Department of Electrical Engineering, National United University, Taiwan.
Institute of Biomedical Engineering, National Taiwan University, Taiwan.
Sci Rep. 2017 Feb 1;7:39834. doi: 10.1038/srep39834.
Long-term comparisons of infrared image can facilitate the assessment of breast cancer tissue growth and early tumor detection, in which longitudinal infrared image registration is a necessary step. However, it is hard to keep markers attached on a body surface for weeks, and rather difficult to detect anatomic fiducial markers and match them in the infrared image during registration process. The proposed study, automatic longitudinal infrared registration algorithm, develops an automatic vascular intersection detection method and establishes feature descriptors by shape context to achieve robust matching, as well as to obtain control points for the deformation model. In addition, competitive winner-guided mechanism is developed for optimal corresponding. The proposed algorithm is evaluated in two ways. Results show that the algorithm can quickly lead to accurate image registration and that the effectiveness is superior to manual registration with a mean error being 0.91 pixels. These findings demonstrate that the proposed registration algorithm is reasonably accurate and provide a novel method of extracting a greater amount of useful data from infrared images.
长期比较红外图像可以促进乳腺癌组织生长的评估和早期肿瘤检测，其中纵向红外图像配准是必要的步骤。然而，很难将标记物附着在身体表面数周，并且在注册过程中很难检测到解剖学基准标记物并在红外图像中匹配它们。本研究提出了一种自动纵向红外配准算法，开发了一种自动血管交点检测方法，并通过形状上下文建立特征描述符，以实现稳健匹配，并为变形模型获得控制点。此外，还开发了竞争优胜者引导机制以实现最佳匹配。该算法通过两种方式进行评估。结果表明，该算法可以快速实现准确的图像配准，并且其有效性优于手动配准，平均误差为 0.91 像素。这些发现表明，所提出的配准算法具有相当的准确性，并为从红外图像中提取更多有用数据提供了一种新方法。
