Applied computational geometry in fundamentals of bio-imaging and analysis

生物成像和分析基础中的应用计算几何

• 批准号: 341716-2007
• 负责人: Morrison, Jason
• 金额: $ 1.02万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=361370
• 关键词: Applied; computational; geometry; fundamentals; bio

As a computer scientist working in the discipline of biosystems engineering, my long term research program focuses on the application of computational geometry to the fundamental physical sciences of imaging and analyzing food and biological materials.  In the analysis of materials, traditional spectroscopic techniques provide the spectral signature of a chemical sample, modern spectroscopic imaging provides entire spectra at each pixel of a sample.  While each technology and manufacturer has equipment with differing spatial resolutions many devices offer hundreds of thousand of pixels per sample.  Similarly spectral resolution varies from the multispectral (tens of spectra per pixel) to hyperspectral (hundreds to tens of thousands per pixel).Computational geometry typically analyzes structure within a geometric abstraction of a data set.  As an example of geometric structure, a particular substance's spectra can be considered geometric data by considering individual peaks in the spectra and their magnitude as separate mathematical values (features) of that data point.  Sets of substances (i.e., data points) can be analyzed to find clusters/structures of chemicals with similar chemical bonds (e.g. chemical sub-groups).   The long term goal of the proposed research is to establish computational geometry as a key component in the analysis and acquisition of spectral signals and their use in the determination of quality of food and biological materials.  My short term goals focus on two areas:  the use of nearest neighbour methods to perform cluster and classification analysis of spectral data and the use of polygonal simplification and estimation techniques to improve data acquisition in nuclear magnetic resonance spectroscopy.

作为生物系统工程领域的计算机科学家，我的长期研究项目集中在计算几何在食品和生物材料成像和分析的基础物理科学中的应用。在材料分析中，传统的光谱技术提供化学样品的光谱特征，现代光谱成像在样品的每个像素处提供完整的光谱。虽然每个技术和制造商具有具有不同空间分辨率的设备，但许多设备为每个样品提供数十万个像素。类似地，光谱分辨率因多光谱（每像素数十个光谱）到高光谱（每像素数百到数万个）。计算几何通常分析数据集的几何抽象内的结构。作为几何结构的示例，通过将光谱中的各个峰及其幅度视为单独的数学值，可以将特定物质的光谱视为几何数据物质的集合（即，数据点），以找到具有相似化学键的化学品的簇/结构（例如，化学子群）。 该研究的长期目标是将计算几何学作为光谱信号分析和获取的关键组成部分，并将其用于确定食品和生物材料的质量。我的短期目标集中在两个领域：使用最近邻方法对光谱数据进行聚类和分类分析，并使用多边形简化和估计技术，改进核磁共振光谱学中数据采集。