48 research outputs found
Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features
Atherosclerotic plaque rupture is the most common mechanism responsible for a majority
of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin
cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound
(VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue.
However, it has limitations in terms of providing clinically relevant information for identifying
vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS
images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by
means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean
distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different
geometric and informative texture features to capture the varying heterogeneity of plaque components
and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection
has only focused on the geometric features. Three commonly used statistical texture features are
extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix
(GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in
order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN
(K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best
classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and
accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed
method, with accuracy rates of 98.61% for TCFA plaque.This research was funded by Universiti Teknologi Malaysia (UTM) under Research University
Grant Vot-02G31, and the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant
Scheme (FRGS Vot-4F551) for the completion of the research. The work and the contribution were also supported
by the project Smart Solutions in Ubiquitous Computing Environments, Grant Agency of Excellence, University
of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2018).
Furthermore, the research is also partially supported by the Spanish Ministry of Science, Innovation and
Universities with FEDER funds in the project TIN2016-75850-R
Combinatorial optimisation for arterial image segmentation.
Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods
2D and 3D segmentation of medical images.
"Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.
Automatic segmentation of cross-sectional coronary arterial images
We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels
Quantifying atherosclerosis in vasculature using ultrasound imaging
Cerebrovascular disease accounts for approximately 30% of the global burden
associated with cardiovascular diseases [1]. According to the World Stroke
Organisation, there are approximately 13.7 million new stroke cases annually,
and just under six million people will die from stroke each year [2]. The
underlying cause of this disease is atherosclerosis – a vascular pathology
which is characterised by thickening and hardening of blood vessel walls.
When fatty substances such as cholesterol accumulate on the inner linings of
an artery, they cause a progressive narrowing of the lumen referred to as a
stenosis.
Localisation and grading of the severity of a stenosis, is important for
practitioners to assess the risk of rupture which leads to stroke. Ultrasound
imaging is popular for this purpose. It is low cost, non-invasive, and permits a
quick assessment of vessel geometry and stenosis by measuring the intima
media thickness. Research is showing that 3D monitoring of plaque
progression may provide a better indication of sites which are at risk of
rupture. Various metrics have been proposed. From these, the quantification
of plaques by measuring vessel wall volume (VWV) using the segmented
media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has
been shown to be sensitive to temporal changes in carotid plaque burden.
Thus, methods to segment these boundaries are required to help generate
VWV measurements with high accuracy, less user interaction and increased
robustness to variability in di↵erent user acquisition protocols.ii
This work proposes three novel methods to address these requirements, to
ultimately produce a highly accurate, fully automated segmentation algorithm
which works on intensity-invariant data. The first method proposed was that
of generating a novel, intensity-invariant representation of ultrasound data by
creating phase-congruency maps from raw unprocessed radio-frequency
ultrasound information. Experiments carried out showed that this
representation retained the necessary anatomical structural information to
facilitate segmentation, while concurrently being invariant to changes in
amplitude from the user. The second method proposed was the novel
application of Deep Convolutional Networks (DCN) to carotid ultrasound
images to achieve fully automatic delineation of the MAB boundaries, in
addition to the use of a novel fusion of amplitude and phase congruency data
as an image source. Experiments carried out showed that the DCN produces
highly accurate and automated results, and that the fusion of amplitude and
phase yield superior results to either one alone. The third method proposed
was a new geometrically constrained objective function for the network's
Stochastic Gradient Descent optimisation, thus tuning it to the segmentation
problem at hand, while also developing the network further to concurrently
delineate both the MAB and LIB to produce vessel wall contours. Experiments
carried out here also show that the novel geometric constraints improve the
segmentation results on both MAB and LIB contours.
In conclusion, the presented work provides significant novel contributions to
field of Carotid Ultrasound segmentation, and with future work, this could lead
to implementations which facilitate plaque progression analysis for the end�user
Intravascular Ultrasound
Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives