Authors:
Cristina V. Sierra
;
Jorge Novo
;
José Santos
and
Manuel G. Penedo
Affiliation:
University of A Coruña, Spain
Keyword(s):
Image Segmentation, Topological Active Nets, Differential Evolution, Artificial Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
;
Vision and Perception
Abstract:
We developed a novel segmentation method using deformable models. As deformable model we used Topological Active Nets, model which integrates features of region-based and boundary-based segmentation techniques. The deformation through time is defined by an Artificial Neural Network (ANN) that learns to move each node of the segmentation model based on its energy surrounding. The ANN is applied to each of the nodes and in different temporal steps until the final segmentation is obtained. The ANN training is obtained by simulated evolution, using differential evolution to automatically obtain the ANN that provides the emergent segmentation. The new proposal was tested in different artificial and real images, showing the capabilities of the methodology.