A neural network-based stochastic active contour model (NNS-SNAKE) for contour finding of distinct features

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.