Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin

Bi-Plane X-ray coronary 3D reconstruction for flow velocity assessment

In coronary angiography the coronary blood flow velocity can be assessed by tracking a contrast agent in an image sequence. The contrast agent flow velocity is used to estimate the functional behavior of the coronary arteries using TIMI frame count (TFC). In this paper we descibe the 3D reconstruction algorithm used in our method towards the automation of TFC. The method creates a two dimensional map of the contrast agent in which the opacification of the vessel centerline is plotted against time. This map is used to find the velocity of the contrast agent and subsequently the TFC. The vessel centerline is obtained using the Fast Marching Method to find the minimum cost path between\ud the catheter point and the end of the vessel. The determination of the start and endpoint is estimated using a 3D model reconstructed from bi-plane 2D image\ud data. The final reconstructed coronary model only includes the segments matching our error criterion

Active modelling of virtual humans

This thesis provides a complete framework that enables the creation of photorealistic 3D human models in real-world environments. The approach allows a non-expert user to use any digital capture device to obtain four images of an individual and create a personalised 3D model, for multimedia applications. To achieve this, it is necessary that the system is automatic and that the reconstruction process is flexible to account for information that is not available or incorrectly captured. In this approach the individual is automatically extracted from the environment using constrained active B-spline templates that are scaled and automatically initialised using only image information. These templates incorporate the energy minimising framework for Active Contour Models, providing a suitable and flexible method to deal with the adjustments in pose an individual can adopt. The final states of the templates describe the individual’s shape. The contours in each view are combined to form a 3D B-spline surface that characterises an individual’s maximal silhouette equivalent. The surface provides a mould that contains sufficient information to allow for the active deformation of an underlying generic human model. This modelling approach is performed using a novel technique that evolves active-meshes to 3D for deforming the underlying human model, while adaptively constraining it to preserve its existing structure. The active-mesh approach incorporates internal constraints that maintain the structural relationship of the vertices of the human model, while external forces deform the model congruous to the 3D surface mould. The strength of the internal constraints can be reduced to allow the model to adopt the exact shape of the bounding volume or strengthened to preserve the internal structure, particularly in areas of high detail. This novel implementation provides a uniform framework that can be simply and automatically applied to the entire human model

Building a Parameterized 4D Cardiac Model with Respiratory Motion from 2D MR Time Series

Projecte final de carrera fet en col.laboració amb Northeastern UniversityAtrial fibrillation (AF) is a growing problem in modern societies with an enormous impact in both short term quality of life and long term survival. A recently developed promising approach to cure AF uses radiofrequency (RF) ablation to carry out "pulmonary vein antrum isolation" (PVAI), electrically isolating the pulmonary veins from the rest of the atrium. However, the lack of proper 3D surgery training, planning, and guidance, along with current limitations in understanding of the true causes and mechanisms of AF, makes this surgery a very difficult task for the surgeons. Therefore recurrence rates and even failures of the procedure, as well as the risk for the patient, increase. The purpose of this work is to develop methods for automatically segmenting and tracking the heart in 4-D cardiac MRI datasets. The reconstructed heart surface will serve as a virtual computer model for the 3D surgery training, planning and guidance. The method used in this project is based on an active contour model for segmentation, followed by a spatial-time post-filtering and processing of the data obtained by the segmentation

Analysis of myocardial motion using generalized spline models and tagged magnetic resonance images

Heart wall motion abnormalities are the very sensitive indicators of common heart diseases, such as myocardial infarction and ischemia. Regional strain analysis is especially important in diagnosing local abnormalities and mechanical changes in the myocardium. In this work, we present a complete method for the analysis of cardiac motion and the evaluation of regional strain in the left ventricular wall. The method is based on the generalized spline models and tagged magnetic resonance images (MRI) of the left ventricle. The whole method combines dynamical tracking of tag deformation, simulating cardiac movement and accurately computing the regional strain distribution. More specifically, the analysis of cardiac motion is performed in three stages. Firstly, material points within the myocardium are tracked over time using a semi-automated snake-based tag tracking algorithm developed for this purpose. This procedure is repeated in three orthogonal axes so as to generate a set of one-dimensional sample measurements of the displacement field. The 3D-displacement field is then reconstructed from this sample set by using a generalized vector spline model. The spline reconstruction of the displacement field is explicitly expressed as a linear combination of a spline kernel function associated with each sample point and a polynomial term. Finally, the strain tensor (linear or nonlinear) with three direct components and three shear components is calculated by applying a differential operator directly to the displacement function. The proposed method is computationally effective and easy to perform on tagged MR images. The preliminary study has shown potential advantages of using this method for the analysis of myocardial motion and the quantification of regional strain

Automated road extraction from terrestrial based mobile laser scanning system using the GVF snake model

Accurate extraction and reconstruction of route corridor features from geospatial data is a prerequisite to effective management of road networks for engineering, safety and environmental applications. High quality road geometry and road side features can now be extracted from dense point cloud LiDAR data, recorded by modern day Mobile Mapping Systems. This valuable route network information is gaining the attention of road safety and maintenance engineers. Road points are needed to be correctly identified, classified and extracted from LiDAR data before reconstructing intrinsic road geometry and road-side infrastructure. In this paper, we present a method to automatically extract the road from terrestrial based mobile laser scanning system using the GVF (Gradient Vector Flow) snake model. A snake is an energy minimizing spline that moves towards the desired feature or object boundary under the influence of internal forces within the curve itself and external GVF forces derived typically from 2D imaging data by minimizing certain energy such as edges or high frequency information. In our novel method, we initialise the snake contours over point cloud data based on the trajectory information produced by the MMS navigation sub-system. The internal energy term provided to the snake contour is based on adjusting the intrinsic properties of the curve, such as elasticity and bending, whilst the GVF energy and constraint energy terms are derived from the LiDAR point cloud attributes. Our method primarily differs from the traditional snake models in initialisation and in deriving the energy terms from the 3D LiDAR data