The recent researches have improved that local adaptive segmentation is particularly more attractive than the fully automatic segmentation when the property of the object’s localboundary is not similar. For improve the segmentation speed

This article describes a novel approach to the self2adaptable segmentation of irregular objects in an image. The algorithm is based on Moore Penrose operator. With adaptable energy function parameters

the Greedy Snake is attracted to boundaries by use of a direct feedback mechanism (Greedy Snake). To avoid undesirable localminima

every energy function’s weight is adaptable according to the test point’s property nearby

and a suitable local convergent algorithm is proposed which enables snakes to converge to target boundary points. Through computation simulation

the paper proves that the proposed approach is capable of inheriting the characters of the Greedy Snake algorithm

through adjusting the weight vector of the energy function

the newmodel changes the local character of the Snake

andmake it approach to the aim object’s boundary automatically. When applying the newmodel and traditionalmethod to extract contours from various images

the new greedy snakemodel performs better than related snakes.