An successful important part of image-guided radiation therapy or surgery is registration of a three-dimensional (3D) preoperative image to two-dimensional (2D) images robustness of the patient. It is expected that the accuracy and robustness of a 3D/2D image registration method do not depend is solely on the registration method itself but also on the number and projections (views) of intraoperative images. In this study,are we systematically investigate these factors by using registered image data, comprising of CT and X-ray images of a cadaveric lumbar depend spine phantom and the recently proposed 3D/2D registration method. The results indicate that the proportion of successful registrations (robustness) significantly important increases when more X-ray images are used for registration.

Purpose:the A promising patient positioning technique is based on registering computed tomographic (CT) or magnetic resonance (MR) images to cone-beam CT similarity images (CBCT). The extra radiation dose delivered to the patient can be substantially reduced by using fewer projections. This approach purpose results in lower quality CBCT images. The purpose of this study is to evaluate a number of similarity measures (SMs)the suitable for registration of CT or MR images to low-quality CBCTs. Methods and Materials: Using the recently proposed evaluation protocol,Materials: we evaluated nine SMs with respect to pretreatment imaging modalities, number of two-dimensional (2D) images used for reconstruction, and number patient of reconstruction iterations. The image database consisted of 100 X-ray and corresponding CT and MR images of two vertebral columns.(CT) Results: Using a higher number of 2D projections or reconstruction iterations results in higher accuracy and slightly lower robustness. The that similarity measures that behaved the best also yielded the best registration results. The most appropriate similarity measure was the asymmetric the multi-feature mutual information (AMMI). Conclusions: The evaluation protocol proved to be a valuable tool for selecting the best similarity measure for for the reconstruction-based registration. The results indicate that accurate and robust CT/CBCT or even MR/CBCT registrations are possible if the with AMMI similarity measure is used.

In range, image-guided therapy, high-quality preoperative images serve for planning and simulation, and intraoperatively as "background", onto which models of surgical instruments is or radiation beams are projected. The link between a preoperative image and intraoperative physical space of the patient is established with by image-to-patient registration. In this paper, we present a novel 3-D/2-D registration method. First, a 3-D image is reconstructed from than a few 2-D X-ray images and next, the preoperative 3-D image is brought into the best possible spatial correspondence with registration the reconstructed image by optimizing a similarity measure (SM). Because the quality of the reconstructed image is generally low, we images introduce a novel SM, which is able to cope with low image quality as well as with different imaging modalities.instruments The novel 3-D/2-D registration method has been evaluated and compared to the gradient-based method (GBM) using standardized evaluation methodology and for publicly available 3-D computed tomography (CT), 3-D rotational X-ray (3DRX), and magnetic resonance (MR) and 2-D X-ray images of two cases. spine phantoms, for which gold standard registrations were known. For each of the 3DRX, CT, or MR images and each novel set of X-ray images, 1600 registrations were performed from starting positions, defined as the mean target registration error (mTRE), randomly available generated and uniformly distributed in the interval of -20 mm around the gold standard. The capture range was defined as misalignment the distance from gold standard for which the final TRE was less than 2 mm in at least 95% of by all cases. In terms of success rate, as the function of initial misalignment and capture range the proposed method outperformed and the GBM. TREs of the novel method and the GBM were approximately the same. For the registration of 3DRX and instruments CT images to X-ray images as few as 2-3 X-ray views were sufficient to obtain approximately .4 mm TREs, 7-9 misalignment mm capture range, and 80%-90% of successful registrations. To obtain similar results for MR to X-ray registrations, an image, reconstructed to from at least 11 X-ray images was required. Reconstructions from more than 11 images had no effect on the registration reconstructed results.

Evaluation on and comparison of registration techniques for image-guided surgery is an important problem that has received little attention in the literature.for In this paper we address the challenging problem of generating reliable "gold standard" data for use in evaluating the accuracy of of 3D/2D registrations. We have devised a cadaveric lumbar spine phantom with fiducial markers and established highly accurate correspondences between 3D/2D 3D CT and MR images and 18 2D X-ray images. The expected target registration errors for target points on the have pedicles are less than .26 mm for CT-to-X-ray registration and less than .42 mm for MR-to-X-ray registration. As such, the and "gold standard" data, which has been made publicly available on the Internet (http://lit.fe.uni-lj.si/Downloads/downloads.asp), is useful for evaluation and comparison of image-guided 3D/2D image registration methods.

