Automatic Registration Method Research Articles

Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features.

We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual interventions in control point-based registration methods. First, the rigorous registration model between the LiDAR points and the panoramic/fish-eye image was built. Second, skyline pixels from panoramic/fish-eye images and skyline points from the MMS’s LiDAR points were extracted, relying on the difference in the pixel values and the registration model, respectively. Third, a brute force optimization method was used to search for optimal matching parameters between skyline pixels and skyline points. In the experiments, the original registration method and the control point registration method were used to compare the accuracy of our method with a sequence of panoramic/fish-eye images. The result showed: (1) the panoramic/fish-eye image registration model is effective and can achieve high-precision registration of the image and the MMS’s LiDAR points; (2) the skyline-based registration method can automatically optimize the initial attitude parameters, realizing a high-precision registration of a panoramic/fish-eye image and the MMS’s LiDAR points; and (3) the attitude correction values of the sequences of panoramic/fish-eye images are different, and the values must be solved one by one.

Selection Strategies for Atlas-Based Mosaicing of Left Atrial 3-D Transesophageal Echocardiography Data

Three-dimensional transesophageal echocardiography (TEE) provides real-time soft tissue information, but its use is hampered by its limited field of view. The mosaicing of multiple TEE views makes it possible to visualize a large structure, like the left atrium, in a single volume. To this end, an automatic registration method is required. Similarly to atlas-based segmentation approaches, atlas-based mosaicing (ABM) uses a full volume atlas set to moderate the onerous registration of the individual TEE views. The performance of ABM depends both on the quality of the involved registrations and on the selection of the optimal transformation from the candidate transformations that result from the various atlases. The study described here explored the performance of different selection strategies on multiview TEE data of the left atrium. We found that by incorporating two stages of transformation selection, using the image similarity and the conformity between the candidate transformations as selection criteria, the average registration error dropped below 3 mm with respect to manual registration of these data. Finally, we used this method for the automatic construction of a wide-view TEE volume of the left atrium.

A preliminary clinical study of automatic registration ultrasound-CT/MR fusion imaging based on liver vessel trees

Objective To explore the feasibility and convenience of automatic registration ultrasound-CT/MR fusion imaging based on hepatic vessel trees. Methods The PercuNav fusion imaging system of Philips Epiq 7 was used to perform ultrasound-CT/MR fusion imaging on 22 patients with focal liver lesions detected by contrast-enhanced CT or MR. Both automatic registration ultrasound-CT/MR fusion imaging based on hepatic vessel trees and the conventional ultrasound-CT/MR fusion imaging based on internal anatomic landmarks were employed for alignment in these patients. The results including the success rate of registration, duration time of initial registration, error of initial registration, number of times of fine-tuning, duration time of fine-tuning and the overall duration time of registration were compared between these two methods. Results The success rates of registration, duration time of initial registration, errors of initial registration, numbers of times of fine-tuning, duration time of fine-tuning and the overall duration time of registration for automatic registration ultrasound-CT/MR fusion imaging based on hepatic vessel trees and the conventional ultrasound-CT/MR fusion imaging based on internal anatomic landmarks were 72.73% and 95.45%, 16.5 s (10~30 s) and 13 s (8~24 s), 3 mm (1~14 mm) and 14 mm (2~43 mm), 0 time (0 to 2 times) and 1 time (0~3 times), 0 s(0~46 s) and 30 s (0~88 s), and 20 s (12~61 s) and 42 s (9~102 s), successively and respectively. There was no statistically significant difference in the success rates between these two methods (P>0.05). The duration time of initial registration of conventional method was less than that of automatic registration method (P<0.05). The error of initial registration, number of times of fine-tuning, duration time of fine-tuning and the overall duration time of registration of automatic registration method were superior to those of conventional method (P<0.05). Conclusions Automatic registration ultrasound-CT/MR fusion imaging based on hepatic vessel trees is feasible. It is also more convenient than conventional ultrasound-CT/MR fusion imaging based on internal anatomic landmarks. Key words: Ultrasonography; Fusion imaging; Liver; Automatic registration; Hepatic vessel trees

4-Plane congruent sets for automatic registration of as-is 3D point clouds with 3D BIM models

