Computer graphics and geometric modeling using Beta-splines
Computer graphics and geometric modeling using Beta-splines
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smoothing and matching of 3-D space curves
International Journal of Computer Vision
Active shape models—their training and application
Computer Vision and Image Understanding
The nature of statistical learning theory
The nature of statistical learning theory
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Quantitative analysis of cardiac function
Handbook of medical imaging
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Intelligent Systems
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Edge detection in ventriculograms using support vector machine classifiers and deformable models
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A knowledge-based boundary delineation system for contrast ventriculograms
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Statistical deformable model-based segmentation of image motion
IEEE Transactions on Image Processing
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
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In this research a two step method for left ventricle segmentation based on landmark detection and evolutionary snakes is reported. The proposed approach is applied to human heart angiograms. Several anatomical landmarks located on the left ventricle are obtained using support vector machines. The training stage is performed based on a set of windows of size 31x31 including landmarks and non-landmarks pixel patterns. The support vector machines use a radial basis function kernel and the structural risk minimization principle as the inference rule. During the training stage, no false positives are obtained and during the detection stage a 97.94% of recognition is attained. The estimated landmark location is used for constructing an approximate myocardial border. This contour is a deformable model that is optimized using a real-coded genetic algorithm. A validation is performed by comparing the estimated contours with respect to contours manually traced by two cardiologists. From this validation stage the maximum of the average contour error considering 6 angiographic sequences (a total of 178 images) is 4.93%.