Morphological methods in 3D image fusion and sequence analysis in medical imaging

医学成像中 3D 图像融合和序列分析中的形态学方法

• 批准号: 5330078
• 负责人: Professor Dr. Martin Rumpf
• 金额: --
• 依托单位: Department of Neurosurgery
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2001
• 资助国家: 德国
• 起止时间: 2000-12-31 至 2008-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5330078?language=en
• 关键词: Morphological; methods; 3D; image; fusion

Neurosurgery planning requires robust and valid segmentation and classification results and an analysis of the temporal change of brain structures. This can only be achieved, if multi-modal 3D datasets (i.e. data from different medical image acquisition devices) can be matched to each other and corresponding structures in 3D image sequences can be correlatd via the computation of a appropriate deformation. Especially, the calculation of deformations inbetween different frames of a medical image sequence allows a detailed and spatially resolved study of diseases and the growth and change of structures such as tumors. Instead of matching image intensity we consider image morphologies only and try to match them between images of different modality or different time steps from a sequence of images. The morphologies are characterized uniquely by the entity of level sets and their Gauss maps respectively. Our model will be based on a cost functional to be minimized which splits into a matching cost functional measuring the deformation of Gauss maps and a regularization cost functional ensuring well-posedness of this inverse problem. The latter functional allows for locally large variations of the deformation across level sets and edges on level sets via an anisotropic quadratic form which depends on the shape operator of the level sets. Several generalization of this approach are proposed. Based on the matching results we will improve automatic segmentation methods, which now can rely on multiple image modalities and are helpful for correlations within a time sequence. We will apply the developed tools to typical medical images and extensively validate the obtained results based on clinical expertise

神经外科规划需要稳健且有效的分割和分类结果以及对大脑结构的时间变化的分析。只有多模态 3D 数据集（即来自不同医学图像采集设备的数据）可以相互匹配，并且 3D 图像序列中的相应结构可以通过计算适当的变形进行关联，才能实现这一点。特别是，医学图像序列的不同帧之间的变形的计算允许对疾病以及肿瘤等结构的生长和变化进行详细的空间解析研究。我们不考虑图像强度匹配，只考虑图像形态，并尝试在图像序列中不同模态或不同时间步长的图像之间进行匹配。形态学的独特特征在于水平集实体及其高斯图。我们的模型将基于要最小化的成本函数，该函数分为测量高斯图变形的匹配成本函数和确保该逆问题适定性的正则化成本函数。后一个函数通过各向异性二次形式允许跨水平集和水平集上的边缘的变形的局部大变化，该二次形式取决于水平集的形状算子。提出了该方法的几种推广。根据匹配结果，我们将改进自动分割方法，该方法现在可以依赖于多种图像模态，并且有助于时间序列内的相关性。我们将把开发的工具应用于典型的医学图像，并根据临床专业知识广泛验证所获得的结果