Blood vessel tracking in MRA images (original) (raw)

Vessel Centerline Tracking in CTA and MRA Images Using Hough Transform

Lecture Notes in Computer Science, 2010

Vascular disease is characterized by any condition that affects the circulatory system. Recently, a demand for sophisticated software tools that can characterize the integrity and functional state of vascular networks from different vascular imaging modalities has appeared. Such tools face significant challenges such as: large datasets, similarity in intensity distributions of other organs and structures, and the presence of complex vessel geometry and branching patterns. Towards that goal, this paper presents a new approach to automatically track vascular networks from CTA and MRA images. Our methodology is based on the Hough transform to dynamically estimate the centerline and vessel diameter along the vessel trajectory. Furthermore, the vessel architecture and orientation is determined by the analysis of the Hessian matrix of the CTA or MRA intensity distribution. Results are shown using both synthetic vessel datasets and real human CTA and MRA images. The tracking algorithm yielded high reproducibility rates, robustness to different noise levels, associated with simplicity of execution, which demonstrates the feasibility of our approach.

A centerline-based estimator of vessel bifurcations in angiography images

Medical Imaging 2013: Computer-Aided Diagnosis, 2013

The analysis of vascular structure based on vessel diameters, density and distance between bifurcations is an important step towards the diagnosis of vascular anomalies. Moreover, vascular network extraction allows the study of angiogenesis. This work describes a technique that detects bifurcations in vascular networks in magnetic resonance angiography and computed tomography angiography images. Initially, a vessel tracking technique that uses the Hough transform and a matrix composed of second order partial derivatives of image intensity is used to estimate the scale and vessel direction, respectively. This semi-automatic technique is capable of connecting isolated tracked vessel segments and extracting a full tree from a vascular network with minimal user intervention. Vessel shape descriptors such as curvature are then used to identify bifurcations during tracking and to estimate the next branch direction. We have initially applied this technique on synthetic datasets and then on real images.

Extraction of Tubular Network From 3D Angiography Using Hough Transform

2016

Computational tools for analysis of vascular structure is an important step towards the diagnosis of vascular<br>anomalies. Vessel diameters, density and distance between bifurcations and tortuosity are fundamental information for distinguish anomaly vessels and can allow the study of angiogenesis. This work describes a technique that detects bifurcations of tubular networks in 3D images: synthetic, magnetic resonance angiography and computed tomography angiography images.Initially a centerline-based tracking using the Hough Transform as scale estimator is performed, then this semi-automatic technique is capable of detecting bifurcation points and use this points as a new tracking seed for extracting a full tree from a tubular network with minimal user intervention. A new measure bifurcation estimator (BE) is based on shape descriptors such as curvature to identify bifurcations and to estimate the next branch direction. Tests were performed on synthetic datasets and real images.

Vessel segmentation and tracking using a two-dimensional model

2005

The segmentation and analysis of blood vessels in retinal images is of immense interest for study of diseases involving vasculature changes. An algorithm to segment blood vessels in colour retinal images by tracking of vessels is described. This algorithm proceeds by fitting a physically inspired two-dimensional model of the vessel profile to a local region of the vessel. By fitting in this manner a number of parameters, such as diameter and orientation, of the local vessel segment, can be accurately measured as tracking proceeds. A modification to the model that enables tracking of tortuous vessels is described. A method to detect vessel branches is also described. Illustrations of the vessel tracking working on retinal images are given.

Blood Vessel Segmentation and Centerline Tracking Using Local Structure Analysis

IFMBE Proceedings, 2015

Blood vessel visualization is important for improving planning and navigation of several interventional procedures. In this paper, we present a novel method for simultaneous blood vessel segmentation, centerline tracing and radius estimation. Our method is based on local structure analysis within the connected regions of the blood vessels. The proposed method is mainly divided into trunk analysis and bifurcation analysis. In the trunk analysis, the vessel is segmented using 2D cross-section analysis while in the bifurcation analysis, the vessel is segmented using modified vesselness. Our method was evaluated on angiogram images. When comparing the processing time for finding the blood vessel centerline, our proposed method was found to be on average more than 20 times faster than multiscale vesselness with thinning and more than 7 times faster than our own earlier method of blood vessel centerline extraction. Also, the centerline extraction was found to be accurate with a mean error less than 1 voxel in comparison to the corresponding geometric centers.

