Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy

Backgrounds: Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). An alternative method using 2D lateral fluoroscopy was developed. Materials and methods: A technique was developed to reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model of the lumbar vertebrae. Four cadaveric lumbar spine segments and two statistical shape models were used for testing. Reconstruction accuracy was determined by comparison of the surface models reconstructed from the single lateral fluoroscopic images to the ground truth data from 3D CT segmentation. For each case, two different surface-based registration techniques were used to recover the unknown scale factor, and the rigid transformation between the reconstructed surface model and the ground truth model before the differences between the two discrete surface models were computed. Results: Successful reconstruction of scaled surface models was achieved for all test lumbar vertebrae based on single lateral fluoroscopic images. The mean reconstruction error was between 0.7 and 1.6mm. Conclusions: A scaled, patient-specific surface model of the lumbar vertebra from a single lateral fluoroscopic image can be synthesized using the present approach. This new method for patient-specific 3D modeling has potential applications in spine kinematics analysis, surgical planning, and navigatio

A survey on 2d object tracking in digital video

This paper presents object tracking methods in video.Different algorithms based on rigid, non rigid and articulated object tracking are studied. The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends.It is often the case that tracking objects in consecutive frames is supported by a prediction scheme. Based on information extracted from previous frames and any high level information that can be obtained, the state (location) of the object is predicted.An excellent framework for prediction is kalman filter, which additionally estimates prediction error.In complex scenes, instead of single hypothesis, multiple hypotheses using Particle filter can be used.Different techniques are given for different types of constraints in video

Using generative models for handwritten digit recognition

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques

Medical image segmentation and analysis using statistical shape modelling and inter-landmark relationships

The study of anatomical morphology is of great importance to medical imaging, with applications varying from clinical diagnosis to computer-aided surgery. To this end, automated tools are required for accurate extraction of the anatomical boundaries from the image data and detailed interpretation of morphological information. This thesis introduces a novel approach to shape-based analysis of medical images based on Inter- Landmark Descriptors (ILDs). Unlike point coordinates that describe absolute position, these shape variables represent relative configuration of landmarks in the shape. The proposed work is motivated by the inherent difficulties of methods based on landmark coordinates in challenging applications. Through explicit invariance to pose parameters and decomposition of the global shape constraints, this work permits anatomical shape analysis that is resistant to image inhomogeneities and geometrical inconsistencies. Several algorithms are presented to tackle specific image segmentation and analysis problems, including automatic initialisation, optimal feature point search, outlier handling and dynamic abnormality localisation. Detailed validation results are provided based on various cardiovascular magnetic resonance datasets, showing increased robustness and accuracy.Open acces

Analysis and Manipulation of Repetitive Structures of Varying Shape

Self-similarity and repetitions are ubiquitous in man-made and natural objects. Such structural regularities often relate to form, function, aesthetics, and design considerations. Discovering structural redundancies along with their dominant variations from 3D geometry not only allows us to better understand the underlying objects, but is also beneficial for several geometry processing tasks including compact representation, shape completion, and intuitive shape manipulation. To identify these repetitions, we present a novel detection algorithm based on analyzing a graph of surface features. We combine general feature detection schemes with a RANSAC-based randomized subgraph searching algorithm in order to reliably detect recurring patterns of locally unique structures. A subsequent segmentation step based on a simultaneous region growing is applied to verify that the actual data supports the patterns detected in the feature graphs. We introduce our graph based detection algorithm on the example of rigid repetitive structure detection. Then we extend the approach to allow more general deformations between the detected parts. We introduce subspace symmetries whereby we characterize similarity by requiring the set of repeating structures to form a low dimensional shape space. We discover these structures based on detecting linearly correlated correspondences among graphs of invariant features. The found symmetries along with the modeled variations are useful for a variety of applications including non-local and non-rigid denoising. Employing subspace symmetries for shape editing, we introduce a morphable part model for smart shape manipulation. The input geometry is converted to an assembly of deformable parts with appropriate boundary conditions. Our method uses self-similarities from a single model or corresponding parts of shape collections as training input and allows the user also to reassemble the identified parts in new configurations, thus exploiting both the discrete and continuous learned variations while ensuring appropriate boundary conditions across part boundaries. We obtain an interactive yet intuitive shape deformation framework producing realistic deformations on classes of objects that are difficult to edit using repetition-unaware deformation techniques

Registration of magnetic resonance and ultrasound images for guiding prostate cancer interventions

Prostate cancer is a major international health problem with a large and rising incidence in many parts of the world. Transrectal ultrasound (TRUS) imaging is used routinely to guide surgical procedures, such as needle biopsy and a number of minimally-invasive therapies, but its limited ability to visualise prostate cancer is widely recognised. Magnetic resonance (MR) imaging techniques, on the other hand, have recently been developed that can provide clinically useful diagnostic information. Registration (or alignment) of MR and TRUS images during TRUS-guided surgical interventions potentially provides a cost-effective approach to augment TRUS images with clinically useful, MR-derived information (for example, tumour location, shape and size). This thesis describes a deformable image registration framework that enables automatic and/or semi-automatic alignment of MR and 3D TRUS images of the prostate gland. The method combines two technical developments in the field: First, a method for constructing patient-specific statistical shape models of prostate motion/deformation, based on learning from finite element simulations of gland motion using geometric data from a preoperative MR image, is proposed. Second, a novel “model-to-image” registration framework is developed to register this statistical shape model automatically to an intraoperative TRUS image. This registration approach is implemented using a novel model-to-image vector alignment (MIVA) algorithm, which maximises the likelihood of a particular instance of a statistical shape model given a voxel-intensity-based feature vector that represents an estimate of the surface normal vectors at the boundary of the organ in question. Using real patient data, the MR-TRUS registration accuracy of the new algorithm is validated using intra-prostatic anatomical landmarks. A rigorous and extensive validation analysis is also provided for assessing the image registration experiments. The final target registration error after performing 100 MR–TRUS registrations for each patient have a median of 2.40 mm, meaning that over 93% registrations may successfully hit the target representing a clinically significant lesion. The implemented registration algorithms took less than 30 seconds and 2 minutes for manually defined point- and normal vector features, respectively. The thesis concludes with a summary of potential applications and future research directions

