Coherent Point Drift Algorithm Research Articles

Assessment of the Implant Geometry in Fractionated Interstitial HDR Breast Brachytherapy

To determine the inter-fraction stability of the implant geometry in fractionated HDR interstitial brachytherapy (HDR-iBT) partial breast irradiations (PBI) by means of electromagnetic tracking (EMT). In our contemporary phantom study, an EMT protocol to track 6F catheters with an accuracy of 1±0.3 mm (maximum: 2 mm) in our typical clinical environment was established [Kellermeier et al. Med. Phys. 42(6):3533]. Based on that protocol, the implant geometry of 21 patients (17 patients: 9-fraction PBI and four patients: 2-fraction boost after teletherapy) was quantified during their iBT treatment. For each patient, the implant geometry was measured on the CT table just after acquiring the treatment planning scan (EMCT) and on the HDR treatment table directly after each treatment fraction (EMFx). Three 6 degrees of freedom (DoF) sensors were placed on the breast surface to obtain fiducial positions used to derive relative positions of a 5 DoF sensor manually and consecutively inserted in the catheters. To compensate for patient movements, e.g. respiratory motion, the implant sensor data were corrected against the mean position of the fiducial sensors. Using the catheter traces, dwell positions (DPs) were determined for each measurement. In analogy to the already published phantom studies, the rigid coherent point drift (CPD) algorithm was used to register corresponding DPs. For each patient, the determined DPs from EMCT and EMFx were compared against the CT-derived DPs from treatment planning (CTRef). In addition, to assess quantitatively the changes in the implant geometry - without the need for registration - the distances from each DP to all DPs within a 3 cm spherical range were determined. For each measured implant geometry, the change in these intra-implant DP distances (IIDD) was compared with those from CTRef. EMT of the whole catheter implant per patient (median: 17 catheters) took per fraction on average 6.8 minutes plus a few minutes for setup of the EMT system. The registered implant geometry from the CT table showed over all DPs a mean deviation of 1.3 mm (median: 1.1 mm, inter-quartile range IQR: 0.9 mm). Registration of the EMT-based DP from the fractions (EMFx) against CTRef resulted in an overall mean deviation of 2.4 mm (median: 2.2 mm, IQR: 1.7 mm). Throughout the fractionated treatment, no significant trend could be determined (see the Figure for the various measurement sessions). Regarding the change in IIDD over CTRef, EMCT showed a mean deviation of 0.9 mm (median: 0.6 mm, IQR: 1.1 mm). In the EMFx data, IIDD changed by 1.0 mm (median: 0.8 mm, IQR: 1.3 mm). EMT measurements in 21 HDR-iBT breast patients were feasible within the clinical workflow and well tolerated by the patients. EMT determined DPs based on the patient measurements on the CT table differed by 1.3 mm (mean) from the CT-derived DPs used for treatment planning. Throughout the fractionated treatment, the EMT determined DP deviation increased to 2.4 mm (mean) without a significant trend over the treatment duration.

Robust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information.

Recently, the Coherent Point Drift (CPD) algorithm has become a very popular and efficient method for point set registration. However, this method does not take into consideration the neighborhood structure information of points to find the correspondence and requires a manual assignment of the outlier ratio. Therefore, CPD is not robust for large degrees of degradation. In this paper, an improved method is proposed to overcome the two limitations of CPD. A structure descriptor, such as shape context, is used to perform the auxiliary calculation of the correspondence, and the proportion of each GMM component is adjusted by the similarity. The outlier ratio is formulated in the EM framework so that it can be automatically calculated and optimized iteratively. The experimental results on both synthetic data and real data demonstrate that the proposed method described here is more robust to deformation, noise, occlusion, and outliers than CPD and other state-of-the-art algorithms.

Accelerated Coherent Point Drift for Automatic Three-Dimensional Point Cloud Registration

Fully automatic 3-D point cloud registration is a highly challenging task in light detection and ranging (LiDAR) remote sensing. The coherent point drift (CPD) algorithm provides an appropriate solution for point cloud registration because of its high accuracy. However, real application of the traditional CPD algorithm is limited due to its demanding computational complexity. In this letter, we present a novel accelerated CPD (ACPD) algorithm for fast, accurate, and automatic registration of 3-D point clouds. First, a global squared iterative expectation–maximization (gSQUAREM) technique is integrated to the ACPD algorithm. Then, the dual-tree improved fast Gauss transform method is used to further accelerate the Gaussian summation process during the correspondence probability matrix calculation. Experimental results on two real data sets show that the proposed algorithm can perform fast and accurate registration on LiDAR point clouds.

CHIMERA: Clustering of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns.

