Abstract-3D surface registration transforms multiple 3D datasets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or non-rigid registration, but seldom discuss them as a whole. Our study serves two purposes: (i) to give a comprehensive survey of both types of registration, focusing on 3D point clouds and meshes, and (ii) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in that it comprises three core interwoven components: model selection, correspondences & constraints and optimization. Study of these components (i) provides a basis for comparison of the novelties of different techniques, (ii) reveals the similarity of rigid and non-rigid registration in terms of problem representations, and (iii) shows how over-fitting arises in non-rigid registration and the reasons for increasing interest in intrinsic techniques. We further summarise some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends.

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A novel statistical model based on texture and shape for fully automatic intraretinal layer segmentation of normal retinal tomograms obtained by a commercial 800nm optical coherence tomography (OCT) system is developed. While existing algorithms often fail dramatically due to strong speckle noise, non-optimal imaging conditions, shadows and other artefacts, the novel algorithm's accuracy only slowly deteriorates when progressively increasing segmentation task difficulty. Evaluation against a large set of manual segmentations shows unprecedented robustness, even in the presence of additional strong speckle noise, with dynamic range tested down to 12dB, enabling segmentation of almost all intraretinal layers in cases previously inaccessible to the existing algorithms. For the first time, an error measure is computed from a large, representative manually segmented data set (466 B-scans from 17 eyes, segmented twice by different operators) and compared to the automatic segmentation with a difference of only 2.6% against the inter-observer variability.

Microscopes offer a limited depth of focus which precludes the observation of a complete image of a three-dimensional (3D) object in a single view. Investigations, by a variety of researchers, have led to the development of extended depth of focus algorithms for serial optical slices of microscopic 3D objects in recent years. However, to date, no quantitative comparison of the different algorithms has been performed, generally leaving the evaluation to the subjective qualitative appreciation of the observer. In this paper we use three different tests for extended depth of focus algorithm evaluation and test 10 different algorithms, some of them have been adapted (by us) for a series of optical slices. However, the main contribution of the paper is a new improved algorithm for computing the extended depth of focus. q

A two stage statistical model based on texture and shape for fully automatic choroidal segmentation of normal and pathologic eyes obtained by a 1060 nm optical coherence tomography (OCT) system is developed. A novel dynamic programming approach is implemented to determine location of the retinal pigment epithelium/ Bruch’s membrane /choriocapillaris (RBC) boundary. The choroid–sclera interface (CSI) is segmented using a statistical model. The algorithm is robust even in presence of speckle noise, low signal (thick choroid), retinal pigment epithelium (RPE) detachments and atrophy, drusen, shadowing and other artifacts. Evaluation against a set of 871 manually segmented cross-sectional scans from 12 eyes achieves an average error rate of 13%, computed per tomogram as a ratio of incorrectly classified pixels and the total layer surface. For the first time a fully automatic choroidal segmentation algorithm is successfully applied to a wide range of clinical volumetric OCT data.