• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Respecting Anatomically Rigid Regions in Nonrigid Registration.
【24h】

Respecting Anatomically Rigid Regions in Nonrigid Registration.

机译：在非刚性套准中尊重解剖学上的刚性区域。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Medical image registration has received considerable attention in the medical imaging and computer vision communities because of the variety of ways in which it can potentially improve patient care. This application of image registration aligns images of regions of the human body for comparing images of the same patient at different times, for example, when assessing differences in a disease over time; comparing images of the same anatomical structure across different patients, for example, to understand patient variability; and comparing images of the same patient from different modalities that provide complementary information, for example, CT and PET to assess cancer.;The two primary types of registration make use of rigid and nonrigid transformations. Medical images typically contain some regions of bone, which behave rigidly, and some regions of soft tissue, which are able to deform. While a strictly rigid transformation would not account for soft tissue deformations, a strictly nonrigid transformation would not abide by the physical properties of the bone regions. Over the years, many image registration techniques have been developed and refined for particular applications but none of them compute a continuous transformation simultaneously containing both rigid and nonrigid regions.;This thesis focuses on using a sophisticated segmentation algorithm to identify and preserve bone structure in medical image registration while allowing the rest of the image to deform. The registration is performed by minimizing an objective function over a set of transformations that are defined in a piecewise manner: rigid over a portion of the domain, nonrigid over the remainder of the domain, and continuous everywhere. The objective function is minimized via steepest gradient descent, yielding an initial boundary value problem that is discretized in both time and space and solved numerically using multigrid. The registration results are compared to results of strictly rigid and nonrigid registrations.
机译：由于医学图像配准可以潜在地改善患者护理的方式多种多样，因此在医学成像和计算机视觉界受到了相当大的关注。图像配准的这种应用使人体区域的图像对齐，以比较同一患者在不同时间的图像，例如，在评估疾病随时间的差异时。比较不同患者的相同解剖结构的图像，例如，以了解患者的变异性；并比较来自不同模式的同一位患者的图像，这些图像可提供补充信息，例如CT和PET来评估癌症。两种主要注册类型都使用刚性和非刚性转换。医学图像通常包含一些骨骼区域，这些区域具有刚性表现，而某些软组织区域则可以变形。虽然严格的刚性转换不能解决软组织的变形，但是严格的非刚性转换不能遵守骨骼区域的物理特性。多年以来，针对特定应用开发和完善了许多图像配准技术，但没有一种能够同时计算包含刚性和非刚性区域的连续变换。;本文主要研究使用复杂的分割算法来识别和保留医学中的骨骼结构图像对齐，同时允许其余图像变形。通过最小化一组以分段方式定义的转换的目标函数来执行配准：在一部分域上是刚性的，在域的其余部分上是非刚性的，并且到处都是连续的。通过最陡峭的梯度下降使目标函数最小化，从而产生初始边界值问题，该问题在时间和空间上都离散化，并使用多重网格进行数值求解。将注册结果与严格的刚性注册和非刚性注册的结果进行比较。

著录项

  • 作者

    Spangler, Eric J.;

  • 作者单位

    Rochester Institute of Technology.;

  • 授予单位 Rochester Institute of Technology.;
  • 学科 Mathematics.;Applied mathematics.
  • 学位 M.S.
  • 年度 2016
  • 页码 49 p.
  • 总页数 49
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 公共建筑;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号