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A knowledge-based deformable surface model for analysis of medical images.

机译:基于知识的可变形表面模型,用于医学图像分析。

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摘要

Medical images are challenging for segmentation. Deformable models proved to be one of the most effective tools for this purpose, however they still have several shortcomings and face several problems when used for images with objects that have low contrast, multiple edges (of other objects) in their vicinity, or do not have a continuous boundary in the image. They also suffer in general from problems such as initialization, self-cutting, and concave shape. In this research, we developed and implemented a two-dimensional (2D) and three-dimensional (3D) deformable model for analysis of medical images. We used the 2D deformable model to segment structures with low contrast and multiple or discontinuous edges. To do this, we developed an edge-tracking algorithm for external forces, adaptive force weights, and interpolation of energies. We further developed a 2D warping model that uses this deformable contour to extract the boundaries and create a mesh-map in each image using equidistance contours. Thin-plate splines or inverse-distance weights are used for interpolation between the mesh-map grids. We developed a 3D deformable surface model as the extension of the deformable contour to 3D. The model has a closed discrete structure based on polygonal facets. We developed a method based on LSE estimation of Dupin indicatrix to calculate internal forces and use gradient-based operator for external forces. We remedy self-cutting by extracting principal axis and performing reslicing followed by triangulation of the model. We propose a method for creating the initial surface from the individual 2D contours, and use multi-resolution and resampling of the surface and re-triangulation to further improve the model deformation. The model is initialized using a rule-based expert system. We extended the 2D warping model to 3D. The developed models have been used in several challenging applications among which, segmentation of hippocampus from brain MRI and prostate from ultrasound images, and the warping have been extensively investigated. Comparison to manual results shows excellent model performance.
机译:医学图像对于分割具有挑战性。变形模型被证明是用于此目的的最有效工具之一,但是,当将其用于具有低对比度对象,附近(其他对象的多个边缘)或没有对比度的对象的图像时,可变形模型仍然存在一些缺点并面临多个问题。在图像中具有连续边界。它们通常还遭受诸如初始化,自切割和凹形的问题。在这项研究中,我们开发并实现了用于分析医学图像的二维(2D)和三维(3D)变形模型。我们使用2D变形模型分割具有低对比度和多个或不连续边缘的结构。为此,我们针对外力,自适应力权重和能量插值开发了一种边缘跟踪算法。我们进一步开发了2D翘曲模型,该模型使用此可变形轮廓提取边界,并使用等距轮廓在每个图像中创建网格图。薄板样条或反距离权重用于网格图网格之间的插值。我们开发了3D可变形曲面模型,将可变形轮廓扩展到3D。该模型具有基于多边形小面的闭合离散结构。我们开发了一种基于Dupin indicatrix的LSE估计的方法来计算内力,并对外力使用基于梯度的算子。我们通过提取主轴并进行切片,然后对模型进行三角剖分来补救自切割。我们提出了一种从各个2D轮廓创建初始曲面的方法,并使用曲面的多分辨率和重采样以及重新三角剖分来进一步改善模型变形。使用基于规则的专家系统初始化模型。我们将2D变形模型扩展到3D。所开发的模型已用于多种具有挑战性的应用中,其中,对来自脑部MRI的海马体分割和超声图像中的前列腺分割以及翘曲进行了广泛研究。与手动结果的比较显示出出色的模型性能。

著录项

  • 作者

    Ghanei, Amir.;

  • 作者单位

    University of Michigan.;

  • 授予单位 University of Michigan.;
  • 学科 Engineering Electronics and Electrical.; Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 172 p.
  • 总页数 172
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;生物医学工程;
  • 关键词

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