Abstract
The precise segmentation of cerebral vessels is essential for detecting cerebral diseases. This work proposed a novel method for automatic extraction of blood vessels. The proposed framework includes three steps: (a) projection from 3D volume to 2D plane: in order to make up the small percentage vessels occupy in each slice and to avoid overlapping between vessels, the volume dataset is projected from different directions; (b) extraction on 2D plane: a new energy model is proposed using phase-field and statistical information which is based on the Allen–Cahn equation with a double well potential and statistical data fitting terms. The segmentation is based on curve evolution. This model is effective in extracting blood vessels with low contrast, multi-branch structure and intensity inhomogeneity from projection images; (c) projecting back from 2D plane to 3D volume: the pixels segmented from previous step will be projected back into the volume dataset, and the corresponding voxels in the volume will be reserved to construct the blood vessels in three-dimensional space. Experimental results illustrate that the performance of the methods is better than existing techniques.
Published Version
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