A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Comprehensive Segmentation of Cine Cardiac MR Images
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Left Ventricle Tracking Using Overlap Priors
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
GPU Accelerated Non-rigid Registration for the Evaluation of Cardiac Function
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Segmentation of Left Ventricle in Cardiac Cine MRI: An Automatic Image-Driven Method
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Regional heart motion abnormality detection via information measures and unscented kalman filtering
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Assessment of regional myocardial function via statistical features in MR images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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There are four main problems that limit application of pattern recognition techniques for recognition of abnormal cardiac left ventricle (LV) wall motion: 1) Normalization of the LV's size, shape, intensity level and position; 2) defining a spatial correspondence between phases and subjects; 3) extracting features; 4) and discriminating abnormal from normal wall motion. Solving these four problems is required for application of pattern recognition techniques to classify the normal and abnormal LV wall motion. In this work, we introduce a normalization scheme to solve the first and second problems. With this scheme, LVs are normalized to the same position, size, and intensity level. Using the normalized images, we proposed an intra-segment classification criterion based on a correlation measure to solve the third and fourth problems. Application of the method to recognition of abnormal cardiac MR LV wall motion showed promising results.