Surface Registration Algorithm Research Articles

A Novel Augmented Reality Assisted Orthopedic Surgical Robotic System With Bidirectional Surface Registration Algorithms

A Novel Augmented Reality Assisted Orthopedic Surgical Robotic System With Bidirectional Surface Registration Algorithms

Intraoperative laparoscopic liver surface registration with preoperative CT using mixing features and overlapping region masks.

Laparoscopic liver resection is a minimal invasive surgery. Augmented reality can map preoperative anatomy information extracted from computed tomography to the intraoperative liver surface reconstructed from stereo 3D laparoscopy. However, liver surface registration is particularly challenging as the intraoperative surface is only partially visible and suffers from large liver deformations due to pneumoperitoneum. This study proposes a deep learning-based robust point cloud registration network. This study proposed a low overlap liver surface registration algorithm combining local mixed features and global features of point clouds. A learned overlap mask is used to filter the non-overlapping region of the point cloud, and a network is used to predict the overlapping region threshold to regulate the training process. We validated the algorithm on the DePoLL (the Deformable Porcine Laparoscopic Liver) dataset. Compared with the baseline method and other state-of-the-art registration methods, our method achieves minimum target registration error (TRE) of 19.9 ± 2.7mm. The proposed point cloud registration method uses the learned overlapping mask to filter the non-overlapping areas in the point cloud, then the extracted overlapping area point cloud is registered according to the mixed features and global features, and this method is robust and efficient in low-overlap liver surface registration.

Freeform optics characterization with surface registration and fitting algorithms for optical point-based spatial path 3D topography metrology.

Accurate characterization of the form error for freeform optics is critical for controlled manufacturing and evaluation of optical properties. To solve the difficulty of current surface registration and fitting algorithms, and improve characterization accuracy of the form error of freeform optics for optical point-based spatial path 3D topography metrology, in this paper, improved surface registration and fitting algorithms are proposed, including a B-spline surface description of freeform optics, point orthogonal projection, registration parameter optimization, and B-spline fitting. Limited by the angular characteristics of an optical point-based sensor, the slope and reference frame of freeform optics must be flexibly adjusted, and polynomial surfaces are described and fitted as B-spline surfaces based on B-spline geometric invariance. To efficiently determine corresponding points on B-spline surfaces, the point orthogonal projection algorithm without Bezier subdivision is proposed using the first-order Newton method. Then, the iteration procedure of coordinate adjustment in sphere space using Lie algebra registration parameters is proposed to solve the difficulty of the current registration parameter optimization procedure. To fit a spatial path form error, the least squares B-spline fitting method is proposed to improve the Zernike method. Through simulation experiments, the proposed registration algorithm with good convergence can improve accuracy by one to two orders of magnitude compared with current registration algorithms. Through repeated experiments of a freeform prism, the proposed method can significantly improve the peak-to-valley and RMS accuracies compared with the stylus method, and characterize the mid-high frequency form error (about 200nm) of a freeform prism.

A novel approach for joint line restoration in revision total ankle arthroplasty based on the three-dimensional registration of the contralateral tibia and fibula

PurposeThe use of total ankle arthroplasty (TAA) is increasing over time, as so will the need for revision TAAs in the future. Restoration of the ankle joint line (JL) in revision TAA is often difficult due to severe bone loss. This study analyzed the accuracy of a three-dimensional (3D) registration of the contralateral tibia and fibula to restore the ankle joint line (JL) and reported side-to-side differences of anatomical landmarks.Methods3D triangular surface models of 96 paired lower legs underwent a surface registration algorithm for superimposition of the mirrored contralateral lower leg onto the original lower leg to approximate the original ankle JL using a proximal, middle and distal segment. Distances of the distal fibular tip, anterior and posterior medial colliculus to the JL were measured and absolute side-to-side differences reported. Anterior lateral distal tibial angle (ADTA) and lateral distal tibial angle (LDTA) were measured.ResultsMean JL approximation was most accurate for the distal segment (0.1 ± 1.4 mm (range: -3.4 to 2.8 mm)) and middle segment (0.1 ± 1.2 mm (range: -2.8 to 2.5 mm)) compared to the proximal segment (-0.2 ± 1.6 mm (range: -3.0 to 4.9 mm)) (p = 0.007). Distance of the distal fibular tip, the anterior, and posterior medial colliculus to the JL, ADTA and LDTA yielded no significant side-to-side differences (n.s.).Conclusion3D registration of the contralateral tibia and fibula reliably approximated the original ankle JL. The contralateral distal fibular tip, anterior and posterior medial colliculi, ADTA and LDTA can be used reliably for the planning of revision TAA with small side-to-side differences reported.Level of EvidenceIV.

