关键词: 类间方差, 磁共振造影, 脑血管, 标准差, 分割, 重建

Abstract: Magnetic resonance angiography (MRA) is the primary choice for vascular imaging in clinic. The precision of vessel segmentation is the key of angiography 3D visualization, diagnosis, treatment and evaluation of vascular disease. By studying the characteristics of the cerebral vascular anatomical structure and MRA images, this paper proposes a cerebrovascular segmentation method based on local Otsu. At first, the original image is preprocessed with anisotropic diffusion filtering. Then, the optimal size of the sub-image is determined by calculating the change rate of the image internal standard deviation between two images of different sub-block sizes. The image is divided into some sub-images of this optimal size. Next, all the sub-images are segmented using the Otsu algorithm and merged to the final segmentation result. The segmentation results are reconstructed by visualization toolkit (VTK) and projected to two-dimensional images for quantitative evaluation. The experimental results show that the method can compensate for the disadvantage of Otsu algorithm which loses the cerebral vascular details, and obtain the better segmentation and reconstruction results.

Key words: between-class variance, magnetic resonance imaging, cerebrovascular, standard deviation, segmentation, reconstruction