TY - GEN
T1 - An automated 3D registration method for optical coherence tomography volumes
AU - Gan, Yu
AU - Yao, Wang
AU - Myers, Kristin M.
AU - Hendon, Christine P.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/2
Y1 - 2014/11/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84929493952&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929493952&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2014.6944469
DO - 10.1109/EMBC.2014.6944469
M3 - Conference contribution
C2 - 25570837
AN - SCOPUS:84929493952
T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
SP - 3873
EP - 3876
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
T2 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Y2 - 26 August 2014 through 30 August 2014
ER -