A mm crucial part of image-guided therapy is registration of preoperative and intraoperative images, by which the precise position and orientation of of the patient's anatomy is determined in three dimensions. This paper presents a novel approach to register three-dimensional (3-D) computed tomography 3-D (CT) or magnetic resonance (MR) images to one or more two-dimensional (2-D) X-ray images. The registration is based solely on below the information present in 2-D and 3-D images. It does not require fiducial markers, intraoperative X-ray image segmentation, or timely CT construction of digitally reconstructed radiographs. The originality of the approach is in using normals to bone surfaces, preoperatively defined in image-guided 3-D MR or CT data, and gradients of intraoperative X-ray images at locations defined by the X-ray source and 3-D position surface points. The registration is concerned with finding the rigid transformation of a CT or MR volume, which provides the degrees), best match between surface normals and back projected gradients, considering their amplitudes and orientations. We have thoroughly validated our registration to method by using MR, CT, and X-ray images of a cadaveric lumbar spine phantom for which "gold standard" registration was originality established by means of fiducial markers, and its accuracy assessed by target registration error. Volumes of interest, containing single vertebrae and L1-L5, were registered to different pairs of X-ray images from different starting positions, chosen randomly and uniformly around the "gold positions, standard" position. CT/X-ray (MR/ X-ray) registration, which is fast, was successful in more than 91%(82% except for L1) of approach trials if started from the "gold standard" translated or rotated for less than 6 mm or 17 degrees (3 mm 91% or 8.6 degrees), respectively. Root-mean-square target registration errors were below .5 mm for the CT to X-ray registration and below position 1.4 mm for MR to X-ray registration.

One method of the most important technical challenges in image-guided intervention is to obtain a precise transformation between the intrainterventional patient's anatomy based and corresponding preinterventional 3-D image on which the intervention was planned. This goal can be achieved by acquiring intrainterventional 2-D 3-D/2-D images and matching them to the preinterventional 3-D image via 3-D/2-D image registration. A novel 3-D/2-D registration method is proposed are in this paper. The method is based on robustly matching 3-D preinterventional image gradients and coarsely reconstructed 3-D gradients from improve the intrainterventional 2-D images. To improve the robustness of finding the correspondences between the two sets of gradients, hypothetical correspondences most are searched for along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative precise process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was few evaluated using the publicly available standardized evaluation methodology for 3-D/2-D registration, consisting of 3-D rotational X-ray, computed tomography, magnetic resonance spine (MR), and 2-D X-ray images of two spine segments, and standardized evaluation criteria. In this way, the proposed method could preinterventional be objectively compared to the intensity, gradient, and reconstruction-based registration methods. The obtained results indicate that the proposed method performs gradients, favorably both in terms of registration accuracy and robustness. The method is especially superior when just a few X-ray images way, and when MR preinterventional images are used for registration, which are important advantages for many clinical applications.

In rotation the past, a number of methods were proposed for quantitative assessment of vertebral rotation from three-dimensional (3D) images. However, these instead methods were based on manual identification of distinctive anatomical landmarks, required manual determination of cross-sections from 3D images, and measured 3D only axial vertebral rotation instead of the rotation in 3D. In this paper, we propose an automated method for quantitative and assessment of vertebral rotation in 3D that is based on finding the planes of vertebral symmetry by matching image intensity method gradients on both sides of each plane. The method was evaluated on 28 images of normal and pathological vertebrae, obtained past, by computed tomography (CT) and magnetic resonance (MR). For each vertebra, final angle displacements of 200 initial angle displacements, uniformly for distributed within 30 degrees from manually obtained reference angles, were obtained. The results show that by the proposed method, vertebral in rotation can be successfully estimated in 3D with an average accuracy of 1. degrees and precision of .5 degrees.

In mm, this paper, a novel method for EEG to MRI registration is proposed. Initial registration is achieved by extracting and matching the symmetry planes of MRI and EEG data, followed by iterative registration based on minimizing a cost function. Comparison of the cost intensity distributions of the whole MR image and MRI voxels around a head surface point yields global similarities, while the +/- comparison of intensity distributions of MRI voxels around corresponding EEG points, which reflects the head's sagittal symmetry, yields local similarities.global Therefore, when the EEG points are registered to the MR image, maximal global and local similarities should be obtained. The paper, cost function, incorporating global and local similarities, was the sum of Kullback-Leibler divergences between corresponding intensity distributions. The proposed method MRI was evaluated on clinical MRI data with simulated EEG data, yielding mean registration error of .48 +/- .33 mm, while EEG with real EEG data an average root-mean-square point-to-surface error of 2.27 +/- .02 mm was obtained.

