Developing a Three Dimensional Registration Method for Optical Coherence Tomography Data

This work proposes a registration method for stitching different overlapping Optical Coherence Tomography (OCT) data. The algorithm is based on the basic procedure of image registration where key points and descriptors are located, feature matching is established and transformed using a homographic transformation to obtain the resultant registered images. Image similarity techniques such as mean square error, structural similarity index, and peak signal to noise ratio are the three basic approaches that are used for the analysis of these registered OCT images from the OCT datasets. An algorithm for locating the differences in the registered images against reference and target images is also demonstrated. Similarity measures and image differentiation approach provide a general analysis regarding the appropriateness of the image registration algorithm. The same methods are also used for the analysis of the optical coherence tomography volume scans. The analysis on the similarity of the images also shows that images with the highest similarities have the highest possibilities to be close to each other and can be further used for the registration. The analysis comprises the comparison of two optical coherence tomographic volumes.