Evolutionary and Discrete Snake-Balloon Models for Image

• Main Authors: Lin, Yu-Chen, 林育醇
• Other Authors: Henry Horng-Shing Lu
• Format: Others
• Language: zh-TW
• Published: 1997
• Online Access: http://ndltd.ncl.edu.tw/handle/82239884552370799980

碩士 === 國立交通大學 === 統計學系 === 85 === Active contour model, also known by the nick name, snake, is a powerful tool for image segmentation. Since it provides continuous boundaries for regions of interest, a snake model has the advantage over edge-detecting approaches for segmentation in that no edge linking would be required.Powerful as it is, the snake model suffers several cons, e.g., corner- fitting problem, struck by noise pixels, etc., which limits itsusage, especially, on a noisy image. With the ultimate goal to accomplish segmentation on a noisy image, thisstudy provides two novel snake models to solve the corner-fitting and noiseproblems. One is evolutionary snake-balloon model and the other is discretesnake-balloon model. The evolutionary snake- balloon model and the other is thediscrete snake-balloon model. The evolutionary model is an effective approachto catching corner points while the snake is moving toward the desired boundaries. The discrete model, on the other hand, circumvents the noisy problems by incorporating our recently developed early vision model.Instead of deforming on the image of interest pixel-by-pixel, the snakesearches for actual boundaries on the distance map discretely, which not onlyminimizes the noise effect but also speeds up convergence significantly. In addition to evolutionary and discrete models, a new adaptive methodologyis proposed in this thesis to determine the weighting factor of each term in the snake energy functions such that the snake may cope with local minima more effectively. The weighting factorsare adjusted adaptively to balance all energy forces according to preset ratio among these forces to provide appropriate forces for deformation and stopping on the actual boundary. Experiments have been carried out to verify the proposed evolutionaryand discrete snake-balloon models. For the evolutionary snake-balloon model,various phantoms filled with gaussian random noises are used to simulate noisy images. By controlling the means and standard deviations of the gaussiandistributions, the noise-resisting capability of the evolutionary model for different signal- to-ratio has been examined. For the discrete snake-balloon odel, clinical ultrasound nimages are used to demonstrate its superiority over the conventional snake models. In most cases, the discrete snakes converge in less than ten steps which is substantially faster than the conventional ones.