Many brain disorders and diseases exhibit heterogeneous symptoms and imaging characteristics. This heterogeneity is typically not captured by commonly adopted neuroimaging analyses that seek only a main imaging pattern when two groups need to be differentiated (e.g., patients and controls, or clinical progressors and non-progressors). We propose a novel probabilistic clustering approach, CHIMERA, modeling the pathological process by a combination of multiple regularized transformations from normal/control population to the patient population, thereby seeking to identify multiple imaging patterns that relate to disease effects and to better characterize disease heterogeneity. In our framework, normal and patient populations are considered as point distributions that are matched by a variant of the coherent point drift algorithm. We explain how the posterior probabilities produced during the MAP optimization of CHIMERA can be used for clustering the patients into groups and identifying disease subtypes. CHIMERA was first validated on a synthetic dataset and then on a clinical dataset mixing 317 control subjects and patients suffering from Alzheimer's Disease (AD) and Parkison's Disease (PD). CHIMERA produced better clustering results compared to two standard clustering approaches. We further analyzed 390 T1 MRI scans from Alzheimer's patients. We discovered two main and reproducible AD subtypes displaying significant differences in cognitive performance.

METHOD OF REGISTRATION FOR 3D FACE POINT CLOUD DATA

This paper analyzes the techniques that can be used to perform point cloud data registration for a human face. We found that there is a limitation in full scale viewing on the input data taken from 3D camera which is only represented the front face of a man as the point of view of a camera. This has caused a hole on the surface that is not filled with the point cloud data. This research is done by mapping the retrieved point cloud to the surface of the face template of the human head. By using Coherent Point Drift (CPD) algorithm which is one of the non-rigid registration techniques, the analysis has been done and it shows that the mapping of points for a three-dimensional (3D) face is not done properly where there are some surfaces work well and certain points spread into the wrong area. Consequently, it has resulted in registration failure occurrences due to the concentration of the points which is focusing on the face only.

Enhanced coherent point drift algorithm for remote sensing image registration

Remote sensing image registration is a key component in many computer vision tasks since it can improve the understanding of information among multisensor images through fusing. After feature detection, the image registration is converted into a point set registration problem. The coherent point drift (CPD) algorithm is regarded as a powerful approach for point set registration. However, for junction set, a serious problem arises when using this algorithm—the structural information of the junction is not included in the Gaussian mixture model. To solve this problem, we present an enhanced coherent point drift (ECPD) algorithm. According to the inherent characteristic of junction, we propose the definition of local structural consistency which measures the similarity between two junctions. Furthermore, we introduce local structural consistency as a part of GMM components’ posterior probabilities to achieve more accurate registration results. The experiments of remote sensing image registration show that the ECPD algorithm is more robust to noises and outliers than CPD and outperforms current state-of-the-art methods.

Multimodal image-guided prostate fusion biopsy based on automatic deformable registration.

Transrectal ultrasound (TRUS)-guided random prostate biopsy is, in spite of its low sensitivity, the gold standard for the diagnosis of prostate cancer. The recent advent of PET imaging using a novel dedicated radiotracer, [Formula: see text]-labeled prostate-specific membrane antigen (PSMA), combined with MRI provides improved pre-interventional identification of suspicious areas. This work proposes a multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS, using automatic segmentation and registration, and offering real-time guidance. The prostate TRUS images are automatically segmented with a Hough transform-based random forest approach. The registration is based on the Coherent Point Drift algorithm to align surfaces elastically and to propagate the deformation field calculated from thin-plate splines to the whole gland. The method, which has minimal requirements and temporal overhead in the existing clinical workflow, is evaluated in terms of surface distance and landmark registration error with respect to the clinical ground truth. Evaluations on agar-gelatin phantoms and clinical data of 13 patients confirm the validity of this approach. The system is able to successfully map suspicious regions from PET/MRI to the interventional TRUS image.

Robust rigid coherent point drift algorithm based on outlier suppression and its application in image matching

The typical probability based point pattern matching method is coherent point drift (CPD) algorithm, which treats one point set as centroids of a Gaussian mixture model, and then fits it to the other. It uses the expectation maximization framework, where the point correspondences and transformation parameters are updated alternately. However, the anti-outlier performance of CPD is not robust enough as outliers have always been involved in the operation until the CPD converges. Hence, an automatic outlier suppression (AOS) mechanism is proposed. First, outliers are judged by a matching probability matrix. Then, transformation parameters are fitted using accurate matching point sets. Finally, the Gaussian centroids are forced to move coherently by this transformation model. AOS-CPD can efficiently improve the anti-outlier performance of rigid CPD. Furthermore, CPD is applied to image matching. A new local changing information descriptor-relative phase histogram (RPH) is designed and RPH-AOS-CPD is proposed to embed RPH measurement into AOS-CPD as a constraint condition. RPH-AOS-CPD makes full use of grayscale information besides having an excellent anti-outlier performance. The experimental results based on both synthetic and real data indicate that compared with other algorithms, AOS-CPD is more robust to outliers and RPH-AOS-CPD offers a good practicability and accuracy in image matching applications.