Registration based assessment of femoral torsion for rotational osteotomies based on the contralateral anatomy

BackgroundComputer-assisted techniques for surgical treatment of femoral deformities have become increasingly important. In state-of-the-art 3D deformity assessments, the contralateral side is used as template for correction as it commonly represents normal anatomy. Contributing to this, an iterative closest point (ICP) algorithm is used for registration. However, the anatomical sections of the femur with idiosyncratic features, which allow for a consistent deformity assessment with ICP algorithms being unknown. Furthermore, if there is a side-to-side difference, this is not considered in error quantification.The aim of this study was to analyze the influence and value of the different sections of the femur in 3D assessment of femoral deformities based on the contralateral anatomy.Material and methods3D triangular surface models were created from CT of 100 paired femurs (50 cadavers) without pathological anatomy. The femurs were divided into sections of eponymous anatomy of a predefined percentage of the whole femoral length. A surface registration algorithm was applied to superimpose the ipsilateral on the contralateral side. We evaluated 3D femoral contralateral registration (FCR) errors, defined as difference in 3D rotation of the respective femoral section before and after registration to the contralateral side. To compare this method, we quantified the landmark-based femoral torsion (LB FT). This was defined as the intra-individual difference in overall femoral torsion using with a landmark-based method.ResultsContralateral rotational deviation ranged from 0° to 9.3° of the assessed femoral sections, depending on the section. Among the sections, the FCR error using the proximal diaphyseal area for registration was larger than any other sectional error. A combination of the lesser trochanter and the proximal diaphyseal area showed the smallest error. The LB FT error was significantly larger than any sectional error (p < 0.001).ConclusionWe demonstrated that if the contralateral femur is used as reconstruction template, the built-in errors with the registration-based approach are smaller than the intraindividual difference of the femoral torsion between both sides. The errors are depending on the section and their idiosyncratic features used for registration. For rotational osteotomies a combination of the lesser trochanter and the proximal diaphyseal area sections seems to allow for a reconstruction with a minimal error.

“Image to patient” equal-resolution surface registration supported by a surface scanner: analysis of algorithm efficiency for computer-aided surgery

PurposeThe “image to patient” registration procedure is crucial for the accuracy of surgical instrument tracking relative to the medical image while computer-aided surgery. The main aim of this work was to create an equal-resolution surface registration algorithm (ERSR) and analyze its efficiency.MethodsThe ERSR algorithm provides two datasets with equal, high resolution and approximately corresponding points. The registered sets are obtained by projection of a user-designed rectangle(s)-shaped uniform clouds of points on DICOM and surface scanner datasets. The tests of the algorithm were performed on a phantom with titanium microscrews. We analyzed the influence of DICOM resolution on the effect of the ERSR algorithm and compared the ERSR to standard paired-points landmark transform registration. The methods of analysis were Target Registration Error, distance maps, and their histogram evaluation.ResultsThe mean TRE in case of ERSR equaled 0.8 ± 0.3 mm (resolution A), 0.8 ± 0.5 mm (resolution B), and 1.0 ± 0.7 mm (resolution C). The mean values were at least 0.4 mm lower than in the case of landmark transform registration. The distance maps between the model achieved from the scanner and the CT-based model were analyzed by histogram. The frequency of the first bin in a histogram of the distance map for ERSR was about 0.6 for all three resolutions of DICOM dataset and three times higher than in the case of landmark transform registration. The results were statistically analyzed using the Wilcoxon signed-rank test (alpha = 0.05).ConclusionThe tests proved a statistically significant higher efficiency of equal resolution surface registration related to the landmark transform algorithm. It was proven that the lower resolution of the CT DICOM dataset did not degrade the efficiency of the ERSR algorithm. We observed a significantly lower response to decreased resolution than in the case of paired-points landmark transform registration.

