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

神经外科计划需要强大的有效分割和分类结果，并分析大脑结构的时间变化。只有可以将多模式3D数据集（即来自不同医疗图像采集设备的数据）相互匹配，并且可以通过计算适当的变形来相关。尤其是，医学图像序列不同框架之间的变形的计算允许对疾病进行详细且空间解决的研究以及肿瘤等结构的生长和变化。我们仅考虑图像形态，而不是匹配图像强度，并尝试在不同模态的图像或一系列图像序列的不同时间步骤之间匹配它们。形态的特征是分别由水平集及其高斯图的实体来表征。我们的模型将基于一个成本功能，以最小化，该功能将其分成匹配的成本功能，以测量高斯图的变形和正则化成本功能，以确保此反问题的适应性良好。后者的功能允许通过各向异性二次形式在级别集合上跨级别集和边缘的变形的局部变化，该二次形式取决于级别集合的形状运算符。提出了这种方法的几种概括。基于匹配结果，我们将改善自动分割方法，现在可以依靠多种图像模式，并且有助于在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疗图像，并根据临床专业知识广泛验证获得的结果