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

Axial deformation with controllable local coordinate frames.

Chow, Yuk Pui.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (leaves 83-87).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.13-16Chapter 1.1. --- Motivation --- p.13Chapter 1.2 --- Objectives --- p.14-15Chapter 1.3 --- Thesis Organization --- p.16Chapter 2. --- Related Works --- p.17-24Chapter 2.1 --- Axial and the Free Form Deformation --- p.17Chapter 2.1.1 --- The Free-Form Deformation --- p.18Chapter 2.1.2 --- The Lattice-based Representation --- p.18Chapter 2.1.3 --- The Axial Deformation --- p.19-20Chapter 2.1.4 --- Curve Pair-based Representation --- p.21-22Chapter 2.2 --- Self Intersection Detection --- p.23-24Chapter 3. --- Axial Deformation with Controllable LCFs --- p.25-46Chapter 3.1 --- Related Methods --- p.25Chapter 3.2 --- Axial Space --- p.26-27Chapter 3.3 --- Definition of Local Coordinate Frame --- p.28-29Chapter 3.4 --- Constructing Axial Curve with LCFs --- p.30Chapter 3.5 --- Point Projection Method --- p.31-32Chapter 3.5.1 --- Optimum Reference Axial Curve Point --- p.33Chapter 3.6 --- Advantages using LCFs in Axial Deformation --- p.34Chapter 3.6.1 --- Deformation with Smooth Interpolated LCFs --- p.34-37Chapter 3.6.2 --- Used in Closed-curve Deformation --- p.38-39Chapter 3.6.3 --- Hierarchy of Axial Curve --- p.40Chapter 3.6.4 --- Applications in Soft Object Deformation --- p.41Chapter 3.7 --- Experiments and Results --- p.42-46Chapter 4. --- Self Intersection Detection of Axial Curve with LCFs --- p.47-76Chapter 4.1 --- Related Works --- p.48-49Chapter 4.2 --- Algorithms for Solving Self-intersection Problem with a set of LCFs --- p.50-51Chapter 4.2.1 --- The Intersection of Two Plane --- p.52Chapter 4.2.1.1 --- Constructing the Normal Plane --- p.53-54Chapter 4.2.1.2 --- A Line Formed by Two Planes Intersection --- p.55-57Chapter 4.2.1.3 --- Problems --- p.58Chapter 4.2.1.4 --- Sphere as Constraint --- p.59-60Chapter 4.2.1.5 --- Intersecting Line between Two Circular Discs --- p.61Chapter 4.2.2 --- Distance between a Mesh Vertex and a Curve Point --- p.62-63Chapter 4.2.2.1 --- Possible Cases of a Line and a Circle --- p.64-66Chapter 4.3 --- Definition Proof --- p.67Chapter 4.3.1 --- Define the Meaning of Self-intersection --- p.67Chapter 4.3.2 --- Cross Product of Two Vectors --- p.68Chapter 4.4 --- Factors Affecting the Accuracy of the Algorithm --- p.69Chapter 4.3.1 --- High Curvature of the Axial Curve --- p.69-70Chapter 4.3.2 --- Mesh Density of an Object. --- p.71-73Chapter 4.5 --- Architecture of the Self Intersection Algorithm --- p.74Chapter 4.6 --- Experimental Results --- p.75- 79Chapter 5. --- Conclusions and Future Development --- p.80-82Chapter 5.1 --- Contribution and Conclusions --- p.80-81Chapter 5.2 --- Limitations and Future Developments --- p.82References --- p.83-8

Endoluminal surface registration for CT colonography using Haustral Fold Matching

Computed Tomographic (CT) colonography is a technique used for the detection of bowel cancer or potentially precancerous polyps. The procedure is performed routinely with the patient both prone and supine to differentiate fixed colonic pathology from mobile faecal residue. Matching corresponding locations is difficult and time consuming for radiologists due to colonic deformations that occur during patient repositioning. We propose a novel method to establish correspondence between the two acquisitions automatically. The problem is first simplified by detecting haustral folds using a graph cut method applied to a curvature-based metric applied to a surface mesh generated from segmentation of the colonic lumen. A virtual camera is used to create a set of images that provide a metric for matching pairs of folds between the prone and supine acquisitions. Image patches are generated at the fold positions using depth map renderings of the endoluminal surface and optimised by performing a virtual camera registration over a restricted set of degrees of freedom. The intensity difference between image pairs, along with additional neighbourhood information to enforce geometric constraints over a 2D parameterisation of the 3D space, are used as unary and pair-wise costs respectively, and included in a Markov Random Field (MRF) model to estimate the maximum a-posteriori fold labelling assignment. The method achieved fold matching accuracy of 96.0% and 96.1% in patient cases with and without local colonic collapse. Moreover, it improved upon an existing surface-based registration algorithm by providing an initialisation. The set of landmark correspondences is used to non-rigidly transform a 2D source image derived from a conformal mapping process on the 3D endoluminal surface mesh. This achieves full surface correspondence between prone and supine views and can be further refined with an intensity based registration showing a statistically significant improvement (p<0.001p<0.001), and decreasing mean error from 11.9mm11.9mm to 6.0mm6.0mm measured at 1743 reference points from 17 CTC datasets

Robust computation of the rotation minimizing frame for sweep surface modeling

The rotation minimizing frame is superior to the Frenet frame for modeling sweep surfaces [F. Klok, Computer Aided Geometric Design 3, 217-229 (1986)]. However, the existing techniques for computing the rotation minimizing frame either have low approximation degree or are unrobust numerically. We present a method to compute an approximate rotation minimizing frame in a robust and efficient manner. The following problem is studied. Given an axial curve A(u) in space and a 2D cross-section curve C(v), generate a sweep surface S(u, v) = A(u) + F(u)C(v), where F(u) is a rotation minimizing frame defined on A(u). Our method works by approximating A(u) with a Gl circular-arc spline curve and then sweeping C(v) with a rotation minimizing frame along the approximating circular-arc spline curve; the sweep surface thus generated is an approximation of S(u, v). The advantages of this method are: (1) the approximate rotation minimizing frame is computed robustly, with its error being much smaller than would be obtained by Klok's linear method with the same number of segmentations; (2) the sweep surface generated is a NURBS surface if the cross-section curve is a NURBS curve; (3) the method is easily adapted to generating a smooth and closed sweep surface when A(u) is a closed smooth curve. © 1997 Elsevier Science Ltd. All rights reserved.link_to_subscribed_fulltex

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

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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo