Suppr超能文献

混合多尺度地标与可变形图像配准

Hybrid multiscale landmark and deformable image registration.

作者信息

Paquin Dana, Levy Doron, Xing Lei

机构信息

Department of Mathematics, Stanford University, Stanford, CA 94305-2125, USA.

出版信息

Math Biosci Eng. 2007 Oct;4(4):711-37. doi: 10.3934/mbe.2007.4.711.

Abstract

An image registration technique is presented for the registration of medical images using a hybrid combination of coarse-scale landmark and B-splines deformable registration techniques. The technique is particularly effective for registration problems in which the images to be registered contain large localized deformations. A brief overview of landmark and deformable registration techniques is presented. The hierarchical multiscale image decomposition of E. Tadmor, S. Nezzar, and L. Vese, A multiscale image representation using hierarchical (BV;L(2)) decompositions, Multiscale Modeling and Simulations, vol. 2, no. 4, pp. 554{579, 2004, is reviewed, and an image registration algorithm is developed based on combining the multiscale decomposition with landmark and deformable techniques. Successful registration of medical images is achieved by first obtaining a hierarchical multiscale decomposition of the images and then using landmark-based registration to register the resulting coarse scales. Corresponding bony structure landmarks are easily identified in the coarse scales, which contain only the large shapes and main features of the image. This registration is then fine tuned by using the resulting transformation as the starting point to deformably register the original images with each other using an iterated multiscale B-splines deformable registration technique. The accuracy and efficiency of the hybrid technique is demonstrated with several image registration case studies in two and three dimensions. Additionally, the hybrid technique is shown to be very robust with respect to the location of landmarks and presence of noise.

摘要

本文提出了一种图像配准技术,用于使用粗尺度地标和B样条可变形配准技术的混合组合来配准医学图像。该技术对于待配准图像包含大的局部变形的配准问题特别有效。文中简要概述了地标和可变形配准技术。回顾了E. Tadmor、S. Nezzar和L. Vese在2004年发表于《多尺度建模与仿真》第2卷第4期第554 - 579页的“使用分层(BV;L(2))分解的多尺度图像表示”中的分层多尺度图像分解,并基于将多尺度分解与地标和可变形技术相结合开发了一种图像配准算法。通过首先对图像进行分层多尺度分解,然后使用基于地标的配准来配准得到的粗尺度,实现了医学图像的成功配准。在仅包含图像大形状和主要特征的粗尺度中,可以轻松识别出相应的骨结构地标。然后,以得到的变换为起点,使用迭代多尺度B样条可变形配准技术对原始图像进行相互可变形配准,从而对该配准进行微调。通过二维和三维的几个图像配准案例研究,证明了混合技术的准确性和效率。此外,还表明混合技术在地标位置和噪声存在方面非常稳健。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验