LANDMARK BASED METHODS FOR BIOMETRIC ANALYSIS OF SHAPE

基于地标的形状生物特征分析方法

• 批准号: 3292481
• 负责人: FRED LEON BOOKSTEIN
• 金额: $ 9.22万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-08-01 至 1995-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3292481
• 关键词: body physical characteristic; congenital oral /facial /cranial defect; face; mathematical model; morphology; physical model; skull; statistics /biometry

This project will continue the development of a class of statistical methods for morphometrics, the quantitative analysis of biological shape. These methods all exploit landmark data, locations of biologically corresponding (homologous) points in all the forms of a series or study. Such data are routinely available from many modes of medical image analysis. Work during the current funding period showed that changes or variability in any configuration of landmarks may be divide into a uniform and a nonuniform part. These are algebraically distinctive pieces of the thin-plate splines that very effectively interpolate deformations from the landmark data. Variation or change of the nonuniform part is calibrated by a certain bending energy that incorporates the geometric scales at which changes at the separate landmarks are coordinated; the uniform part may be considered to have bending energy zero. The adaptations of factor and component analysis that this decomposition supports fully exploit the special structure of landmark data, just as the earlier methods carefully adapted the general linear model to this context. In the new funding period, we will explore several extensions of these feature spaces for studies of shape variability and the statistical methods they support. We will rigorously consider the relation between the two ways in which one can parameterize "blobs" in medical images: as locations or as patterns of gray levels. We will explore models for "growth centers", bulges, and other processes which involve directed divergences among many landmark locations and curves or surfaces at once. We will extend the hypotheses for which rigorous machinery is available to include questions of asymmetry and correlations of form with scalar fields; and we will carefully explore ties between the landmark-based methods and other approaches of image summary, such as "stacks" of successive blurs, under study at other medical imaging research centers. The visualization of the landmark-based biometric machinery will be transferred in extenso to a very powerful graphics computer, the Stellar GS1000, in order to support interactive analysis of three-dimensional data. These new morphometric methods will be applied in demonstration studies from craniofacial growth, craniofacial anomalies, and neurology. The methods should greatly increase the efficiency with which morphological information may be exploited, alone or in combination with other measures, in biomedical studies of the large-scale shapes of organs, their variability, their changes over time, or their covariation with causes or effects.

该项目将继续开发一类统计 形态计量学的方法，生物形状的定量分析。 这些方法都利用了地标数据，生物学上的位置 相应的（同源）点的各种形式或研究形式。 这些数据通常可以从许多医学图像中获得 分析。 在当前资金期间的工作表明变化或 地标的任何配置的可变性都可以分为统一 和一个不均匀的部分。 这些是代数独特的部分 薄板花键非常有效地从 地标数据。 不均部件的变化或变化被校准 一定的弯曲能，其中包含几何尺度 各个地标的变化是协调的；统一部分可能是 被认为弯曲能量为零。 因素和 组件分析，该分解支持完全利用 地标数据的特殊结构，就像仔细的方法一样 将一般线性模型调整为此上下文。 在新的资金期间，我们将探索这些范围的几个扩展 用于研究形状可变性和统计方法的特征空间 他们支持。 我们将严格考虑两者之间的关系 可以在医学图像中参数化“斑点”的方式：作为位置 或作为灰度的模式。 我们将探索“增长的模型 中心”，凸起和其他涉及定向分歧的过程 在许多具有里程碑意义的位置和曲线或表面中。 我们将 扩展可用于包括的严格机械的假设 形式与标量场的不对称性和相关性的问题；还有我们 将仔细探索基于里程碑的方法与其他之间的联系 图像摘要的方法，例如连续模糊的“堆栈”， 在其他医学成像研究中心进行研究。 可视化 基于里程碑的生物识别机械将在Extenso中转移到非常 强大的图形计算机，恒星GS1000，以支持 三维数据的互动分析。 这些新的形态计量学方法将应用于示范研究 根据颅面生长，颅面异常和神经病学。 这 方法应大大提高形态学的效率 可以单独使用或与其他措施相结合的信息， 在大型器官大规模形状的生物医学研究中 可变性，随着时间的变化或与原因的协方差或 效果。