learned the relations among the wall regions off of the data using a structure learning algorithm. We checked the validity of the obtained structure using anatomical knowledge of the heart and medical rules as described by doctors. The resultant Bayesian Network classifier depends only on a small subset of numerical features extracted from dual-contours tracked through time and selected using a filter-based approach. Our numerical results confirm that our system is robust and accurate on echocardiograms collected in routine clinical practice at one hospital; our system is built to be used in real-time.