IMAGE PROCESSING AND GEOMETRICAL MODELING

图像处理和几何建模

• 批准号: 8172257
• 负责人: ROSS T WHITAKER
• 金额: $ 17.38万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8172257
• 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; 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; 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 Segmentation; imaging modality; method development; models and simulation; n-dimensional; prevent; research and development; scientific computing; shared memory; spatial relationship; speech recognition; success; three-dimensional modeling; two-dimensional; usability; 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; 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; 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 Segmentation; imaging modality; method development; models and simulation; n-dimensional; prevent; research and development; scientific computing; shared memory; spatial relationship; speech recognition; success; three-dimensional modeling; 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 also 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 are not yet 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 Image Datasets: Methods for automatic and semiautomatic segmentation of image datasets. (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和分析科学和医学数据的问题。数据处理的艺术状态会有所不同，具体取决于应用程序的数据类型和目标。例如，我们在这里使用的信号处理领域是指对一维函数或波形的分析，这是有些成熟的。 信号处理中仍然存在重要的研究主题，但是有多种众所周知的，有效的，一般的算法用于过滤和分类信号。从语音识别到心脏监测的特定应用比比皆是。 图像是多维信号，即在二维，三维或更高尺寸域上定义的函数。图像处理领域年轻，被证明是具有挑战性的。图像的重要方面不仅在其灰度（或频谱）值中编码，而且还以它们描述的形状进行编码。例如，在考虑MRI数据时，皮层不仅是由其强度来定义的，而且还取决于其形状及其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，但是这些技术远非成熟，并且在生物医学科学家社区中尚未广泛采用。 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。这些歧管生存的空间可以是二维，三维或n维（其中n> 3）。此外，可以以不同的方式组织这些点，以形成曲线，表面，超曲面或更复杂的对象，这些对象包括这些其他对象的组合。数字表面的几何处理是相对年轻的领域，并且仍然存在许多理论和实用问题。例如，代表数字表面的问题本身很复杂，研究人员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，包括对几何对象的分析和科学数据的几何模型的产生。该项目解决了生物医学应用的图像和几何形状的处理。我们将信号处理视为一种有点成熟的技术，我们将通过与其他工具包进行集成并依靠合作者的工作来将其包括在应用程序中。我们的研发目的在图像和几何处理中的目的将反映这些技术中每种技术的相对成熟度，当前对生物医学研究人员的可用性，科学计算和成像研究所的专业知识以及我们的合作者的专业知识以及驱动应用的特定需求。 图像和几何处理领域非常广泛，各种生物学研究人员的数据处理需求是广泛的。如果我们将其与整个领域甚至与其他正在进行的项目和中心进行比较，那么该中心与该技术领域相关的资源相对较小，这些项目和中心更专注于图像分析（例如）。考虑到这一点，我们已经采用了这一核心，这是一种利用犹他州和其他地方的图像和几何处理研究的策略，并扩展了这项工作以解决特定的障碍，以防止我们的合作者充分利用最先进的技术。鉴于此，我们集中于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，但这也将与该提案中的其他技术核心进行紧密整合，并与在生物领域工作的其他团队进行重大合作。 该项目的具体目标分为两组：研究目标和发展目标。研究目标是我们希望在算法水平上进行一些基本工作或广泛的工程。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，这意味着某种风险或某些潜在的算法重新加工。开发目标是指已知算法的新实现以及将算法集成到新系统中的开发。但是，它还包括开发更快的专业计算体系结构实现，在某些情况下包括对成功的并行算法的调查。 图像处理的研究目标： （1）强大的过滤方法：开发用于图像过滤的新的，更通用的图像过滤方法，可以更容易地在广泛的应用程序上应用，而自由参数调整较少。 （2）分割不完整和嘈杂的图像数据集：自动和半自动分割图像数据集的方法。 （3）用户相互作用分割：分割算法与二维和三维可视化功能有效相互作用的细化。 几何处理的研究目标： （1）统计形状表征：用于形状变形具有适用性的统计表征的公式，适用于大型，明确的解剖模型。 （2）随机模型生成：用于模拟显微镜（例如细胞）结构骨料效应的中尺度模型的数据驱动的生成。 图像处理的开发目标： （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源定位管道中。 （3）主动形状模型（ASM）：将自适应形状模型的ITK实现扩展到：对三维模型的支持，与半自动分割方法集成（例如，流域和级别模型）以及层次结构的模型。 几何处理的发展目标： （1）网格生成：二维和三维网格生成，包括融合了应用特定的几何约束的四面体和六面体网格。 （2）手动网格编辑：用户引导的网状几何和拓扑操作。 （3）基于点的注册：将点/曲线/基于表面的注册算法的集成到逆问题工作流程中。