A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Generalized gradient vector flow external forces for active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
General Object Reconstruction Based on Simplex Meshes
International Journal of Computer Vision
Digital Image Processing
A Robust Snake Implementation; A Dual Active Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global Optimization of Deformable Surface Meshes Based on Genetic Algorithms
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Deformable models with application to human cerebral cortex reconstruction from magnetic resonance images
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Review: A comparative study of deformable contour methods on medical image segmentation
Image and Vision Computing
Fuzzy energy-based active contours
IEEE Transactions on Image Processing
Spline based inhomogeneity correction for 11C-PIB PET segmentation using expectation maximization
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Parametric active contour model by using the honey bee mating optimization
Expert Systems with Applications: An International Journal
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Deformable models are by their formulation able to solve surface extraction problem from noisy volumetric images. This is since they use image independent information, in form of internal energy or internal forces, in addition to image data to achieve the goal. However, it is not a simple task to deform initially given surface meshes to a good representation of the target surface in the presence of noise. Several methods to do this have been proposed and in this study a few recent ones are compared. Basically, we supply an image and an arbitrary but reasonable initialization and examine how well the target surface is captured with different methods for controlling the deformation of the mesh. Experiments with synthetic images as well as medical images are performed and results are reported and discussed. With synthetic images, the quality of results is measured also quantitatively. No optimal method was found, but the properties of different methods in distinct situations were highlighted.