Snake s original intention was to carry out image segmentation, but it was too sensitive to initial position and can not deal with the issue of topology change. Initial position The sensitivity of home can be used to overcome the genetic algorithm because it is a global optimization algorithm, and have good numerical stability. In order to more accurately map Like segmentation, this paper presents a genetic algorithm based on dual-T-Snake model for image segmentation method, it will double-T-Snake model solution found as a genetic algorithm Cable space, which not only inherited the T-Snake model the ability to change the topology, but also speed up the convergence rate of genetic algorithm. It uses genetic algorithms as a result of the overall excellent Of performance, to overcome the local minimum of Snake contour deficiencies, which can be more precise on the target partition. Will be applied to MRI images of left ventricle Segmentation, and achieved good results.

TSnake.pdf

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