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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Doctoral thesis
Subjects: ZA4050, R1
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1 Introduction ............................................................................................................. 1 1.1 Anatomy of the Heart and Coronary Arteries .................................................... 1 1.2 Coronary Artery Disease .................................................................................... 2 1.3 Diagnostic Imaging of Coronary Artery Disease ............................................... 3 1.3.1 Coronary Artery Angiography (Cardiac Catheterisation) ........................... 3 1.3.2 Computed Tomography Angiography......................................................... 5 1.4 Outline of the Thesis .......................................................................................... 8
    • 2.12 Illustration of the axial view of CT images. (a) The definition of the projection planes, (b) A transaxial image examination allows the visualisation of the 2D axial projection image ....................................................................
    • 2.13 A diagram shows the relation between image contrast and brightness and the window-level settings ...............................
    • 2.14 The images illustrate the multiplanar reformatted image reconstructed from the volumetric CT dataset. (a) 3D visualisation showing the volume rendered image of the thorax, (b) Image reconstructed from an off-axial plane, defined by the red rectangle shown in (a) .............................
    • 2.15 Visualisation of CPR images of the (a) left, and (b) right coronary arteries......................................................................
    • 2.16 Examples of volume rendered views constructed from coronary CT angiography. (a) and (b) illustrate the volume rendered images with the alpha and colour map configurations, as depicted in (c) and (d), for visualising the vasculature and bones, respectively ........................................
    • 3.15 An example of fitting the histogram and defining the labelling function ..................................................................
    • 3.16 Illustration of the global energy map. (a) The labelled image, (b) The smoothed labelled map following anisotropic diffusion ..................................................................................
    • 3.17 Comparison of the segmentation results obtained using the Yang et al., model, the localised CV method and the proposed algorithm. (a) Initialisation, (b) Yang et al., model results, (c) Localised CV method results, (d) Results of the proposed algorithm. ..............................................................
    • 3.21 Comparison of the resulting segmentation for datasets #3, #5 and #11 obtained using the proposed model (a), (c) and (e), and Yang et al. technique (b), (d) and (f), with respect to the ground truth data. ..................................................................
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