Graduate Institute of Biomedical Engineering, and Graduate Institute of Applied Science and Technology, Honors College National Taiwan University of Science and Technology, Taipei City, 10607 Taiwan.
Bioinformatics. 2013 Aug 1;29(15):1879-87. doi: 10.1093/bioinformatics/btt309. Epub 2013 May 29.
Alignment of medical images is a vital component of a large number of applications throughout the clinical track of events; not only within clinical diagnostic settings, but prominently so in the area of planning, consummation and evaluation of surgical and radiotherapeutical procedures. However, image registration of medical images is challenging because of variations on data appearance, imaging artifacts and complex data deformation problems. Hence, the aim of this study is to develop a robust image alignment method for medical images.
An improved image registration method is proposed, and the method is evaluated with two types of medical data, including biological microscopic tissue images and dental X-ray images and compared with five state-of-the-art image registration techniques. The experimental results show that the presented method consistently performs well on both types of medical images, achieving 88.44 and 88.93% averaged registration accuracies for biological tissue images and X-ray images, respectively, and outperforms the benchmark methods. Based on the Tukey's honestly significant difference test and Fisher's least square difference test tests, the presented method performs significantly better than all existing methods (P ≤ 0.001) for tissue image alignment, and for the X-ray image registration, the proposed method performs significantly better than the two benchmark b-spline approaches (P < 0.001).
The software implementation of the presented method and the data used in this study are made publicly available for scientific communities to use (http://www-o.ntust.edu.tw/∼cweiwang/ImprovedImageRegistration/).
医学图像的配准是临床事件过程中大量应用的重要组成部分；不仅在临床诊断环境中如此，而且在手术和放射治疗计划、完成和评估领域中也是如此。然而，由于数据外观、成像伪影和复杂的数据变形问题的变化，医学图像的配准具有挑战性。因此，本研究的目的是开发一种用于医学图像的强大图像配准方法。
提出了一种改进的图像配准方法，并使用两种类型的医学数据（包括生物微观组织图像和牙科 X 射线图像）对该方法进行了评估，并与五种最先进的图像配准技术进行了比较。实验结果表明，所提出的方法在两种类型的医学图像上都表现良好，生物组织图像和 X 射线图像的平均配准精度分别达到 88.44%和 88.93%，并且优于基准方法。基于图基的诚实显著差异检验和费希尔的最小二乘差异检验，所提出的方法在组织图像配准方面明显优于所有现有方法（P≤0.001），而对于 X 射线图像注册，所提出的方法明显优于两种基准的样条方法（P<0.001）。
所提出方法的软件实现和本研究中使用的数据可供科学界使用（http://www-o.ntust.edu.tw/∼cweiwang/ImprovedImageRegistration/）。
