Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets

Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

Hybrid model for vascular tree structures

This paper proposes a new representation scheme of the cerebral blood vessels. This model provides information on the semantics of the vascular structure: the topological relationships between vessels and the labeling of vascular accidents such as aneurysms and stenoses. In addition, the model keeps information of the inner surface geometry as well as of the vascular map volume properties, i.e. the tissue density, the blood flow velocity and the vessel wall elasticity. The model can be constructed automatically in a pre-process from a set of segmented MRA images. Its memory requirements are optimized on the basis of the sparseness of the vascular structure. It allows fast queries and efficient traversals and navigations. The visualizations of the vessel surface can be performed at different levels of detail. The direct rendering of the volume is fast because the model provides a natural way to skip over empty data. The paper analyzes the memory requirements of the model along with the costs of the most important operations on it.Postprint (published version

3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Doctor of Philosophy

dissertationHigh arterial tortuosity, or twistedness, is a sign of many vascular diseases. Some ocular diseases are clinically diagnosed in part by assessment of increased tortuosity of ocular blood vessels. Increased arterial tortuosity is seen in other vascular diseases but is not commonly used for clinical diagnosis. This study develops the use of existing magnetic resonance angiography (MRA) image data to study arterial tortuosity in a range of arteries of hypertensive and intracranial aneurysm patients. The accuracy of several centerline extraction algorithms based on Dijkstra's algorithm was measured in numeric phantoms. The stability of the algorithms was measured in brain arteries. A centerline extraction algorithm was selected based on its accuracy. A centerline tortuosity metric was developed using a curve of tortuosity scores. This tortuosity metric was tested on phantoms and compared to observer-based tortuosity rankings on a test data set. The tortuosity metric was then used to measure and compare with negative controls the tortuosity of brain arteries from intracranial aneurysm and hypertension patients. A Dijkstra based centerline extraction algorithm employing a distance-from-edge weighted center of mass (DFE-COM) cost function of the segmented arteries was selected based on generating 15/16 anatomically correct centerlines in a looping artery iv compared to 15/16 for the center of mass (COM) cost function and 7/16 for the inverse modified distance from edge cost function. The DFE-COM cost function had a lower root mean square error in a lopsided phantom (0.413) than the COM cost function (0.879). The tortuosity metric successfully ordered electronic phantoms of arteries by tortuosity. The tortuosity metric detected an increase in arterial tortuosity in hypertensive patients in 13/13 (10/13 significant at α = 0.05). The metric detected increased tortuosity in a subset of the aneurysm patients with Loeys-Dietz syndrome (LDS) in 7/7 (three significant at α = 0.001). The tortuosity measurement combination of the centerline algorithm and the distance factor metric tortuosity curve was able to detect increases in arterial tortuosity in hypertensives and LDS patients. Therefore the methods validated here can be used to study arterial tortuosity in other hypertensive population samples and in genetic subsets related to LDS

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Evaluation of semiautomated internal carotid artery stenosis quantification from 3-dimensional contrast-enhanced magnetic resonance angiograms

Rationale and Objectives: The performance of a semiautomatic technique for internal carotid artery (ICA) stenosis quantification of the internal carotid artery in contrast-enhanced magnetic resonance angiography was evaluated. Materials and Methods: The degree of stenosis of 52 ICAs was quantified by measuring the cross-sectional area along the center lumen line. This was performed both by 3 independent observers and the semiautomated method. The degree of stenosis was defined as the amount of cross-sectional lumen reduction. Results: Agreement between the method and observers was good (weighted-kappa, kappa(w) = 0.89). Reproducibility of measurements of the semiautomated technique was better (kappa(w) = 0.97) than that of the observers (kappa(w) = 0.76), and the evaluated technique was considerably less time-consuming. Conclusions: Because the user interaction is limited, this technique can be used to replace an expert observer in 3-dimensional stenosis quantification of the ICA at CE-MRA in clinical practice