Abstract Construction quality and progress control are demanding, yet critical construction activities. Building Information Models and as-built scanned data can be used in Scan-vs-BIM processes to effectively and comprehensively support these activities. This however requires accurate registration of scanned point clouds with 3D (BIM) models. Automating such registration remains a challenge in the context of the built environment, because as-built can be incomplete and/or contain data from non-model objects, and construction buildings and other structures often present symmetries and self-similarities that are very challenging to registration. In this paper, we present a novel automatic coarse registration method that is an adaptation of the ‘4 Points Congruent Set’ algorithm to the use of planes; we call it the ‘4-Plane Congruent Set’ (4-PlCS) algorithm. The approach is further integrated in a software system that delivers not one but a ranked list of the most likely transformations, so to allow the user to quickly select the correct transformation, if need be. Two variants of the method are also considered, in particular one in the case when the vertical axis is known a priori; we call that method the 4.5-PlCS method. The proposed algorithm is tested using five different datasets, including three simulated and two real-life ones. The results show the effectiveness of the proposed method, where the correct transformation always ranks very high (in our experiments, first or second), and is extremely close to the ground-truth transformation. Experimental comparison of the proposed approach with a standard, more intuitive approach based on finding 3-plane congruent sets shows the discriminatory power of 4-plane bases over 3-plane bases, albeit at no clear benefits in terms of computational time. The experimental results for the 4.5-PlCS method show that it delivers a non-negligible reduction in computational time (approx. 20%), but at no additional benefit in terms of effectiveness in finding the correct transformation.

A novel contour-based registration of lateral cephalogram and profile photograph

A contour-based automatic registration method of lateral cephalograms and profile photographs is proposed to facilitate the accurate measurement of anteroposterior (AP) position of maxillary central incisors relative to the forehead. There are mainly three steps in the proposed method. First, a hierarchical contour detection algorithm is employed to obtain contours of forehead and nose in lateral cephalograms and profile photographs, respectively. Curve deviation around Nasion of Soft Tissue (Ns) is further corrected using an iterative polynomial curve fitting algorithm according to the characteristics of cephalograms. Second, the coarse registration is performed based on four landmark pairs using a routine least squares method for each image pair, and then the contours are roughly matched. Finally, the coarse registration is further refined by applying the coherent point drift (CPD) algorithm to all the contour points. Using image pairs of lateral cephalograms and profile photographs from 49 Chinese patients, we show that the proposed method achieves higher performance in terms of accuracy and robustness than the existing methods.

Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features

Inaccurate exterior orientation parameters (EoPs) between sensors obtained by pre-calibration leads to failure of registration between panoramic image sequence and mobile laser scanning data. To address this challenge, this paper proposes an automatic registration method based on semantic features extracted from panoramic images and point clouds. Firstly, accurate rotation parameters between the panoramic camera and the laser scanner are estimated using GPS and IMU aided structure from motion (SfM). The initial EoPs of panoramic images are obtained at the same time. Secondly, vehicles in panoramic images are extracted by the Faster-RCNN as candidate primitives to be matched with potential corresponding primitives in point clouds according to the initial EoPs. Finally, translation between the panoramic camera and the laser scanner is refined by maximizing the overlapping area of corresponding primitive pairs based on the Particle Swarm Optimization (PSO), resulting in a finer registration between panoramic image sequences and point clouds. Two challenging urban scenes were experimented to assess the proposed method, and the final registration errors of these two scenes were both less than three pixels, which demonstrates a high level of automation, robustness and accuracy.

Mapping field photographs to textured surface meshes directly on mobile devices

AbstractThe mapping of photographs to surface geometry is an important procedure for many applications within the geosciences. This paper proposes an interactive framework for feature‐based image‐to‐geometry mapping that works directly on mobile devices, under challenging imaging conditions and with limited available hardware performance. The framework makes use of openly available digital elevation models (DEMs) together with mobile position‐and‐orientation sensor data. It integrates calculation heuristics for result evaluation and feedback, synthesising available knowledge in current registration literature. The approach is assessed on two image datasets captured on separate occasions. Their interpretations are mapped to one textured lidar surface model and the projection accuracy is qualitatively assessed. The experiments show a significant accuracy improvement in photograph registration results, as well as the faithful mapping of image interpretations on the underlying surface geometry. This semi‐automatic, user‐guided, interactive approach is superior to comparable fully automatic registration methods.