Restoration of the patient-specific anatomy of the distal fibula based on a novel three-dimensional contralateral registration method

PurposePosttraumatic fibular malunion alters ankle joint biomechanics and may lead to pain, stiffness, and premature osteoarthritis. The accurate restoration is key for success of reconstructive surgeries. The aim of this study was to analyze the accuracy of a novel three-dimensional (3D) registration algorithm using different segments of the contralateral anatomy to restore the distal fibula.MethodsTriangular 3D surface models were reconstructed from computed tomographic data of 96 paired lower legs. Four segments were defined: 25% tibia, 50% tibia, 75% fibula, and 75% fibula and tibia. A surface registration algorithm was used to superimpose the mirrored contralateral model on the original model. The accuracy of distal fibula restoration was measured.ResultsThe median rotation error, 3D distance (Euclidean distance), and 3D angle (Euler’s angle) using the distal 25% tibia segment for the registration were 0.8° (− 1.7–4.8), 2.1 mm (1.4–2.9), and 2.9° (1.9–5.4), respectively. The restoration showed the highest errors using the 75% fibula segment (rotation error 3.2° (0.1–8.3); Euclidean distance 4.2 mm (3.1–5.8); Euler’s angle 5.8° (3.4–9.2)). The translation error did not differ significantly between segments.Conclusion3D registration of the contralateral tibia and fibula reliably approximated the premorbid anatomy of the distal fibula. Registration of the 25% distal tibia, including distinct anatomical landmarks of the fibular notch and malleolar colliculi, restored the anatomy with increasing accuracy, minimizing both rotational and translational errors. This new method of evaluating malreductions could reduce morbidity in patients with ankle fractures.Level of evidenceIV

Accuracy evaluation of surface registration algorithm using normal distribution transform in stereotactic body radiotherapy/radiosurgery: A phantom study

PurposeTo evaluate a feasibility of normal distribution transform (NDT) algorithm compared with the iterative closest point (ICP) method as a useful surface registration in stereotactic body radiotherapy (SBRT)/stereotactic radiosurgery (SRS).MethodsPoint cloud images using the 3D triangulation technology were obtained from a depth camera‐based optical imaging (OSI) system equipped in a radiosurgery room. Two surface registration algorithms, NDT and ICP, were used to measure and compare the discrepancy values between the reference and the current surfaces during the positioning of the patient. The performance evaluation was investigated by calculating the registration error and root‐mean‐square (RMS) values for the surface model, reposition, and target accuracy, which were analyzed statistically using a paired t‐test.ResultsFor surface model accuracy, the average of the registration error and RMS values were measured as 3.56 ± 2.20 mm and 6.98 ± 1.89 mm for ICP method, and 1.76 ± 1.32 mm and 3.58 ± 1.30 mm for NDT method (p < 0.05). For reposition accuracy, the average registration error and RMS values were calculated as 1.41 ± 0.98 mm and 2.53 ± 1.64 mm using ICP method, and 0.92 ± 0.61 mm and 1.75 ± 0.80 mm using NDT method (p = 0.005). The overall target accuracy using the NDT method reduced the average of the reposition error and overall RMS value by 0.71 and 1.32 mm, respectively, compared to the ICP method (p = 0.03).ConclusionsWe found that the surface registration algorithm based on NDT method provides more reliable accuracy in the values of surface model, reposition, and target accuracies than the classic ICP method. The NDT method in OSI systems offers reasonable accuracy in SBRT/SRS.

