Journal of VLSI Signal Processing Systems - special issue on applications of neural networks in biomedical image processing
Deformable Contour Method: A Constrained Optimization Approach
International Journal of Computer Vision
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized fuzzy c-means method for brain tissue clustering
Pattern Recognition Letters
A cooperative framework for segmentation of MRI brain scans
Artificial Intelligence in Medicine
An adaptive sample count particle filter
Computer Vision and Image Understanding
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Contour finding of distinct features in 2-D/3-D images is essential for image analysis and computer vision. To overcome the potential problems associated with existing contour finding algorithms, we propose a framework, called the neural network-based stochastic active contour model (NNS-SNAKE), which integrates a neural network classifier for systematic knowledge building, an active contour model (also known as the “Snake”) for automated contour finding using energy functions, and the Gibbs sampler to help the snake to find the most probable contour using a stochastic decision mechanism. Successful application of the NNS-SNAKE to extraction of several types of contours on magnetic resonance (MR) images is presented