Sum and Difference Histograms for Texture Classification
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Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Multiresolution elastic matching
Computer Vision, Graphics, and Image Processing
The appeal of parallel distributed processing
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Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A neural network architecture for understanding discrete three-dimensional scenes in medical imaging
Computers and Biomedical Research
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
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“Brownian strings”: segmenting images with stochastically deformable contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally constrained deformable models for 3D object reconstruction
Signal Processing - Special issue on deformable models and techniques for image and signal processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
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Numerical Optimization of Computer Models
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Multi-Objective Optimization Using Evolutionary Algorithms
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Genetic Learning for Adaptive Image Segmentation
Genetic Learning for Adaptive Image Segmentation
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Genetic Snakes for Medical Images Segmentation
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
Unsupervised Markovian Segmentation Of Sonar Images
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Pattern Classification (2nd Edition)
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Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Segmentation of medical images using a genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series)
An improved algorithm for clustering gene expression data
Bioinformatics
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
Introduction to the special section on computationalintelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Natural and remote sensing image segmentation using memetic computing
IEEE Computational Intelligence Magazine
IR image segmentation using GA-MRF with neighborhood labels coding
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Index-based genetic algorithm for continuous optimization problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A new algorithm for image segmentation via watershed transformation
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Optimization of parameter settings for genetic algorithms in music segmentation
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
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Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.