An automatic markerless registration method for neurosurgical robotics based on an optical camera.

Current markerless registration methods for neurosurgical robotics use the facial surface to match the robot space with the image space, and acquisition of the facial surface usually requires manual interaction and constrains the patient to a supine position. To overcome these drawbacks, we propose a registration method that is automatic and does not constrain patient position. An optical camera attached to the robot end effector captures images around the patient's head from multiple views. Then, high coverage of the head surface is reconstructed from the images through multi-view stereo vision. Since the acquired head surface point cloud contains color information, a specific mark that is manually drawn on the patient's head prior to the capture procedure can be extracted to automatically accomplish coarse registration rather than using facial anatomic landmarks. Then, fine registration is achieved by registering the high coverage of the head surface without relying solely on the facial region, thus eliminating patient position constraints. The head surface was acquired by the camera with a good repeatability accuracy. The average target registration error of 8 different patient positions measured with targets inside a head phantom was [Formula: see text], while the mean surface registration error was [Formula: see text]. The method proposed in this paper achieves automatic markerless registration in multiple patient positions and guarantees registration accuracy inside the head. This method provides a new approach for establishing the spatial relationship between the image space and the robot space.

High thickness histological sections as alternative to study the three-dimensional microscopic human sub-cortical neuroanatomy.

Stereotaxy is based on the precise image-guided spatial localization of targets within the human brain. Even with the recent advances in MRI technology, histological examination renders different (and complementary) information of the nervous tissue. Although several maps have been selected as a basis for correlating imaging results with the anatomical locations of sub-cortical structures, technical limitations interfere in a point-to-point correlation between imaging and anatomy due to the lack of precise correction for post-mortem tissue deformations caused by tissue fixation and processing. We present an alternative method to parcellate human brain cytoarchitectural regions, minimizing deformations caused by post-mortem and tissue-processing artifacts and enhancing segmentation by means of modified high thickness histological techniques and registration with MRI of the same specimen and into MNI space (ICBM152). A three-dimensional (3D) histological atlas of the human thalamus, basal ganglia, and basal forebrain cholinergic system is displayed. Structure's segmentations were performed in high-resolution dark-field and light-field microscopy. Bidimensional non-linear registration of the histological slices was followed by 3D registration with in situ MRI of the same subject. Manual and automated registration procedures were adopted and compared. To evaluate the quality of the registration procedures, Dice similarity coefficient and normalized weighted spectral distance were calculated and the results indicate good overlap between registered volumes and a small shape difference between them in both manual and automated registration methods. High thickness high-resolution histological slices in combination with registration to in situ MRI of the same subject provide an effective alternative method to study nuclear boundaries in the human brain, enhancing segmentation and demanding less resources and time for tissue processing than traditional methods.

A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system

Background: Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty.Methods: A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration.Results: Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18–26 mm) and different support radii (2–6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm.Conclusion: This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

AUTOMATED COARSE REGISTRATION OF POINT CLOUDS IN 3D URBAN SCENES USING VOXEL BASED PLANE CONSTRAINT

Abstract. For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

Comparison of image registration methods for composing spectral retinal images

Abstract Spectral retinal images have significant potential for improving the early detection and visualization of subtle changes due to eye diseases and many systemic diseases. High resolution in both the spatial and the spectral domain can be achieved by capturing a set of narrow-band channel images from which the spectral images are composed. With imaging techniques where the eye movement between the acquisition of the images is unavoidable, image registration is required. As manual registration of the channel images is laborious and prone to error, a suitable automatic registration method is necessary. In this paper, the applicability of a set of image registration methods for the composition of spectral retinal images is studied. The registration methods are quantitatively compared using synthetic channel image data of an eye phantom and a semisynthetic set of retinal channel images generated by using known transformations. The experiments show that generalized dual-bootstrap iterative closest point method outperforms the other evaluated methods in registration accuracy, measured in pixel error, and the number of successful registrations.