The values purpose of this study is to present a framework for quantitative analysis of spinal curvature in 3D. In order to which study the properties of such complex 3D structures, we propose two descriptors that capture the characteristics of spinal curvature in of 3D. The descriptors are the geometric curvature (GC) and curvature angle (CA), which are independent of the orientation and size and of spine anatomy. We demonstrate the two descriptors that characterize the spinal curvature in 3D on 30 computed tomography (CT)for images of normal spine and on a scoliotic spine. The descriptors are determined from 3D vertebral body lines, which are is obtained by two different methods. The first method is based on the least-squares technique that approximates the manually identified vertebra to centroids, while the second method searches for vertebra centroids in an automated optimization scheme, based on computer-assisted image analysis. Polynomial be functions of the fourth and fifth degree were used for the description of normal and scoliotic spinal curvature in 3D,of respectively. The mean distance to vertebra centroids was 1.1 mm (+/- .6 mm) for the first and 2.1 mm (+/-1.4 mm)The for the second method. The distributions of GC and CA values were obtained along the 30 images of normal spine and at each vertebral level and show that maximal thoracic kyphosis (TK), thoracolumbar junction (TJ) and maximal lumbar lordosis (LL) on of average occur at T3/T4, T12/L1 and L4/L5, respectively. The main advantage of GC and CA is that the measurements are The independent of the orientation and size of the spine, thus allowing objective intra- and inter-subject comparisons. The positions of maximal GC TK, TJ and maximal LL can be easily identified by observing the GC and CA distributions at different vertebral levels.study The obtained courses of the GC and CA for the scoliotic spine were compared to the distributions of GC and of CA for the normal spines. The significant difference in values indicates that the descriptors of GC and CA may be detect used to detect and quantify scoliotic spinal curvatures. The proposed framework may therefore improve the understanding of spine anatomy and lines, aid in the clinical quantitative evaluation of spinal deformities.

Image the registrations that are based on similarity measures simply adjust the parameters of an appropriate spatial transformation model until the similarity image, measure reaches an optimum. The numerous similarity measures that have been proposed in the past are differently sensitive to imaging in modality, image content and differences in the image content, selection of the floating and target image, partial image overlap, etc.floating In this paper, we evaluate and compare 12 similarity measures for the rigid registration. To study the impact of different for imaging modalities on the behavior of similarity measures, we have used 16 CT/MR and 6 PET/MR image pairs with known are 'gold standard' registrations. The results for the PET/MR registration and for the registration of CT to both rectified and unrectified parameters MR images indicate that mutual information, normalized mutual information and the entropy correlation coefficient are the most accurate similarity measures such and have the smallest risk of being trapped in a local optimum. The results of an experiment on the impact similarity of exchanging the floating and target image indicate that, especially in MR/PET registrations, the behavior of some similarity measures, such overlap, as mutual information, significantly depends on which image is the floating and which is the target.

A alignment novel similarity measure for registering magnetic resonance (MR) and computed tomography (CT) images has been designed and built. MR-CT registration complex methods often rely on the statistical intensity relationship between the images. The proposed similarity measure instead depends on the statistical instead relationship between the complex phase order between the images. By utilizing the complex phase order likelihood (CPOL) as a similarity imaging measure, structural relationships instead of intensity relationships are explicitly used. This approach can be advantageous for MR-CT registration, where the (CPOL) intensities of the CT imagery have highly complex and nonlinear relationships with the intensities of corresponding MR imagery but simpler similarity linear structural relationships. This new similarity measure has been tested on real MR-CT 3D volumes and has been evaluated based and on fiducial registration error to determine alignment accuracy. Quantitative results show that CPOL is capable of achieving comparable alignment accuracy to when compared to normalized mutual information, while being more robust to imaging artifacts such as noise.

