IMAGE PROCESSING AND GEOMETRICAL MODELING

图像处理和几何建模

• 批准号: 7723093
• 负责人: ROSS T WHITAKER
• 金额: $ 18.47万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723093
• 关键词: Address; Adopted; Algorithms; Anatomic Models; Anatomy; Architecture; Area; Arts; Atlases; Automobile Driving; Biological; Cardiac; Cellular Structures; Collaborations; Collection; Communities; Complex; Computer Retrieval of Information on Scientific Projects Database; Data; Data Set; Development; Drug Formulations; Electroencephalography; Electron Microscope; Engineering; Funding; Generations; Goals; Grant; Gray unit of radiation dose; Head; Image; Image Analysis; Imagery; Institutes; Institution; Investigation; Life; Light; Magnetic Resonance Imaging; Manuals; Medical; Methods; Microscopic; Mind; Modeling; Monitor; Morphologic artifacts; Not Defined; Process; Range; Relative (related person); Research; Research Personnel; Resources; Risk; Shapes; Signal Transduction; Source; Surface; System; Techniques; Technology; Testing; United States National Institutes of Health; Utah; Work; base; biomedical scientist; computerized data processing; digital; fundamental research; image processing; method development; models and simulation; n-dimensional; prevent; reconstruction; research and development; scientific computing; shared memory; spatial relationship; speech recognition; success; three-dimensional modeling; tomography; two-dimensional; usability