Accuracy of joint line restoration based on three-dimensional registration of the contralateral tibial tuberosity and the fibular tip

PurposeTo assess a novel method of three-dimensional (3D) joint line (JL) restoration based on the contralateral tibia and fibula.Methods3D triangular surface models were generated from computed tomographic data of 96 paired lower legs (48 cadavers) without signs of pathology. Three segments of the tibia and fibula, excluding the tibia plateau, were defined (tibia, fibula, tibial tuberosity (TT) and fibular tip). A surface registration algorithm was used to superimpose the mirrored contralateral model onto the original model. JL approximation and absolute mean errors for each segment registration were measured and its relationship to gender, height, weight and tibia and fibula length side-to-side differences analyzed. Fibular tip to JL distance was measured and analyzed.ResultsMean JL approximation did not yield significant differences among the three segments. Mean absolute JL error was highest for the tibia 1.4 ± 1.4 mm (range: 0 to 6.0 mm) and decreased for the fibula 0.8 ± 1.0 mm (range: 0 to 3.7 mm) and for TT and fibular tip segment 0.7 ± 0.6 (range: 0 to 2.4 mm) (p = 0.03). Mean absolute JL error of the TT and fibular tip segment was independent of gender, height, weight and tibia and fibula length side-to-side differences. Mean fibular tip to JL distance was 11.9 ± 3.4 mm (range: 3.4 to 22.1 mm) with a mean absolute side-to-side difference of 1.6 ± 1.1 mm (range: 0 to 5.3 mm).Conclusion3D registration of the contralateral tibia and fibula reliably approximated the original JL. The registration of, TT and fibular tip, as robust anatomical landmarks, improved the accuracy of JL restoration independent of tibia and fibula length side-to-side differences.Level of evidenceIV

Evaluation of technical performance of optical surface imaging system using conventional and novel stereotactic radiosurgery algorithms.

The Catalyst HD (C‐RAD Positioning AB, Uppsala, Sweden) optical surface imaging (OSI) system is able to manage interfractional patient positioning, intrafractional motion monitoring, and non‐contact respiratory gating without x‐ray exposure for radiation therapy. In recent years, a novel high‐precision surface registration algorithm for stereotactic radiosurgery (SRS algorithm) has been released. This study aimed to evaluate the technical performance of the OSI system using rigid phantoms, by comparing the conventional and SRS algorithms. To determine the system’s technical performance, isocenter displacements were calculated by surface image registration via the OSI system using head, thorax, and pelvis rigid phantoms. The reproducibility of positioning was evaluated by the mean value calculated by repeating the registration 10 times, without moving each phantom. The accuracy of positioning was evaluated by the mean value of the residual error, where the 10 offset values given to each phantom were subtracted from the isocenter displacement values. The stability of motion monitoring was evaluated by measuring isocenter drift during 20 min and averaging it over 10 measurements. For the head phantom, all tests were compared with the mask types and algorithms. As a result, for all sites and both algorithms, the reproducibility, accuracy, and stability for translation and rotation were <0.1 mm and <0.1°, <1.0 mm and <1.0°, and <0.1 mm and <0.1°, respectively. In particular, the SRS algorithm had a small absolute error and standard deviation of calculated isocenter displacement, and a significantly higher reproducibility and accuracy than the conventional algorithm (P < 0.01). There was no difference in the stability between the algorithms (P = 0.0280). The SRS algorithm was found to be suitable for the treatment of rigid body sites with less deformation and small area, such as the head and face.

Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images.