Automatic model-based tracing algorithm for vessel segmentation and diameter estimation

Computer Methods and Programs in Biomedicine, 2010

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 0 ( 2 0 1 0 ) 108-122 Vessel diameter measurement Vessel parametric model Automatic tracking a b s t r a c t An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation.

Recursive tracking of vascular tree axes in 3D medical images

International Journal of Computer Assisted Radiology and Surgery, 2007

Object This article describes a method for automated extraction of branching structures in three dimensional (3D) medical images. Materials and methods The algorithm recursively tracks branches and detects bifurcations by analyzing the binary connected components on the surface of a sphere that moves along the vessels. Local segmentation within the sphere is performed using a clustering algorithm based on both geometric and intensity information. It minimizes a combination of the intra-class intensity variances and of the inertia moment of the “vessel” class, which emphasizes the cylindrical structures. The algorithm was applied to 16 MRA and 12 CTA 3D images of different anatomic regions. Its capability of extracting all the branches and avoiding spurious detections was evaluated by comparing the number of extracted branches with the number of branches found by visual inspection of the datasets. Its reproducibility and sensitivity to parameter variation were also assessed. Results With a fixed parameter setting, 68 out of 286 perceptible branches were missed or partly extracted and 11 spurious branches were obtained. Increasing the weight of the geometric criterion helped in tracking the principal branches in noisy data but increased the number of missed branches. Processing time was within 5 min per dataset. Conclusion From one initial point, the algorithm extracts a vascular tree where the differences of size and of intensity between the branches are not large. Missed sub-trees can be recovered using additional starting points.

Automatic tracking of neuro vascular tree paths

Medical Imaging 2006: Image Processing, 2006

3-D analysis of blood vessels from volumetric CT and MR datasets has many applications ranging from examination of pathologies such as aneurysm and calcification to measurement of cross-sections for therapy planning. Segmentation of the vascular structures followed by tracking is an important processing step towards automating the 3-D vessel analysis workflow. This paper demonstrates a fast and automated algorithm for tracking the major arterial structures that have been previously segmented. Our algorithm uses anatomical knowledge to identify the start and end points in the vessel structure that allows automation. Voxel coding scheme is used to code every voxel in the vessel based on its geodesic distance from the start point. A shortest path based iterative region growing is used to extract the vessel tracks that are subsequently smoothed using an active contour method. The algorithm also has the ability to automatically detect bifurcation points of major arteries. Results are shown for tracking the major arteries such as the common carotid, internal carotid, vertebrals, and arteries coming off the Circle of Willis across multiple cases with various data related and pathological challenges from 7 CTA cases and 2 MR Time of Flight (TOF) cases.

Semi-automated software for the three-dimensional delineation of complex vascular networks

Journal of Microscopy, 2003

The understanding of tumour angiogenesis is of great importance in cancer research, as is the tumour response to vasculartargeted drugs. This paper presents software aimed at aiding these investigations and other situations where linear or dendritic structures are to be delineated from threedimensional (3D) data sets. This software application was written to analyse the data from 3D data sets by allowing the manual and semi-automated tracking and delineation of the vascular tree, including the measurement of vessel diameter. A new algorithm, CHARM, based on a compact Hough transform and the formation of a radial map, has been used to locate vessel centres and measure diameters automatically. The robustness of this algorithm to image smoothing and noise has been investigated.

Geometrical and Structural Analysis of Vessel Systems in 3D Medical Image Datasets

Medical Imaging Systems Technology, 2005

We present several methods for the analysis and visualization of vessel systems in 3D CT and MR image datasets, including segmentation, skeletonization, topological and morphometrical analysis methods. We describe a number of clinical and medical applications, including quantitative vessel diagnostic, automatic detection of aneurysms, liver surgery planning, and simulation of vascular trees. The applications are implemented as software prototypes based on a research and development platform for medical imaging and rapid application prototyping. Most of the applications have been evaluated under clinical conditions.