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Automatic landmark identification in orthodontic cephalometric radiographs.

机译：正畸头颅X线片中的自动地标识别。

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摘要

The goal of orthodontic and orthognathic therapy is to improve the interrelationships among craniofacial tissues, which determine form, function, aesthetics, and relative stability. A two-dimensional x-ray image of the sagital skull projection, called a cephalometric radiograph, can be used to evaluate these relationships. In orthodontics, distances and angles among cephalometric image landmarks are compared with normative values to diagnose a patient's deviation from ideal form and prescribe treatment. This process is often extremely time consuming for the orthodontist. A computer-based system which automatically performs these visual tasks has enormous potential in diagnostic medicine, particularly in orthodontics. One task which may benefit from such a system is landmark identification in cephalometric radiographs. Automatic landmark identification and analysis could simultaneously save time, mathematically define landmarks, improve the repeatability of landmark identification, and support alternative methods of form analysis.;Computer vision methods can be heuristic (based on a set of rules usually applicable to only one image type) or can be broad-based (applicable to many tasks). Previous attempts to automatically locate landmarks have been heuristic. These attempts have been only partially successful. The use of a unified broad based approach to image analysis may solve the automatic landmarking problem and may also apply to a wider range of computer vision tasks.;This dissertation substantiates and tests Spatial Spectroscopy, a unified broad based approach to vision that makes decisions about image structure based on a convolution of the image with a set of filters followed by a nonlinear decision method using statistical pattern recognition techniques. This study tested two filter sets for comparison: a multiscale Gaussian derivative filter set and an offset Gaussian set. Furthermore, a statistical decision process is proposed and tested that includes probability measures and outlier removal. This study tested both high and low resolution images.;These results show: (1) There is no difference in landmark identification errors between human identification on the computer display at low resolution (4 mm
机译：正畸和正颌治疗的目的是改善颅面组织之间的相互关系，这些相互关系决定了形态，功能，美观性和相对稳定性。可以使用被称为头颅放射线照片的矢状颅骨投影的二维X射线图像来评估这些关系。在正畸学中，将头颅测量图像界标之间的距离和角度与标准值进行比较，以诊断患者与理想形式的偏离并开出处方。对于正畸医生而言，该过程通常非常耗时。自动执行这些视觉任务的基于计算机的系统在诊断医学，尤其是正畸学中具有巨大的潜力。可以从这样的系统中受益的一项任务是在头颅射线照相术中的地标识别。自动地标识别和分析可以同时节省时间，数学上定义地标，提高地标识别的可重复性并支持其他形式分析方法。计算机视觉方法可以是启发式的（基于通常仅适用于一种图像类型的一组规则） ），也可以是基础广泛的（适用于许多任务）。以前尝试自动定位地标一直是试探性的。这些尝试只是部分成功。使用统一的广泛基础的方法进行图像分析可以解决自动界标问题，并且还可以应用于更广泛的计算机视觉任务。本论文证实并测试了空间光谱学，这是一种统一的广泛基础的视觉方法，可以对基于具有一组滤波器的图像卷积的图像结构，然后是使用统计模式识别技术的非线性决策方法。本研究测试了两个滤波器组进行比较：多尺度高斯导数​​滤波器组和偏移高斯组。此外，提出并测试了包括概率测度和异常值消除在内的统计决策过程。这项研究测试了高分辨率和低分辨率的图像。这些结果表明：（1）在低分辨率（4 mm

著录项

  • 作者

    Rudolph, David Jeffrey.;

  • 作者单位

    The University of North Carolina at Chapel Hill.;

  • 授予单位 The University of North Carolina at Chapel Hill.;
  • 学科 Engineering Biomedical.;Health Sciences Dentistry.;Computer Science.
  • 学位 Ph.D.
  • 年度 1995
  • 页码 128 p.
  • 总页数 128
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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