Adapting the coherent point drift algorithm to the fixtureless dimensional inspection of compliant parts

In a free-state condition, deformable bodies (or compliant parts) have a significantly different shape than their nominal geometry (CAD model) due to gravity loads and residual stress. Typically, the dimensional metrology of such parts requires a particular approach where expensive and specialized jigs are needed to constrain and follow the component during inspection. This paper proposes a new method to fixtureless inspect deformable bodies by adapting the coherent point drift (CPD) algorithm. This new approach combines the optimization of the smoothness regularization parameters of the CPD powerful non-rigid registration method alongside the Thompson-Biweight statistical test as an identification method to distinguish profile and localization process manufacturing defects from a part’s deformation. In other words, a new “flexible” registration approach that meets the specifications of compliant parts is proposed. The CAD model is smoothly modified to fit the scanned part by minimizing two criteria that belong to deformable bodies. The first criterion is the conservation of the curvilinear distance (isometric transformation). That is to say, the condition that the stretch difference between the original CAD model and the modified one should be very small. The second criterion is the usual minimization of the Euclidean distance between the modified CAD model and its corresponding scanned part. Many case studies were performed on a typical industrial sheet metal which presented profile and localization defects. The estimated values of the defects, as well as their positions, are much closer to their reference ones in most cases, thus reflecting the effectiveness of the proposed method.

Image Registration Technique Based on a Fast CPD Algorithm

The Coherent Point Drift (CPD) algorithm which based on Gauss Mixture Model is a robust point set registration algorithm. However, the selection of robustness weight which used to describe the noise may directly affect the point set registration efficiency. For resolving the problem, this paper presents a CPD registration algorithm which based on distance threshold constraint. Before the point set registration, the inaccurate template point set by resampling become the initial point set of point set matching, in order to eliminate some points that the distance to target point set is too close and too far in the inaccurate template point set, and set the weights of robustness as . In the simulation experiments, we make two group experiments: the first group is the registration of the inaccurate template point set and the accurate target point set, while the second group is the registration of the accurate template point set and the accurate target point set. The results of comparison show that our method can solve the problem of selection for the weight. And it improves the speed and precision of the original CPD registration.

The Coherent Point Drift Algorithm Adapted for Fixtureless Metrology of Non-rigid Parts

Unlike the metrology of rigid parts, no viable and industrial solutions in the case of non-rigid parts are available. Due to gravity load and residual stress, non-rigid parts (flexible, compliant) may have in a Free State condition a significant different shape than their corresponding nominal geometry (CAD model). As a result, very expensive and specialized fixtures mounting are needed by the industry to constrain the component during the inspection. Dealing with this real industrial problem, this paper proposes a new method to inspect non-rigid parts without these specialized fixtures. In this method, the CAD model is smoothly modified to fit the scanned part respecting two criteria that belong to non-rigid parts. The first criterion is the isometric transformation (or the condition that stretch should be very small) between the original CAD model and the modified one. The second criterion is the Euclidian distance between the modified CAD model and its corresponding scanned part. The proposed approach consists of adapting the Coherent Point Drift powerful non rigid registration method to meet the specifications of non-rigid parts. In other words, by minimizing the two above criteria, the paper proposes a ‘flexible’ registration to align the scanned manufactured compliant part to its nominal model in order to compare them and to deliver an inspection report. Satisfying results were obtained when validating the proposed method on a case study taken from the aerospace industry. The low percentage of error between the estimated value of defect and the reference one reflect the effectiveness of the proposed approach.

Kinect-based automatic spatial registration framework for neurosurgical navigation

As image-guided navigation plays an important role in neurosurgery, the spatial registration mapping the pre-operative images with the intra-operative patient position becomes crucial for a high accurate surgical output. Conventional landmark-based registration requires expensive and time-consuming logistic support. Surface-based registration is a plausible alternative due to its simplicity and efficacy. In this paper, we propose a comprehensive framework for surface-based registration in neurosurgical navigation, where Kinect is used to automatically acquire patient’s facial surface in a real time manner. Coherent point drift (CPD) algorithm is employed to register the facial surface with pre-operative images (e.g., computed tomography (CT) or magnetic resonance imaging (MRI)) using a coarse-to-fine scheme. The spatial registration results of 6 volunteers demonstrate that the proposed framework has potential for clinical use.