3D facial landmarks are known to be diagnostically relevant biometrics for many genetic syndromes. The objective of this study was to extend a state-of-the-art image-based 2D facial landmarking algorithm for the challenging task of 3D landmark identification on subjects with genetic syndromes, who often have moderate to severe facial dysmorphia. The automatic 3D facial landmarking algorithm presented here uses 2D image-based facial detection and landmarking models to identify 12 landmarks on 3D facial surface scans. The landmarking algorithm was evaluated using a test set of 444 facial scans with ground truth landmarks identified by two different human observers. Three hundred and sixty nine of the subjects in the test set had a genetic syndrome that is associated with facial dysmorphology. For comparison purposes, the manual landmarks were also used to initialize a non-linear surface-based registration of a non-syndromic atlas to each subject scan. Compared to the average intra- and inter-observer landmark distances of 1.1 mm and 1.5 mm respectively, the average distance between the manual landmark positions and those produced by the automatic image-based landmarking algorithm was 2.5 mm. The average error of the registration-based approach was 3.1 mm. Comparing the distributions of Procrustes distances from the mean for each landmarking approach showed that the surface registration algorithm produces a systemic bias towards the atlas shape. In summary, the image-based automatic landmarking approach performed well on this challenging test set, outperforming a semi-automatic surface registration approach, and producing landmark errors that are comparable to state-of-the-art 3D geometry-based facial landmarking algorithms evaluated on non-syndromic subjects.

A 3D face registration algorithm based on conformal mapping

SummaryRecently, 3D facial datasets are more easily available, and at the same time, the research of 3D face becomes more and more important. One of the most important research fields is 3D face registration, which plays an important role in face recognition, face shape analysis, and face animation. However, one of the most challenging issues in 3D face registration is to obtain a unique mapping for faces with different expression and landmark constraints. In this paper, we propose a novel conformal mapping algorithm to deal with the 3D face registration. Besides, the calculation is about harmonic energy, which makes our method applicable to low‐quality meshes. We begin with a harmonic mapping, then minimize the harmonic energy by a specific boundary condition on surfaces, and to obtain the conformal mapping, finally, we use a landmark‐constrained surface registration algorithm to register faces. Numerical experiments on various surfaces demonstrate the efficiency and robustness of our method.

Efficient feature-based image registration by mapping sparsified surfaces

With the advancement in the digital camera technology, the use of high resolution images and videos has been widespread in the modern society. In particular, image and video frame registration is frequently applied in computer graphics and film production. However, conventional registration approaches usually require long computational time for high resolution images and video frames. This hinders the application of the registration approaches in the modern industries. In this work, we first propose a new image representation method to accelerate the registration process by triangulating the images effectively. For each high resolution image or video frame, we compute an optimal coarse triangulation which captures the important features of the image. Then, we apply a surface registration algorithm to obtain a registration map which is used to compute the registration of the high resolution image. Experimental results suggest that our overall algorithm is efficient and capable to achieve a high compression rate while the accuracy of the registration is well retained when compared with the conventional grid-based approach. Also, the computational time of the registration is significantly reduced using our triangulation-based approach.

Skull Localization using Ultrasound for Navigated Neurosurgery

Image-guided navigation for neurosurgery requires accurate localization of the skull. Localization can be problematic when the patient is in a facedown position. The posterior skull lacks unique identifiable landmarks, which complicates standard localization methods using a tracked pointer. In addition to the lack of anatomical landmarks, trying to access facial surfaces is error-prone when working under the table and problems arise with line-of-sight of the optical tracker. We proposed the use of ultrasound to perform localization and investigated the accuracy of this process. A simulation study was performed to test the feasibility of ultrasound for localization on a plastic skull. An initial localization, using an optically tracked pointer, was performed to partially align pre-operative images and the skull model. Skull surface points were localized by optically tracked ultrasound and used in a surface registration algorithm. Accuracy and reproducibility was then investigated. Evaluation of the proposed localization method found that the average distance of points off the skull surface was 0.6 ± 0.1mm, which meets the same standards set by current commercially available systems for face-up positions. Using tracked ultrasound for registration is feasible for patients in facedown position. We provided a non-invasive method of registration that could be accomplished using one optical tracking camera, and maintains a constant line-of-sight. This project was performed in cooperation with Dr. Gernot Kronreif and the Austrian Center for Medical Innovation and Technology. Dr. Kronreif and his staff are preparing for a clinical test of this localization process.

A Novel Registration-Based Approach for 3D Assessment of Posttraumatic Distal Humeral Deformities.