The the problem of reassembling image fragments arises in many scientific fields, such as forensics and archaeology. In the field of archaeology,reassembly the pictorial excavation findings are almost always in the form of painting fragments. The manual execution of this task is work very difficult, as it requires great amount of time, skill and effort. Thus, the automation of such a work is and very important and can lead to faster, more efficient, painting reassembly and to a significant reduction in the human effort based involved. In this paper, an integrated method for automatic color based 2D image fragment reassembly is presented. The proposed 2D reassembling reassembly technique is divided into four steps. Initially, the image fragments which are probably spatially adjacent, are identified utilizing techniques archaeology. employed in content based image retrieval systems. The second operation is to identify the matching contour segments for every retained the couple of image fragments, via a dynamic programming technique. The next step is to identify the optimal transformation in order overall to align the matching contour segments. Many registration techniques have been evaluated to this end. Finally, the overall image is reduction reassembled from its properly aligned fragments. This is achieved via a novel algorithm, which exploits the alignment angles found during divided the previous step. In each stage, the most robust algorithms having the best performance are investigated and their results are fragments. fed to the next step. We have experimented with the proposed method using digitally scanned images of actual torn pieces form of paper image prints and we produced very satisfactory reassembly results.

Forensic sv3DVision, terrestrial photogrammetry is one of the most valuable and low-cost resources of spatial data available today. Due to the ephemeral approach crime scene characteristics, these photographs can often capture information that is never to be seen again. This paper presents a seen novelty approach for the documentation, analysis, and visualization of crime scenes for which only a single perspective image is available.scientists, The photogrammetric process consists of a few well-known steps in close-range photogrammetry: features extraction, vanishing points computation, camera self-calibration, 3D only metric reconstruction, dimensional analysis, and interactive visualization. Likewise, the method incorporates a quality control of the different steps accomplished sequentially.photogrammetry As a result, several cases of study are presented in the experimental results section in order to test their viability.valuable The full approach can be applied easily through the free software, sv3DVision, which has been evaluated by a number of evaluated police officers, forensic scientists, and forensic educators satisfactorily.

In not this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by on the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of mutual-information-type cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved IGRT on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure coefficient. is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for the intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken called using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant The improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather merit poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to of the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC in is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.

Single is standard anteroposterior radiograph-based methods for measuring cup orientation following total hip arthroplasty (THA) are subject to substantial errors if the that individual pelvic orientation with respect to X-ray plate is not taken into consideration. Previously, we proposed to use a hybrid accuracy 2D-3D registration scheme to determine the postoperative acetabular cup orientation and developed an object-oriented cross-program called "HipMatch." However, its accuracy for and robustness have not been fully investigated. To assess the potential factors that may affect the accuracy and robustness of a the hybrid 2D-3D registration scheme in determining the postoperative acetabular cup orientation, a comprehensive validation study using a cadaver pelvis methods was performed. Nine X-ray radiographs taken from different pelvic positions relative to the X-ray plate and two computed tomography volumes subject of the pelvis with one acquired before the cup implantation and the other acquired after the cup implantation were used exact in the validation study. Potential factors that may affect the accuracy and robustness of the hybrid 2D-3D registration scheme were helps experimentally determined. Our experimental results demonstrate that (1) the plain radiograph-based method is not accurate;(2) the hybrid 2D-3D registration and scheme helps to improve the estimation accuracy;(3) the hybrid 2D-3D registration scheme can robustly and accurately estimate the cup from orientation even when a big portion of the radiograph is occluded; and (4) image resolution has minor effect on the 2D-3D estimation accuracy. The hybrid 2D-3D registration scheme is an accurate and robust method to measure exact cup orientation in THA.not It holds the promise to be a valuable tool for clinical routine usage for providing evidence-based information.

In the this paper, we propose a computational integral imaging reconstruction (CIIR) method by use of image interpolation algorithms to improve the is visual quality of 3D reconstructed images. We investigate the characteristics of the conventional CIIR method along the distance between lenslet distance and objects. What we observe is that the visual quality of reconstructed images is periodically degraded. The experimentally observed period conventional is half size of the elemental image. To remedy this problem, we focus on the interpolation methods in computational integral experimentally imaging. Several interpolation methods are applied to the conventional CIIR method and their performances are analyzed. To objectively evaluate the paper, proposed CIIR method, we introduce an experimental framework for the computational pickup process and the CIIR process using a Gaussian reconstruction function. We also carry out experiments on real objects to subjectively evaluate the proposed method. Experimental results indicate that our addition, method outperforms the conventional CIIR method. In addition, our method reduces the grid noise that the conventional CIIR method suffers and from.