Effective Association of SAR and AIS Data Using Non-Rigid Point Pattern Matching

Ship surveillance using multiple remote sensing sensors becomes more and more vital presently. Among the various sensors, space-borne Synthetic Aperture Radar (SAR) is optimal for its high resolution over wide swaths and all-weather working capabilities. Meanwhile, Automatic Identification System (AIS) is efficient to provide ship navigational information. Limited to the progress of ship surveillance using SAR image only, the integration of them significantly benefits more. Data association is the fundamental issue. Many algorithms have been developed including the Nearest-Neighbour (NN) algorithm, the Joint Probabilistic Data Association (JPDA) method, and the Multiple Hypothesis Testing (MHT) approach. Ship positions derived from SAR image can be associated with the ones provided by AIS. State-of-the-art method (NN algorithm) is proved to be feasible. But it faces more challenges under adverse circumstances, such as high-density-shipping condition. We investigate the non-rigid Point Pattern Matching (PPM) method to solve this problem. To the best of our knowledge, this paper is the first to introduce non-rigid PPM to the data association of SAR and AIS. On the basis of introduction to the data association, Coherent Point Drift (CPD) algorithm is investigated. Experiments are carried out and the results illustrate that the CPD algorithm achieves higher accuracy and outperforms state-of-the-art method, especially under high-density-shipping condition.

Multiexposure and multifocus image fusion with multidimensional camera shake compensation

Multiexposure image fusion algorithms are used for enhancing the perceptual quality of an image captured by sensors of limited dynamic range. This is achieved by rendering a single scene based on multiple images captured at different exposure times. Similarly, multifocus image fusion is used when the limited depth of focus on a selected focus setting of a camera results in parts of an image being out of focus. The solution adopted is to fuse together a number of multifocus images to create an image that is focused throughout. A single algorithm that can perform both multifocus and multiexposure image fusion is proposed. This algorithm is a new approach in which a set of unregistered multiexposure/focus images is first registered before being fused to compensate for the possible presence of camera shake. The registration of images is done via identifying matching key-points in constituent images using scale invariant feature transforms. The random sample consensus algorithm is used to identify inliers of SIFT key-points removing outliers that can cause errors in the registration process. Finally, the coherent point drift algorithm is used to register the images, preparing them to be fused in the subsequent fusion stage. For the fusion of images, a new approach based on an improved version of a wavelet-based contourlet transform is used. The experimental results and the detailed analysis presented prove that the proposed algorithm is capable of producing high-dynamic range (HDR) or multifocus images by registering and fusing a set of multiexposure or multifocus images taken in the presence of camera shake. Further, comparison of the performance of the proposed algorithm with a number of state-of-the art algorithms and commercial software packages is provided. In particular, our literature review has revealed that this is one of the first attempts where the compensation of camera shake, a very likely practical problem that can result in HDR image capture using handheld devices, has been addressed as a part of a multifocus and multiexposure image enhancement system.

A refined coherent point drift (CPD) algorithm for point set registration

The coherent point drift (CPD) algorithm is a powerful approach for point set registration. However, it suffers from a serious problem-there is a weight parameter w that reflects the assumption about the amount of noise and number of outliers in the Gaussian mixture model, and its value has an influence on the point set registration performance In the original CPD algorithm, the value of w is set manually, and hence an improper value will lead to poor registration results. To solve this problem, a fully automatic algorithm for the selection of an optimal weight parameter is proposed using a hybrid optimization scheme that combines the genetic algorithm with the Nelder-Mead simplex method. The experiments show that the refined CPD algorithm is more effective and extends the original CPD algorithm in its methodology and applications.

Point Set Registration: Coherent Point Drift

Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other. Multiple factors, including an unknown nonrigid spatial transformation, large dimensionality of point set, noise, and outliers, make the point set registration a challenging problem. We introduce a probabilistic method, called the Coherent Point Drift (CPD) algorithm, for both rigid and nonrigid point set registration. We consider the alignment of two point sets as a probability density estimation problem. We fit the Gaussian mixture model (GMM) centroids (representing the first point set) to the data (the second point set) by maximizing the likelihood. We force the GMM centroids to move coherently as a group to preserve the topological structure of the point sets. In the rigid case, we impose the coherence constraint by reparameterization of GMM centroid locations with rigid parameters and derive a closed form solution of the maximization step of the EM algorithm in arbitrary dimensions. In the nonrigid case, we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation. We also introduce a fast algorithm that reduces the method computation complexity to linear. We test the CPD algorithm for both rigid and nonrigid transformations in the presence of noise, outliers, and missing points, where CPD shows accurate results and outperforms current state-of-the-art methods.