With current 3-dimensional (3D) computer-based methods for the assessment of deformities, a surface registration method is applied to superimpose a computer model of the pathological bone onto a mirrored computer model of the contralateral side. However, because of bilateral differences, especially in humeral torsion, such template-based approaches may introduce bias in the assessment of a distal humeral deformity. We hypothesized that a novel registration approach might prove superior to the current approach in reducing such bias, thus yielding improved accuracy of 3D assessment of distal humeral deformities. Three-dimensional triangular surface models were generated from computed tomographic (CT) data of 100 paired humeri without a pathological condition. Humeral segments of varying, predetermined lengths, excluding the distal part of the humerus, were defined. A surface registration algorithm was applied to superimpose the humeral models of both sides based on each selected segment. Humeral contralateral registration (HCR) errors, defined as the residual differences in apparent 3D orientation between the distal parts, were evaluated. The mean HCR error (and standard deviation) using the distal-most humeral shaft segment to assess the angular orientation was 2.3° ± 1.1 (range, 0.5° to 5.8°). Including the humeral head in the surface registration algorithm, however, as is done currently, resulted in a higher HCR error (p < 0.001). The HCR error using the proximal-most segment was >10° in 20% of the cases and between 5° and 10° in an additional 50% of the cases. By comparison, using the proposed distal-most humeral shaft segment, the HCR error was between 5° and 10° in only 2% of cases, and was never >10°. The proximal segments are nevertheless used in the proposed method for registering humeral length. The proposed new approach yields a deformity assessment that is less prone to bias arising from inherent bilateral differences and therefore is more accurate than current surface registration approaches. Accurate 3D assessment is of fundamental importance if computer-based methods are applied in the correction of posttraumatic deformities.

Nonrigid iterative closest points for registration of 3D biomedical surfaces

Advanced 3D optical and laser scanners bring new challenges to computer graphics. We present a novel nonrigid surface registration algorithm based on Iterative Closest Point (ICP) method with multiple correspondences. Our method, called the Nonrigid Iterative Closest Points (NICPs), can be applied to surfaces of arbitrary topology. It does not impose any restrictions on the deformation, e.g. rigidity or articulation. Finally, it does not require parametrization of input meshes. Our method is based on an objective function that combines distance and regularization terms. Unlike the standard ICP, the distance term is determined based on multiple two-way correspondences rather than single one-way correspondences between surfaces. A Laplacian-based regularization term is proposed to take full advantage of multiple two-way correspondences. This term regularizes the surface movement by enforcing vertices to move coherently with their 1-ring neighbors. The proposed method achieves good performances when no global pose differences or significant amount of bending exists in the models, for example, families of similar shapes, like human femur and vertebrae models.

Nasolabial symmetry and esthetics in cleft lip and palate: analysis of 3D facial images.

To determine the relationship between nasolabial symmetry and esthetics in subjects with orofacial clefts. Eighty-four subjects (mean age 10 years, standard deviation 1.5) with various types of nonsyndromic clefts were included: 11 had unilateral cleft lip (UCL); 30 had unilateral cleft lip and alveolus (UCLA); and 43 had unilateral cleft lip, alveolus, and palate (UCLAP). A 3D stereophotogrammetric image of the face was taken for each subject. Symmetry and esthetics were evaluated on cropped 3D facial images. The degree of asymmetry of the nasolabial area was calculated based on all 3D data points using a surface registration algorithm. Esthetic ratings of various elements of nasal morphology were performed by eight lay raters on a 100 mm visual analog scale. Statistical analysis included ANOVA tests and regression models. Nasolabial asymmetry increased with growing severity of the cleft (p = 0.029). Overall, nasolabial appearance was affected by nasolabial asymmetry; subjects with more nasolabial asymmetry were judged as having a less esthetically pleasing nasolabial area (p < 0.001). However, the relationship between nasolabial symmetry and esthetics was relatively weak in subjects with UCLAP, in whom only vermilion border esthetics was associated with asymmetry. Nasolabial symmetry assessed with 3D facial imaging can be used as an objective measure of treatment outcome in subjects with less severe cleft deformity. In subjects with more severe cleft types, other factors may play a decisive role. Assessment of nasolabial symmetry is a useful measure of treatment success in less severe cleft types.

