Busayara Sata, Zrimec Tatjana
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):670-7.
Image registration is a fundamental problem in medical imaging. It is especially challenging in lung images compared, for example, with the brain. The challenges include large anatomical variations of human lung and a lack of fixed landmarks inside the lung. This paper presents a new method for lung HRCT image registration. It employs a landmark-based global transformation and a novel ray-tracing-based lung surface registration. The proposed surface registration method has two desirable properties: 1) it is fully reversible, and 2) it ensures that the registered lung will be inside the target lung. We evaluated the registration performance by applying it to lung regions mapping. Tested on 46 scans, the registered regions were 89% accurate compared with the ground-truth.
图像配准是医学成像中的一个基本问题。例如,与脑部图像相比,肺部图像的配准尤其具有挑战性。这些挑战包括人类肺部较大的解剖变异以及肺部内部缺乏固定的标志物。本文提出了一种用于肺部高分辨率计算机断层扫描(HRCT)图像配准的新方法。它采用基于标志物的全局变换和一种新颖的基于光线追踪的肺表面配准方法。所提出的表面配准方法具有两个理想特性:1)它是完全可逆的,2)它确保配准后的肺将位于目标肺内部。我们通过将其应用于肺部区域映射来评估配准性能。在46次扫描上进行测试,与真实情况相比,配准区域的准确率为89%。