Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of ultrasonic images using support vector machines
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Segmentation of Meningiomas and Low Grade Gliomas in MRI
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Multiparent recombination in evolutionary computing
Advances in evolutionary computing
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Tuning range image segmentation by genetic algorithm
EURASIP Journal on Applied Signal Processing
SSIAI '06 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Image Processing
Hi-index | 0.00 |
This paper presents a novel adaptive vision system for accurate segmentation of tissue structures in echographic medical images. The proposed vision system incorporates a level-set deformable model based on a modified Mumford-Shah functional, which is estimated over sparse foreground and background regions in the image. This functional is designed so that it copes with the intensity inhomogeneity that characterizes echographic medical images. Moreover, a parameter tuning mechanism has been considered for the adaptation of the deformable model parameters. Experiments were conducted over a range of echographic images displaying abnormal structures of the breast and of the thyroid gland. The results show that the proposed adaptive vision system stands as an efficient, effective and nearly objective tool for segmentation of echographic images.