Multi-contrast multi-scale surface registration for improved alignment of cortical areas

The position of cortical areas can be approximately predicted from cortical surface folding patterns. However, there is extensive inter-subject variability in cortical folding patterns, prohibiting a one-to-one mapping of cortical folds in certain areas. In addition, the relationship between cortical area boundaries and the shape of the cortex is variable, and weaker for higher-order cortical areas. Current surface registration techniques align cortical folding patterns using sulcal landmarks or cortical curvature, for instance. The alignment of cortical areas by these techniques is thus inherently limited by the sole use of geometric similarity metrics. Magnetic resonance imaging T1 maps show intra-cortical contrast that reflects myelin content, and thus can be used to improve the alignment of cortical areas. In this article, we present a new symmetric diffeomorphic multi-contrast multi-scale surface registration (MMSR) technique that works with partially inflated surfaces in the level-set framework. MMSR generates a more precise alignment of cortical surface curvature in comparison to two widely recognized surface registration algorithms. The resulting overlap in gyrus labels is comparable to FreeSurfer. Most importantly, MMSR improves the alignment of cortical areas further by including T1 maps. As a first application, we present a group average T1 map at a uniquely high-resolution and multiple cortical depths, which reflects the myeloarchitecture of the cortex. MMSR can also be applied to other MR contrasts, such as functional and connectivity data.

Markerless Augmented Reality via Stereo Video See-Through Head-Mounted Display Device

Conventionally, the camera localization for augmented reality (AR) relies on detecting a known pattern within the captured images. In this study, a markerless AR scheme has been designed based on a Stereo Video See-Through Head-Mounted Display (HMD) device. The proposed markerless AR scheme can be utilized for medical applications such as training, telementoring, or preoperative explanation. Firstly, a virtual model for AR visualization is aligned to the target in physical space by an improved Iterative Closest Point (ICP) based surface registration algorithm, with the target surface structure reconstructed by a stereo camera pair; then, a markerless AR camera localization method is designed based on the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm and the Random Sample Consensus (RANSAC) correction algorithm. Our AR camera localization method is shown to be better than the traditional marker-based and sensor-based AR environment. The demonstration system was evaluated with a plastic dummy head and the display result is satisfactory for a multiple-view observation.

Exact surface registration of retinal surfaces from 3-D optical coherence tomography images.

Nonrigid registration of optical coherence tomography (OCT) images is an important problem in studying eye diseases, evaluating the effect of pharmaceuticals in treating vision loss, and performing group-wise cross-sectional analysis. High dimensional nonrigid registration algorithms required for cross-sectional and longitudinal analysis are still being developed for accurate registration of OCT image volumes, with the speckle noise in images presenting a challenge for registration. Development of algorithms for segmentation of OCT images to generate surface models of retinal layers has advanced considerably and several algorithms are now available that can segment retinal OCT images into constituent retinal surfaces. Important morphometric measurements can be extracted if accurate surface registration algorithm for registering retinal surfaces onto corresponding template surfaces were available. In this paper, we present a novel method to perform multiple and simultaneous retinal surface registration, targeted to registering surfaces extracted from ocular volumetric OCT images. This enables a point-to-point correspondence (homology) between template and subject surfaces, allowing for a direct, vertex-wise comparison of morphometric measurements across subject groups. We demonstrate that this approach can be used to localize and analyze regional changes in choroidal and nerve fiber layer thickness among healthy and glaucomatous subjects, allowing for cross-sectional population wise analysis. We also demonstrate the method's ability to track longitudinal changes in optic nerve head morphometry, allowing for within-individual tracking of morphometric changes. This method can also, in the future, be used as a precursor to 3-D OCT image registration to better initialize nonrigid image registration algorithms closer to the desired solution.