Gray Cerebrovascular Image Skeleton Extraction Algorithm Using Level Set Model

The ambiguity and complexity of medical cerebrovascular i mage makes the skeleton gained by conventional skeleton algorithm discontinuous, which is sensitive at the weak edges, with poor robust ness and too many burrs. This paper proposes a cerebrovascular image skeleton extraction algorithm based on Level Set model, using Euclidean distance field and improv ed gradient vector flow to obtain two different energy functions. The first energy function control s the obtain of topological node s for the beginning of skeleton curve . T he second energy function control s the extraction of skeleton surface. This algorithm avoids the locating and classifying of the skeleton connection points which guide the skeleton extraction . B ecause all its parameters are gotten by the analysis and reasoning, no artificial interference is needed.