Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Elements of information theory
Elements of information theory
Surface shape and curvature scales
Image and Vision Computing
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Some Notes on Alternating Optimization
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
An introduction to variable and feature selection
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Nonparametri information fusion for motion estimation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Coronary heart disease (CHD) is a global epidemic that is the leading cause of death worldwide. CHD can be detected by measuring and scoring the regional and global motion of the left ventricle (LV) of the heart. This project describes a novel automatic technique which can detect the regional wall motion abnormalitie, of the LV from echocardiograms. Given a sequence of endocardial contours extracted from LV ultrasound images, the sequence of contours moving through time can be interpreted as a three-dimensional (3D) surface. From the 3D surfaces, we compute several geometry-based features (shape-index values, curvedness, surface normals, etc.) to obtain histograms-based similarity functions that are optimally combined using a mathematical programming approach to learn a kernel function designed to classify normal vs. abnormal heart wall motion. In contrast with other state-of-the-art methods, our formulation also generates sparse kernels. Kernel sparsity is directly related to the computational cost of the kernel evaluation, which is an important factor when designing classifiers that are part of a real-time system. Experimental results on a set of echocardiograms collected in routine clinical practice at one hospital demonstrate the potential of the proposed approach.