Chicherova Natalia, Fundana Ketut, Müller Bert, Cattin Philippe C
Med Image Comput Comput Assist Interv. 2014;17(Pt 1):243-50. doi: 10.1007/978-3-319-10404-1_31.
The fusion of information from different medical imaging techniques plays an important role in data analysis. Despite the many proposed registration algorithms the problem of registering 2D histological images to 3D CT or MR imaging data is still largely unsolved. In this paper we propose a computationally efficient automatic approach to match 2D histological images to 3D micro Computed Tomography data. The landmark-based approach in combination with a density-driven RANSAC plane-fitting allows efficient localization of the histology images in the 3D data within less than four minutes (single-threaded MATLAB code) with an average accuracy of 0.25 mm for orrect and 2.21mm for mismatched slices. The approach managed to uccessfully localize 75% of the histology images in our database. The proposed algorithm is an important step towards solving the problem of registering 2D histology sections to 3D data fully automatically.
来自不同医学成像技术的信息融合在数据分析中起着重要作用。尽管已经提出了许多配准算法，但将二维组织学图像与三维CT或MR成像数据进行配准的问题在很大程度上仍未得到解决。在本文中，我们提出了一种计算效率高的自动方法，用于将二维组织学图像与三维显微计算机断层扫描数据进行匹配。基于地标的方法与密度驱动的RANSAC平面拟合相结合，能够在不到四分钟的时间内（单线程MATLAB代码）在三维数据中高效定位组织学图像，正确切片的平均精度为0.25毫米，错配切片的平均精度为2.21毫米。该方法成功地在我们的数据库中定位了75%的组织学图像。所提出的算法是朝着完全自动解决将二维组织学切片与三维数据配准问题迈出的重要一步。
