Gan Yu, Yao Wang, Myers Kristin M, Hendon Christine P
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3873-6. doi: 10.1109/EMBC.2014.6944469.
Optical coherence tomography (OCT) is able to provide high resolution volumetric data for biological tissues. However, the field of view (FOV) of OCT is sometimes smaller than the field of interest, which limits the clinical application of OCT. One way to overcome the drawback is to stitch multiple 3D volumes. In this paper, we propose a novel method to register multiple overlapped volumetric OCT data into a single volume. The relative positions of overlapped volumes were estimated on en face plane and at depth. On en face plane, scale invariant feature transform (SIFT) was implemented to extract the keypoints in each volume. Based on the invariant features, volumes were paired through keypoint matching. Then, we formulated the relationship between paired offsets and absolute positions as a linear model and estimated the centroid of each volume using least square method. Moreover, we calibrated the depth displacement in each paired volume and aligned the z coordinates of volumes globally. The algorithm was validated through stitching multiple volumetric OCT datasets of human cervix tissue and of swine heart. The experimental results demonstrated that our method is capable of visualizing biological samples over a wider FOV, which enhances the investigation of tissue structure such as fiber orientation.
光学相干断层扫描（OCT）能够为生物组织提供高分辨率的体积数据。然而，OCT的视野（FOV）有时小于感兴趣的区域，这限制了OCT的临床应用。克服这一缺点的一种方法是拼接多个三维体积。在本文中，我们提出了一种将多个重叠的体积OCT数据配准到单个体积中的新方法。重叠体积的相对位置在表面平面和深度上进行估计。在表面平面上，实施尺度不变特征变换（SIFT）以提取每个体积中的关键点。基于不变特征，通过关键点匹配对体积进行配对。然后，我们将配对偏移量与绝对位置之间的关系公式化为线性模型，并使用最小二乘法估计每个体积的质心。此外，我们校准了每个配对体积中的深度位移，并全局对齐了体积的z坐标。该算法通过拼接人体宫颈组织和猪心脏的多个体积OCT数据集进行了验证。实验结果表明，我们的方法能够在更宽的视野上可视化生物样本，这增强了对组织结构（如纤维方向）的研究。
