Blood vessel segmentation method based on geodesic distance map and process function equation

Abstract

The invention discloses a blood vessel segmentation method based on a geodesic distance map and a process function equation, and the method comprises the steps: enabling the front propagation based on the geodesic distance map to be expanded to the application of an asymmetric Finsler measurement condition, considering the anisotropy and asymmetry of a boundary, strengthening the description of a measurement function, and setting a freezing criterion of the front propagation; the situation of leakage in the anterior propagation process is effectively prevented, smooth anterior propagation is guaranteed, the target object can be found more accurately and quickly, and accurate segmentation of the blood vessel is achieved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a blood vessel segmentation method based on a geodesic distance map and an eigenfunction equation. Background technique [0002] Since the original level set framework (references: Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988 )) Since it was proposed, the front propagation model has been greatly developed in the application of image segmentation and boundary detection. Due to its strong mathematical background, the front propagation model can be widely applied in the field of computer vision, adaptively processing segmentation and merging in images, etc. Malladi and Sethian proposed a frontier propagation model based on geodesic distance (References: Malladi, R., Sethian, J.A.: A real-time algorithm for medical shape recovery. In: Proceeding of ICCV, pp.304–310( 1...

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The main difficulty of this model lies in choosing an appropriate Euclidean curve length threshold

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