基于水平集方法的三维磁共振脑血管图像分割

Tomasz Wozniak, M. Strzelecki
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引用次数: 8

摘要

脑血管系统的定量建模对血管病变的诊断以及手术治疗计划都很重要。磁共振血管造影(MRA)提供可靠的血管树形结构及其组织的可视化。MRA中血管分割的准确性是建立模型的重要步骤;其精度影响所得模型的质量。本文提出了三种基于水平集的分割方法,包括一种代表原作者贡献的方法。将这些方法与分析图像的血管性函数估计相结合。所提出的算法分别应用于人工和真实脑三维磁共振图像。分析结果和讨论也包括在内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation of 3D magnetic resonance brain vessel images based on level set approaches
Quantitative modeling of brain vasculature is important for diagnosis of vessel pathologies as well as for surgery treatment planning. Magnetic resonance angiography (MRA) provides reliable visualization of vessel tree structure and its organization. Accuracy of vessel segmentation from MRA is an important step in model building; its accuracy influences obtained model quality. This paper presents three level set based segmentation approaches, including one that represents original authors contribution. These methods are combined together with vesselness function estimated for analyzed images. Presented algorithms were applied both for artificial and real brain 3D MR images. Analysis results along with discussion are also included.
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