IMAGE PROCESSING AND GEOMETRICAL MODELING

图像处理和几何建模

• 批准号: 7957215
• 负责人: ROSS T WHITAKER
• 金额: $ 13.53万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7957215
• 关键词: Address; Adopted; Algorithms; Anatomic Models; Anatomy; Architecture; Area; Arts; Atlases; Automobile Driving; Biological; Biomedical Computing; 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; Process; 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; imaging modality; 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 and 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 proven to be 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 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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 该技术子项目涉及处理和分析科学和医学数据的问题。数据处理的最新技术各不相同，具体取决于数据类型和应用程序的目标。例如，信号处理领域，我们在这里指的是一维函数或波形的分析，已经有些成熟了。 重要的研究主题仍然是信号处理，但有多种众所周知的、有效的、通用的算法用于信号的过滤和分类。从语音识别到心脏监测，具体应用比比皆是。 图像是多维信号，即在二维、三维或更高维域上定义的函数。图像处理领域比较年轻，并且被证明具有挑战性。图像的重要方面不仅以灰度（或光谱）值编码，而且以它们描述的形状编码。例如，在考虑 MRI 数据时，皮层不仅由其强度来定义，还由其形状及其与其他解剖结构的空间关系来定义。研究人员正在开发有效的图像分析技术，但这些技术还远未成熟，尚未在生物医学科学家群体中广泛采用。 几何是指在空间中组织形成流形的点的集合。与信号和图像不同，几何对象（或流形）不一定是函数。这些流形所在的空间可以是二维、三维或 n 维（其中 n > 3）。此外，这些点可以以不同的方式组织以形成曲线、曲面、超曲面或更复杂的对象（由这些其他对象的组合组成）。数字表面的几何处理是一个相对年轻的领域，仍然存在许多理论和实践问题。例如，表示数字表面的问题本身就相当复杂，研究人员仍在研究各种可能性，包括点集、网格、多项式补丁和隐式表面。 几何处理，包括几何对象的分析和从科学数据生成几何模型。该项目致力于生物医学应用的图像和几何处理。我们将把信号处理视为一项较为成熟的技术，并通过与其他工具包集成并依赖我们合作者的工作，将其纳入我们的应用程序中。我们在图像和几何处理方面的研发目标将反映这些技术的相对成熟度、生物医学研究人员当前的可用性、科学计算和成像研究所和我们合作者的专业知识，以及驱动应用的具体需求。 图像和几何处理领域广阔，各种生物研究人员的数据处理需求也广泛。如果我们将其与整个领域甚至其他正在进行的项目和更专注于图像分析（例如）的中心进行比较，则该中心与该技术领域相关的资源相对较小。考虑到这一点，我们为此核心采取了一项策略，利用犹他州和其他地方正在进行的图像和几何处理研究，并扩展这项工作以解决阻止我们的合作者充分利用国家的具体障碍。最先进的技术。有鉴于此，我们的目标主要是解决可用性和可扩展性问题。解决这些问题需要一些基础研究，但也需要与本提案中的其他技术核心紧密结合，并与生物领域的其他团队进行重大合作。 该项目的具体目标分为两组：研究目标和开发目标。研究目标是我们期望在算法层面进行一些基础工作或广泛工程的目标。它还意味着尚未针对相关应用程序进行测试的某些方法的开发，这意味着存在某些风险或算法的某些潜在返工。开发目标是指开发已知算法的新实现以及将算法集成到新系统中。然而，它还包括在专用计算架构上开发更快的实现，并在某些情况下包括对成功可能性很高的并行算法的研究。 图像处理的研究目标： (1) 鲁棒过滤方法：开发新的、更通用的图像过滤方法，可以更轻松地应用于各种应用，而无需调整自由参数。 (2)不完整和嘈杂的断层摄影数据集的分割：对重建伪影稳健的电子显微镜断层摄影数据集的自动和半自动分割方法。 (3) 用户交互式分割：细化分割算法，以与二维和三维可视化功能有效交互。 几何处理的研究目标： (1) 统计形状表征：形状变形统计表征的公式，适用于大型、铰接的解剖模型。 (2)随机模型生成：数据驱动的介观模型生成，用于模拟微观（例如细胞）结构的聚合效应。 图像处理的发展目标： (1)并行实现：用于过滤和注册的迭代算法的并行（分布式和共享内存）实现。 (2)基于图集的头部分割：将基于图集的头部分割集成到模型生成和脑电图源定位管道中。 (3) 主动形状模型 (ASM)：扩展自适应形状模型的 ITK 实现，包括：支持三维模型、与半自动分割方法（例如分水岭和水平集模型）集成以及分层铰接模型。 几何处理的发展目标： (1) 网格生成：二维和三维网格生成，包括结合特定应用几何约束的四面体和六面体网格。 (2) 手动网格编辑：用户引导的网格几何形状和拓扑操作。 (3) 基于点的配准：将基于点/曲线/表面的配准算法集成到反问题工作流程中。