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This technical subproject deals with the problem of processing or analyzing scientific and medical data. The state of the art for data processing varies, depending on the type of data and goals of the application. For instance, the field of signal processing, which we use here to refer to the analysis of one-dimensional functions or waveforms, is somewhat mature. Important research topics remain in signal processing, but there are a variety of well-known, effective, general algorithms for filtering and classifying signals. Specific applications abound, from speech recognition to cardiac monitoring. Images are multidimensional signals, that is, functions defined on two-dimensional, three-dimensional, or higher- dimensional domains. The field of image processing is younger, and it has proven to be more challenging. The important aspects of images are encoded not only in their grey-scale (or spectral) values, but in the shapes that they describe. For instance, when considering MRI data, the cortex is defined not simply by its intensities but also by its shape and its spatial relationships to other anatomy. Researchers are developing effective technologies for image analysis, but the techniques are far from mature and have not yet been widely adopted within the community of biomedical scientists. Geometry refers to collections of points that are organized in space to form manifolds. Unlike signals and images, geometric objects (or manifolds) are not necessarily functions. The space in which these manifolds live could be two- dimensional, three-dimensional, or n-dimensional (where n > 3). Furthermore these points can be organized in different ways to form curves, surfaces, hypersurfaces, or more complex objects that consist of combinations of these other objects. Geometry processing for digital surfaces is a relatively young field, and a great many theoretical and practical questions remain. For instance, the problem of representing digital surfaces is itself quite complex, and researchers are still investigating a variety of possibilities including point sets, meshes, polynomial patches, and implicit surfaces. Geometry processing, includes both the analysis of geometric objects and the generation of geometric models from scientific data. This project addresses the processing of images and geometry for biomedical applications. We will consider signal processing as a somewhat mature technology, and we will include it in our applications by integrating with other toolkits and relying on the work of our collaborators. Our research and development aims in image and geometry processing will reflect the relative maturity of each of these technologies, their current availability to biomedical researchers, the expertise of the Scientific Computing and Imaging Institute and our collaborators, and the specific needs of driving applications. The fields of image and geometry processing are vast, and the data processing needs of various biological researchers are extensive. The Center's resource associated with this technical domain are relatively smallif we compare them to either the field as a whole or even to other ongoing projects and centers that focus more exclusively on image analysis (for instance). With this in mind, we have adopted, for this core, a strategy of leveraging ongoing research in image and geometry processing, at Utah and elsewhere, and extending this work to address the specific roadblocks that prevent our collaborators from taking full advantage of state-of-the-art technologies. In light of this, we have focused the aims to address primarily issues of usability and scalability. Addressing these issues will entail some fundamental research, but it will also entail a tight integration with other technical cores in this proposal and significant collaborations with other teams working in biological areas. The specific aims of this project are divided into two groups: research goals and development goals. The research goals are those for which we expect there will be some fundamental work or extensive engineering at the algorithm level. It also implies some development of methods that have not been tested for the associated applicationsimplying some risk or some potential reworking of algorithms. The development goals refer to the development of new implementations of known algorithms and the integration of algorithms into new systems. However, it also includes the development of faster implementations on specialized computing architectures and includes, in some cases, the investigation of parallel algorithms for which there is a high likelihood of success. Research Goals for Image Processing: (1) Robust Filtering Methods: The development of new, more general methods for image filtering that can be more easily applied across a wide range of applications with less tuning of free parameters. (2) Segmentation of Incomplete and Noisy Tomographic Datasets: Methods for automatic and semiautomatic segmentation of electron microscope tomography datasetsrobust to reconstruction artifacts. (3) User-Interactive Segmentation: The refinement of segmentation algorithms to interact effectively with two- dimensional and three-dimensional visualization capabilities. Research Goals for Geometry Processing: (1) Statistical Shape Characterization: Formulations for the statistical characterization of shape deformations with applicability to large, articulated anatomical models. (2) Stochastic Model Generation: The data-driven generation of mesoscale models for simulation of aggregate effects of microscopic (e.g., cellular) structures. Development Goals for Image Processing: (1) Parallel Implementations: Parallel (distributed and shared memory) implementations of iterative algorithms for filtering and registration. (2) Atlas-Based Head Segmentation: Integrate atlas-based head segmentation into model generation and EEG source localization pipeline. . (3) Active Shape Models (ASMs): Extend the ITK implementation of adaptive shape models to include: support for three- dimensional models, integration with semi-automated segmentation methods (e.g., watersheds and level-set models), and hierarchical articulated models. Development Goals for Geometry Processing: (1) Mesh Generation: Two-dimensional and three-dimensional mesh generation, including tetrahedral and hexahedral meshes that incorporate application-specific geometric constraints. (2) Manual Mesh Editing: User-guided manipulation of mesh geometries and topologies. (3) Point-Based Registration: Integration of point/curve/surface-based registration algorithms into the inverse-problems workflow.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 该技术子项目涉及处理或分析科学和医学数据的问题。状态 用于数据处理的ART取决于应用程序的数据类型和目标。例如，信号字段 我们在这里使用的处理是指对一维函数或波形的分析，这有些成熟。 重要的研究主题仍在信号处理中，但是有许多知名，有效的，一般的 用于过滤和分类信号的算法。从语音识别到心脏的特定应用比比皆是 监视。 图像是多维信号，即在二维，三维或更高的函数 维域。图像处理领域还年轻，事实证明它更具挑战性。重要 图像的各个方面不仅在其灰度（或频谱）值中编码，而且还以它们描述的形状进行编码。为了 实例，考虑MRI数据时，皮质不仅是由其强度定义的，而且还由其形状及其形状定义 与其他解剖结构的空间关系。研究人员正在开发有效的图像分析技术，但是 在生物医学科学家社区中，技术远非成熟，尚未被广泛采用。 几何是指在空间中组织以形成歧管的点的集合。与信号和图像不同， 几何对象（或歧管）不一定是功能。这些流形的空间可能是两个 尺寸，三维或n维（其中n> 3）。此外，这些点可以在不同的 形成曲线，表面，超曲面或更复杂的对象的方法，这些对象由其他组合组合 对象。数字表面的几何处理是一个相对年轻的领域，而且许多理论和实用 问题仍然存在。例如，代表数字表面的问题本身很复杂，研究人员是 仍在研究各种可能性，包括点集，网格，多项式斑块和隐式表面。 几何处理，包括几何对象的分析和从 科学数据。该项目解决了生物医学应用的图像和几何形状的处理。我们会考虑的 信号处理是一种有点成熟的技术，我们将通过与其他集成来将其包括在应用程序中 工具包并依靠我们的合作者的工作。我们的研发目的是图像和几何形状 处理将反映每种技术的相对成熟度，它们当前的生物医学可用性 研究人员，科学计算与成像研究所的专业知识以及我们的合作者以及具体的 驾驶应用的需求。 图像和几何处理领域很广泛，各种生物学研究人员的数据处理需求是 广泛的。如果我们将它们与该中心与该技术领域相关的中心资源相对较小，如果我们将它们与 整个领域，甚至针对其他正在进行的项目和中心，这些项目更专注于图像分析 （例如）。考虑到这一点，我们已经采用了这一核心，一种利用图像中正在进行的研究和 几何处理，在犹他州和其他地方，并扩展了这项工作以解决防止特定的障碍 我们的合作者充分利用了最先进的技术。鉴于此，我们集中了目标 主要解决可用性和可扩展性问题。解决这些问题将需要一些基本研究，但 它还将在此提案中与其他技术核心进行紧密整合，并与其他重要合作 在生物区工作的团队。 该项目的具体目标分为两组：研究目标和发展目标。研究目标 我们希望在算法级别上会有一些基本工作或广泛的工程。它 这也意味着一些未针对相关应用程序进行测试的方法的开发 风险或算法的一些潜在重新加工。发展目标是指新的发展 已知算法的实现以及将算法集成到新系统中。但是，它还包括 开发更快的专业计算体系结构实施速度，在某些情况下包括 对平行算法的调查很有可能成功。 图像处理的研究目标： （1）强大的过滤方法：开发新的，更通用的图像过滤方法，可以是更多 轻松地在广泛的应用程序上应用，自由参数调整较少。 （2）分割不完整和嘈杂的断层扫描数据集：自动和半自动的方法 电子显微镜断层扫描数据集的分割对重建工件的鲁棒性。 （3）用户相互作用分割：分割算法的细化，以有效与两种相互作用 尺寸和三维可视化功能。 几何处理的研究目标： （1）统计形状表征：使用 适用于大型，表达的解剖模型。 （2）随机模型生成：数据驱动的中尺度模型的生成用于模拟的骨料效应 显微镜（例如细胞）结构。 图像处理的开发目标： （1）并行实现：迭代算法的并行（分布式和共享内存）实现 过滤和注册。 （2）基于地图集的头部分割：将基于ATLA的头部分割整合到模型生成和EEG源 本地化管道。 。 （3）主动形状模型（ASM）：扩展自适应形状模型的ITK实现，包括：支持三 - 维度模型，与半自动分割方法（例如，流域和级别模型）的集成 和分层表达模型。 几何处理的发展目标： （1）网格生成：二维和三维网格一代，包括四面体和六面体 包含特定应用的几何约束的网格。 （2）手动网格编辑：用户引导的网状几何和拓扑操作。 （3）基于点的注册：基于点/曲线/基于表面的注册算法的集成到逆问题中 工作流程。