Adaptive Image Segmentation With Distributed Behavior-Based Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Deformable Organisms for Automatic Medical Image Analysis
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
IEEE Transactions on Image Processing
Editorial: From medical imaging to medical informatics
Computer Methods and Programs in Biomedicine
Image segmentation by automatic histogram thresholding
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
An Approach to Identify Optic Disc in Human Retinal Images Using Ant Colony Optimization Method
Journal of Medical Systems
An efficient ant-based edge detector
Transactions on computational collective intelligence I
Swarm intelligence for medical volume segmentation: the contribution of self-reproduction
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
A stochastic gravitational approach to feature based color image segmentation
Engineering Applications of Artificial Intelligence
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The success of image analysis depends heavily upon accurate image segmentation algorithms. This paper presents a novel segmentation algorithm based on artificial ant colonies (AC). Recent studies show that the self-organization of ants is similar to neurons in the human brain in many respects. Therefore, it has been used successfully for understanding biological systems. It is also widely used in many applications in robotics, computer graphics, etc. Considering the features of artificial ant colonies, we present an extended model for image segmentation. In our model, each ant can memorize a reference object, which will be refreshed when it finds a new target. A fuzzy connectedness measure is adopted to evaluate the similarity between target and the reference object. The behavior of an ant is affected by the neighbors and the cooperation between ants is performed by exchanging information through pheromone updating. Experimental results show that the new algorithm can preserve the detail of the object and is also insensitive to noise.