A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Digital Image Processing
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Applied Signal Processing
Review: A comparative study of deformable contour methods on medical image segmentation
Image and Vision Computing
Segmentation of CT Brain Images Using K-Means and EM Clustering
CGIV '08 Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation
Genetic approaches for topological active nets optimization
Pattern Recognition
Localisation of the optic disc by means of GA-optimised Topological Active Nets
Image and Vision Computing
Evolutionary multiobjective optimization of Topological Active Nets
Pattern Recognition Letters
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Active Contour External Force Using Vector Field Convolution for Image Segmentation
IEEE Transactions on Image Processing
A Real-Time Algorithm for the Approximation of Level-Set-Based Curve Evolution
IEEE Transactions on Image Processing
Topological Active Models optimization with Differential Evolution
Expert Systems with Applications: An International Journal
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Topological Active Nets are promising parametric deformable models that integrate features of region-based and boundary-based segmentation techniques. Problems associated with the complexity of the model, however, have limited their utility. This paper introduces an extension of the model, defining a new behavior for changing its topology, as well as a novel external force definition and a new local search optimization procedure. In particular, we propose a new automatic pre-processing phase, a new external energy term based on the Extended Vector Field Convolution, node movement constraints to avoid crossing links, and different procedures to perform link cuts and hole detection. Moreover, the new local search procedure also incorporates heuristics to correct the position of eventually misplaced nodes. The proposal has been tested on 18 synthetic images which present different segmentation difficulties along with 3 real medical images. Its performance has been compared with that of the original Topological Active Net optimization approach along with both state-of-the-art parametric and geometric active contours: two snakes (based on Gradient Vector Flow and Vector Field Convolution), and two level sets (Chan and Vese, and Geodesic Active Contour). Our new method outperforms all the others for the given image sets, in terms of segmentation accuracy measured by using four standard segmentation metrics.