Towards multiple 3D bone surface identification and reconstruction using few 2D X-ray images for intraoperative applications

This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone’s edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

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

Modelling and tracking objects with a topology preserving self-organising neural network

Human gestures form an integral part in our everyday communication. We use gestures not only to reinforce meaning, but also to describe the shape of objects, to play games, and to communicate in noisy environments. Vision systems that exploit gestures are often limited by inaccuracies inherent in handcrafted models. These models are generated from a collection of training examples which requires segmentation and alignment. Segmentation in gesture recognition typically involves manual intervention, a time consuming process that is feasible only for a limited set of gestures. Ideally gesture models should be automatically acquired via a learning scheme that enables the acquisition of detailed behavioural knowledge only from topological and temporal observation. The research described in this thesis is motivated by a desire to provide a framework for the unsupervised acquisition and tracking of gesture models. In any learning framework, the initialisation of the shapes is very crucial. Hence, it would be beneficial to have a robust model not prone to noise that can automatically correspond the set of shapes. In the first part of this thesis, we develop a framework for building statistical 2D shape models by extracting, labelling and corresponding landmark points using only topological relations derived from competitive hebbian learning. The method is based on the assumption that correspondences can be addressed as an unsupervised classification problem where landmark points are the cluster centres (nodes) in a high-dimensional vector space. The approach is novel in that the network can be used in cases where the topological structure of the input pattern is not known a priori thus no topology of fixed dimensionality is imposed onto the network. In the second part, we propose an approach to minimise the user intervention in the adaptation process, which requires to specify a priori the number of nodes needed to represent an object, by utilising an automatic criterion for maximum node growth. Furthermore, this model is used to represent motion in image sequences by initialising a suitable segmentation that separates the object of interest from the background. The segmentation system takes into consideration some illumination tolerance, images as inputs from ordinary cameras and webcams, some low to medium cluttered background avoiding extremely cluttered backgrounds, and that the objects are at close range from the camera. In the final part, we extend the framework for the automatic modelling and unsupervised tracking of 2D hand gestures in a sequence of k frames. The aim is to use the tracked frames as training examples in order to build the model and maintain correspondences. To do that we add an active step to the Growing Neural Gas (GNG) network, which we call Active Growing Neural Gas (A-GNG) that takes into consideration not only the geometrical position of the nodes, but also the underlined local feature structure of the image, and the distance vector between successive images. The quality of our model is measured through the calculation of the topographic product. The topographic product is our topology preserving measure which quantifies the neighbourhood preservation. In our system we have applied specific restrictions in the velocity and the appearance of the gestures to simplify the difficulty of the motion analysis in the gesture representation. The proposed framework has been validated on applications related to sign language. The work has great potential in Virtual Reality (VR) applications where the learning and the representation of gestures becomes natural without the need of expensive wear cable sensors

Patch-based Corner Detection for Cervical Vertebrae in X-ray Images

Corners hold vital information about size, shape and morphology of a vertebra in an x-ray image, and recent literature [1, 2] has shown promising performance for detecting vertebral corners using a Hough forest-based architecture. To provide spatial context, this method generates a set of 12 patches around a vertebra and uses a machine learning approach to predict corners of a vertebral body through a voting process. In this paper, we extend this framework in terms of patch generation and prediction methods. During patch generation, the square region of interest has been replaced with data-driven rectangular and trapezoidal region of interest which better aligns the patches to the vertebral body geometry, resulting in more discriminative feature vectors. The corner estimation or the prediction stage has been improved by utilising more efficient voting process using a single kernel density estimation. In addition, advanced and more complex feature vectors are introduced. We also present a thorough evaluation of the framework with different patch generation methods, forest training mechanisms and prediction methods. In order to compare the performance of this framework with a more general method, a novel multi-scale Harris corner detector-based approach is introduced that incorporates a spatial prior through a naive Bayes method. All these methods have been tested on a dataset of 90 X-ray images and achieved an average corner localization error of 2.01 mm, representing a 33% improvement in localisation accuracy compared to the previous state-of-the-art method [2]

Blood Vessel Segmentation and shape analysis for quantification of Coronary Artery Stenosis in CT Angiography

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm

3-D Quantitative Vascular Shape Analysis for Arterial Bifurcations via Dynamic Tube Fitting

Reliable and reproducible estimation of vessel centerlines and reference surfaces is an important step for the assessment of luminal lesions. Conventional methods are commonly developed for quantitative analysis of the “straight” vessel segments and have limitations in defining the precise location of the centerline and the reference lumen surface for both the main vessel and the side branches in the vicinity of bifurcations. To address this, we propose the estimation of the centerline and the reference surface through the registration of an elliptical cross-sectional tube to the desired constituent vessel in each major bifurcation of the arterial tree. The proposed method works directly on the mesh domain, thus alleviating the need for image upsampling, usually required in conventional volume domain approaches. We demonstrate the efficiency and accuracy of the method on both synthetic images and coronary CT angiograms. Experimental results show that the new method is capable of estimating vessel centerlines and reference surfaces with a high degree of agreement to those obtained through manual delineation. The centerline errors are reduced by an average of 62.3% in the regions of the bifurcations, when compared to the results of the initial solution obtained through the use of mesh contraction method