Surface Extraction from Volumetric Images Using Deformable Meshes: A Comparative Study
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Genetic-Greedy Hybrid Approach for Topological Active Nets Optimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Automatic Topological Active Net Division in a Genetic-Greedy Hybrid Approach
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Genetic approaches for topological active nets optimization
Pattern Recognition
Evolutionary multiobjective optimization of Topological Active Nets
Pattern Recognition Letters
Optimization of topological active nets with differential evolution
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Topological active nets optimization using genetic algorithms
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Topological Active Models optimization with Differential Evolution
Expert Systems with Applications: An International Journal
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Applied Soft Computing
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Abstract: Deformable models are by their formulation able to solve surface extraction problem from noisy volumetric image data encountered commonly in medical image analysis. However, this ability is shadowed by the fact that the minimization problem formulated is difficult to solve globally. Constrained global solutions are needed, if the amount of noise is substantial. This paper presents a new optimization strategy for deformable surface meshes based on real coded genetic algorithms. Real coded genetic algorithms are favored over binary coded ones because they can more efficiently be adapted to the particular problem domain. The experiments with synthetic images are performed. These demonstrate that the applied deformable model is able extract a surface from a noisy volumetric image. Also superiority of the proposed approach compared to a greedy minimization with multiple initializations is demonstrated.