An automatic and accurate registration method for electro-anatomical map and CT surface.

Electro-anatomical maps (EAM) and CT surface registration are widely used for catheter navigation in atrial fibrillation ablations. However, few studies have investigated the registration algorithm. Moreover, some of them are semiautomatic, so that physicians must be proficient; some are inaccurate for catheter navigation. A both automatic and accurate registration method is needed. A Hausdorff distance based approach (HD) was proposed for EAM/CT registration. First, using principal axes based registration, EAM/CT pairs were coarsely aligned. Then, using Hausdorff distance as the fine objective function, EAM/CT pairs were finely aligned. Six real EAM/CT pairs were collected from five patients and 38 simulated pairs were generated. Each pair was aligned using Carto-Merge, a stochastic approach (SA) and HD. Considering the balance of operability, accuracy and robustness, HD obtained the best EAM/CT registration results among the three approaches. Experiments validate that the proposed method registers EAM and CT surface both automatically and accurately.

Multilevel active registration for kinect human body scans: from low quality to high quality

Registration of 3D human body has been a challenging research topic for over decades. Most of the traditional human body registration methods require manual assistance, or other auxiliary information such as texture and markers. The majority of these methods are tailored for high-quality scans from expensive scanners. Following the introduction of the low-quality scans from cost-effective devices such as Kinect, the 3D data capturing of human body becomes more convenient and easier. However, due to the inevitable holes, noises and outliers in the low-quality scan, the registration of human body becomes even more challenging. To address this problem, we propose a fully automatic active registration method which deforms a high-resolution template mesh to match the low-quality human body scans. Our registration method operates on two levels of statistical shape models: (1) the first level is a holistic body shape model that defines the basic figure of human; (2) the second level includes a set of shape models for every body part, aiming at capturing more body details. Our fitting procedure follows a coarse-to-fine approach that is robust and efficient. Experiments show that our method is comparable with the state-of-the-art methods for high-quality meshes in terms of accuracy and it outperforms them in the case of low-quality scans where noises, holes and obscure parts are prevalent.

An automatic 3D point cloud registration method based on regional curvature maps

3D point cloud registration is a fundamental and critical issue in 3D reconstruction and object recognition. Most of the existing methods are based on local shape descriptor. In this paper, we propose a discriminative and robust local shape descriptor-Regional Curvature Map (RCM). The keypoint and its neighboring points are firstly projected onto a 2D plane according to a robust mapping against normal errors. Then, the projection points are quantized into corresponding bins of the 2D support region and their weighted curvatures are encoded into a curvature distribution image. Based on the RCM, an efficient and accurate 3D point cloud registration method is presented. We firstly find 3D point correspondences by a RCM searching and matching strategy based on the sub-regions of the RCM. Then, a coarse registration can be achieved with geometrically consistent point correspondences, followed by a fine registration based on a modified iterative closest point (ICP) algorithm. The experimental results demonstrate that the RCM is distinctive and robust against normal errors and varying point cloud density. The corresponding registration method can achieve a higher registration precision and efficiency compared with two existing registration methods.

Ultrasound fusion image error correction using subject-specific liver motion model and automatic image registration

PurposeUltrasound fusion imaging is an emerging tool and benefits a variety of clinical applications, such as image-guided diagnosis and treatment of hepatocellular carcinoma and unresectable liver metastases. However, respiratory liver motion-induced misalignment of multimodal images (i.e., fusion error) compromises the effectiveness and practicability of this method. The purpose of this paper is to develop a subject-specific liver motion model and automatic registration-based method to correct the fusion error. MethodsAn online-built subject-specific motion model and automatic image registration method for 2D ultrasound-3D magnetic resonance (MR) images were combined to compensate for the respiratory liver motion. The key steps included: 1) Build a subject-specific liver motion model for current subject online and perform the initial registration of pre-acquired 3D MR and intra-operative ultrasound images; 2) During fusion imaging, compensate for liver motion first using the motion model, and then using an automatic registration method to further correct the respiratory fusion error. Evaluation experiments were conducted on liver phantom and five subjects. ResultsIn the phantom study, the fusion error (superior-inferior axis) was reduced from 13.90±2.38mm to 4.26±0.78mm by using the motion model only. The fusion error further decreased to 0.63±0.53mm by using the registration method. The registration method also decreased the rotation error from 7.06±0.21° to 1.18±0.66°. In the clinical study, the fusion error was reduced from 12.90±9.58mm to 6.12±2.90mm by using the motion model alone. Moreover, the fusion error decreased to 1.96±0.33mm by using the registration method. ConclusionsThe proposed method can effectively correct the respiration-induced fusion error to improve the fusion image quality. This method can also reduce the error correction dependency on the initial registration of ultrasound and MR images. Overall, the proposed method can improve the clinical practicability of ultrasound fusion imaging.