Object localization using deformable templates

Object localization refers to the detection, matching and segmentation of objects in images. The localization model presented in this paper relies on deformable templates to match objects based on shape alone. The shape structure is captured by a prototype template consisting of hand-drawn edges and contours representing the object to be localized. A multistage, multiresolution algorithm is utilized to reduce the computational intensity of the search. The first stage reduces the physical search space dimensions using correlation to determine the regions of interest where a match it likely to occur. The second stage finds approximate matches between the template and target image at progressively finer resolutions, by attracting the template to salient image features using Edge Potential Fields. The third stage entails the use of evolutionary optimization to determine control point placement for a Local Weighted Mean warp, which deforms the template to fit the object boundaries. Results are presented for a number of applications, showing the successful localization of various objects. The algorithm’s invariance to rotation, scale, translation and moderate shape variation of the target objects is clearly illustrated

Computer-Assisted Electroanatomical Guidance for Cardiac Electrophysiology Procedures

Cardiac arrhythmias are serious life-threatening episodes affecting both the aging population and younger patients with pre-existing heart conditions. One of the most effective therapeutic procedures is the minimally-invasive catheter-driven endovascular electrophysiology study, whereby electrical potentials and activation patterns in the affected cardiac chambers are measured and subsequent ablation of arrhythmogenic tissue is performed. Despite emerging technologies such as electroanatomical mapping and remote intraoperative navigation systems for improved catheter manipulation and stability, successful ablation of arrhythmias is still highly-dependent on the operator’s skills and experience. This thesis proposes a framework towards standardisation in the electroanatomical mapping and ablation planning by merging knowledge transfer from previous cases and patient-specific data. In particular, contributions towards four different procedural aspects were made: optimal electroanatomical mapping, arrhythmia path computation, catheter tip stability analysis, and ablation simulation and optimisation. In order to improve the intraoperative electroanatomical map, anatomical areas of high mapping interest were proposed, as learned from previous electrophysiology studies. Subsequently, the arrhythmic wave propagation on the endocardial surface and potential ablation points were computed. The ablation planning is further enhanced, firstly by the analysis of the catheter tip stability and the probability of slippage at sparse locations on the endocardium and, secondly, by the simulation of the ablation result from the computation of convolutional matrices which model mathematically the ablation process. The methods proposed by this thesis were validated on data from patients with complex congenital heart disease, who present unusual cardiac anatomy and consequently atypical arrhythmias. The proposed methods also build a generic framework for computer guidance of electrophysiology, with results showing complementary information that can be easily integrated into the clinical workflow.Open Acces

Novel Methods for Multi-Shape Analysis

Multi-shape analysis has the objective to recognise, classify, or quantify morphological patterns or regularities within a set of shapes of a particular object class in order to better understand the object class of interest. One important aspect of multi-shape analysis are Statistical Shape Models (SSMs), where a collection of shapes is analysed and modelled within a statistical framework. SSMs can be used as (statistical) prior that describes which shapes are more likely and which shapes are less likely to be plausible instances of the object class of interest. Assuming that the object class of interest is known, such a prior can for example be used in order to reconstruct a three-dimensional surface from only a few known surface points. One relevant application of this surface reconstruction is 3D image segmentation in medical imaging, where the anatomical structure of interest is known a-priori and the surface points are obtained (either automatically or manually) from images. Frequently, Point Distribution Models (PDMs) are used to represent the distribution of shapes, where each shape is discretised and represented as labelled point set. With that, a shape can be interpreted as an element of a vector space, the so-called shape space, and the shape distribution in shape space can be estimated from a collection of given shape samples. One crucial aspect for the creation of PDMs that is tackled in this thesis is how to establish (bijective) correspondences across the collection of training shapes. Evaluated on brain shapes, the proposed method results in an improved model quality compared to existing approaches whilst at the same time being superior with respect to runtime. The second aspect considered in this work is how to learn a low-dimensional subspace of the shape space that is close to the training shapes, where all factors spanning this subspace have local support. Compared to previous work, the proposed method models the local support regions implicitly, such that no initialisation of the size and location of these regions is necessary, which is advantageous in scenarios where this information is not available. The third topic covered in this thesis is how to use an SSM in order to reconstruct a surface from only few surface points. By using a Gaussian Mixture Model (GMM) with anisotropic covariance matrices, which are oriented according to the surface normals, a more surface-oriented fitting is achieved compared to a purely point-based fitting when using the common Iterative Closest Point (ICP) algorithm. In comparison to ICP we find that the GMM-based approach gives superior accuracy and robustness on sparse data. Furthermore, this work covers the transformation synchronisation method, which is a procedure for removing noise that accounts for transitive inconsistency in the set of pairwise linear transformations. One interesting application of this methodology that is relevant in the context of multi-shape analysis is to solve the multi-alignment problem in an unbiased/reference-free manner. Moreover, by introducing an improvement of the numerical stability, the methodology can be used to solve the (affine) multi-image registration problem from pairwise registrations. Compared to reference-based multi-image registration, the proposed approach leads to an improved registration accuracy and is unbiased/reference-free, which makes it ideal for statistical analyses