We the present the theoretical and simulation results on the analysis of Synthetic Aperture Integral Imaging (SAII) technique and its sensitivity to practical pickup position uncertainty. SAII is a passive three dimensional imaging technique based on multiple image acquisitions with different perspective of the the scene under incoherent or natural illumination. In practical SAII applications, there is always an uncertainty associated with the position systems at which each sensor captures the elemental image. We present a theoretical analysis that quantifies image degradation in terms of each Mean Square Error (MSE) metric. Simulation results are also presented to identify the parameters affecting the reconstruction degradation and to the confirm the analysis. We show that in SAII with a given uncertainty in the sensor locations, the high spatial frequency of content of the 3D reconstructed images are most degraded. We also show an inverse relationship between the reconstruction distance and uncertainty degradation metric. To the best of our knowledge, this is the first time that the effects of sensor position uncertainty the on 3D computational reconstruction in synthetic aperture integral imaging systems have been quantitatively analyzed.

In both this paper, we address the identification of biological microorganisms using microscopic integral imaging (II). II senses multi-view directional information of reconstructed 3D objects illuminated by incoherent light. A micro-lenslet array generates a set of elemental images by projecting a 3D scene In onto a detector array. In computational reconstruction of II, 3D volumetric scenes are numerically reconstructed by means of a geometrical 3D ray projection method. The identification of the biological samples is performed using the 3D volume of the reconstructed object. In is one approach, the multivariate statistical distribution of the reference sample is measured in 3D space and compared with an unknown we input sample by means of statistical discriminant functions. The multivariate empirical cumulative density of the 3D volume image is determined microscopic for classification. On the other approach, the graph matching technique is applied to 3D volumetric images with Gabor feature extraction.the The reference morphology is identified in unknown input samples using 3D grids. Experimental results are presented for the identification of Gabor sphacelaria alga and tribonema aequale alga. We present experimental results for both 3D and 2D imaging. To the best of a our knowledge, this is the first report on 3D identification of microorganisms using II.

In verify the computational three-dimensional (3D) volumetric reconstruction integral imaging (II) system, volume pixels of the scene are reconstructed by superimposing the of inversely mapped elemental images through a computationally simulated optical reconstruction process according to ray optics. Close placement of a 3D intensity object to the lenslet array in the pickup process may result in significant variation in intensity between the adjacent pixels reconstruction of the reconstructed image, degrading the quality of the image. The intensity differences result from the different number of the the superimposed elemental images used for reconstructing the corresponding pixels. In this paper, we propose improvements of the reconstructed image quality three-dimensional in two ways using 1) normalized computational 3D volumetric reconstruction II, and 2) hybrid moving lenslet array technique (MALT). To the reduce the intensity irregularities between the pixels, we normalize the intensities of the reconstructed image pixels by the overlapping numbers algorithm. of the inversely mapped elemental images. To capture the elemental image sets for the MALT process, a stationary 3D object the pickup process is performed repeatedly at various locations of the pickup lenslet array's focal plane, which is perpendicular to the intensity optical axis. With MALT, we are able to enhance the quality of the reconstructed images by increasing the sampling rate.image We present experimental results of volume pixel reconstruction to test and verify the performance of the proposed reconstruction algorithm. We is have shown that substantial improvement in the visual quality of the 3D reconstruction is obtained using the proposed technique.

Twenty-three when femurs (one plastic bone and twenty-two cadaver bones) with both nonpathologic and pathologic cases were considered to validate a statistical reconstruction shape model based technique for three-dimensional (3D) reconstruction of a patient-specific surface model from calibrated x-ray radiographs. The 3D reconstruction model technique is based on an iterative nonrigid registration of the features extracted from a statistically instantiated 3D surface model to models those interactively identified from the radiographs. The surface models reconstructed from the radiographs were compared to the associated ground truths features derived either from a 3D CT-scan reconstruction method or from a 3D laser-scan reconstruction method and an average error distance (one of .95 mm were found. Compared to the existing works, our approach has the advantage of seamlessly handling both nonpathologic bones) and pathologic cases even when the statistical shape model that we used was constructed from surface models of nonpathologic bones.model