Automated registration and enhanced processing of clinical optical coherence tomography angiography.

Motion artifacts degrade the quality of optical coherence tomography angiography (OCTA). Orthogonal registration can eliminate the majority of these artifacts, but some artifacts persist in most clinical images. We evaluate an automated registration algorithm with selective merging and filtering to remove remaining artifacts and improve the quality of images. A 70 kHz commercial spectral domain OCT was used to obtain 3 mm × 3 mm OCTA in 10 healthy, 5 age-related macular degeneration (AMD), and 31 diabetic retinopathy (DR) participants. Projection artifacts were removed and images were segmented into 3 inner retinal plexuses. Amplitude thresholding identified lines containing a residual artifact and correlation between neighboring lines identified distorted stripes. Then the angiograms were registered and the lines selectively merged. A vesselness filter was applied to the resulting images. The images were evaluated for signal-to-noise ratio (SNR), image entropy, vessel connectivity and vessel density. Registration and selective merging (RSM) algorithm improved the SNR (P<0.02) compared to orthogonal registration alone. RSM with vesselness filter increased the image entropy (P<10-8) and reduced inter-subject variability (standard error ≤3%, n=10) in healthy eyes. The method improved vessel details and connectivity in OCTA of healthy, DR and neovascular AMD eyes. This automated registration method eliminates residual motion artifacts and enhances the visualization of vessels in OCTA.

COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6&amp;thinsp;mm and 9.5&amp;thinsp;mm respectively, which are adequate for fine registration.

COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS

Abstract. Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

SU‐F‐J‐177: A Novel Image Analysis Technique (center Pixel Method) to Quantify End‐To‐End Tests

Purpose:To implement a novel image analysis technique, “center pixel method”, to quantify end‐to‐end tests accuracy of a frameless, image guided stereotactic radiosurgery system.Methods:The localization accuracy was determined by delivering radiation to an end‐to‐end prototype phantom. The phantom was scanned with 0.8 mm slice thickness. The treatment isocenter was placed at the center of the phantom. In the treatment room, CBCT images of the phantom (kVp=77, mAs=1022, slice thickness 1 mm) were acquired to register to the reference CT images. 6D couch correction were applied based on the registration results. Electronic Portal Imaging Device (EPID)‐based Winston Lutz (WL) tests were performed to quantify the errors of the targeting accuracy of the system at 15 combinations of gantry, collimator and couch positions. The images were analyzed using two different methods. a) The classic method. The deviation was calculated by measuring the radial distance between the center of the central BB and the full width at half maximum of the radiation field. b) The center pixel method. Since the imager projection offset from the treatment isocenter was known from the IsoCal calibration, the deviation was determined between the center of the BB and the central pixel of the imager panel.Results:Using the automatic registration method to localize the phantom and the classic method of measuring the deviation of the BB center, the mean and standard deviation of the radial distance was 0.44 ± 0.25, 0.47 ± 0.26, and 0.43 ± 0.13 mm for the jaw, MLC and cone defined field sizes respectively. When the center pixel method was used, the mean and standard deviation was 0.32 ± 0.18, 0.32 ± 0.17, and 0.32 ± 0.19 mm respectively.Conclusion:Our results demonstrated that the center pixel method accurately analyzes the WL images to evaluate the targeting accuracy of the radiosurgery system.The work was supported by a Research Scholar Grant, RSG‐15‐137‐01‐CCE from the American Cancer Society.