85 research outputs found
Automatic Spatiotemporal Analysis of Cardiac Image Series
RĂSUMĂ
Ă ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
dĂ©cĂšs en AmĂ©rique du Nord. Chez lâadulte et au sein de populations de plus en plus jeunes,
la soi-disant Ă©pidĂ©mie dâobĂ©sitĂ© entraĂźnĂ©e par certaines habitudes de vie tels que la mauvaise
alimentation, le manque dâexercice et le tabagisme est lourde de consĂ©quences pour les personnes
affectées, mais aussi sur le systÚme de santé. La principale cause de morbidité et de
mortalitĂ© chez ces patients est lâathĂ©rosclĂ©rose, une accumulation de plaque Ă lâintĂ©rieur des
vaisseaux sanguins à hautes pressions telles que les artÚres coronaires. Les lésions athérosclérotiques
peuvent entraĂźner lâischĂ©mie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mĂšne souvent Ă de graves consĂ©quences telles quâun infarctus. Outre les
problÚmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il sâagit dâune vasculite aigĂŒe pouvant affecter lâintĂ©gritĂ© structurale des parois des
artĂšres coronaires et mener Ă la formation dâanĂ©vrismes. Dans certains cas, ceux-ci entravent
lâhĂ©modynamie artĂ©rielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectuĂ©s Ă lâaide
dâangiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en sĂ©ries suite Ă lâinfusion artĂ©rielle dâun agent de
contraste. Ces images révÚlent la lumiÚre des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, sâil y a lieu. Parce que les sĂ©ries acquises contiennent de lâinformation
trĂšs dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et dĂ©placement dâorganes), le clinicien base gĂ©nĂ©ralement
son interprĂ©tation sur une seule image angiographique oĂč des mesures gĂ©omĂ©triques sont effectuĂ©es
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
lâangiographie par fluoroscopie soit frĂ©quemment utilisĂ© partout dans le monde et souvent
considĂ©rĂ© comme lâoutil de diagnostic âgold-standardâ pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalitĂ© dâimagerie est malheureusement trĂšs
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des stĂ©noses et des anĂ©vrismes ne peut pas ĂȘtre pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
lâimageur. De plus, la prĂ©sence de lĂ©sions affectant les artĂšres coronaires peut ne pas reflĂ©ter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flowâs hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vesselsâ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
Automatic Spatiotemporal Analysis of Cardiac Image Series
RĂSUMĂ
Ă ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
dĂ©cĂšs en AmĂ©rique du Nord. Chez lâadulte et au sein de populations de plus en plus jeunes,
la soi-disant Ă©pidĂ©mie dâobĂ©sitĂ© entraĂźnĂ©e par certaines habitudes de vie tels que la mauvaise
alimentation, le manque dâexercice et le tabagisme est lourde de consĂ©quences pour les personnes
affectées, mais aussi sur le systÚme de santé. La principale cause de morbidité et de
mortalitĂ© chez ces patients est lâathĂ©rosclĂ©rose, une accumulation de plaque Ă lâintĂ©rieur des
vaisseaux sanguins à hautes pressions telles que les artÚres coronaires. Les lésions athérosclérotiques
peuvent entraĂźner lâischĂ©mie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mĂšne souvent Ă de graves consĂ©quences telles quâun infarctus. Outre les
problÚmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il sâagit dâune vasculite aigĂŒe pouvant affecter lâintĂ©gritĂ© structurale des parois des
artĂšres coronaires et mener Ă la formation dâanĂ©vrismes. Dans certains cas, ceux-ci entravent
lâhĂ©modynamie artĂ©rielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectuĂ©s Ă lâaide
dâangiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en sĂ©ries suite Ă lâinfusion artĂ©rielle dâun agent de
contraste. Ces images révÚlent la lumiÚre des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, sâil y a lieu. Parce que les sĂ©ries acquises contiennent de lâinformation
trĂšs dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et dĂ©placement dâorganes), le clinicien base gĂ©nĂ©ralement
son interprĂ©tation sur une seule image angiographique oĂč des mesures gĂ©omĂ©triques sont effectuĂ©es
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
lâangiographie par fluoroscopie soit frĂ©quemment utilisĂ© partout dans le monde et souvent
considĂ©rĂ© comme lâoutil de diagnostic âgold-standardâ pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalitĂ© dâimagerie est malheureusement trĂšs
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des stĂ©noses et des anĂ©vrismes ne peut pas ĂȘtre pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
lâimageur. De plus, la prĂ©sence de lĂ©sions affectant les artĂšres coronaires peut ne pas reflĂ©ter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flowâs hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vesselsâ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
Object Tracking
Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application
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Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities
YesOne of the common cardiac disorders is a cardiac attack called Myocardial infarction (MI), which occurs due to the blockage of one or more coronary arteries. Timely treatment of MI is important and slight delay results in severe consequences. Electrocardiogram (ECG) is the main diagnostic tool to monitor and reveal the MI signals. The complex nature of MI signals along with noise poses challenges to doctors for accurate and quick diagnosis. Manually studying large amounts of ECG data can be tedious and time-consuming. Therefore, there is a need for methods to automatically analyze the ECG data and make diagnosis. Number of studies has been presented to address MI detection, but most of these methods are computationally expensive and faces the problem of overfitting while dealing real data. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG-Net model is selected as a feature descriptor of the ECG images to describe it with informative features. Considering the limited availability of dataset, ECG data is augmented which has increased the classification performance. A standard well-known database Physikalisch-Technische Bundesanstalt (PTB) Diagnostic ECG is used for the validation of the proposed framework. It is evident from experimental results that the proposed framework achieves a high accuracy surpasses the existing methods. In terms of accuracy, sensitivity, and specificity; VGG-MI1 achieved 99.02%, 98.76%, and 99.17%, respectively, while VGG-MI2 models achieved an accuracy of 99.22%, a sensitivity of 99.15%, and a specificity of 99.49%.This project was funded by University of Jeddah, Jeddah, Saudi Arabia (Project number: UJ-02-018-ICGR)
Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis
The aim of this thesis is to develop automated methods for the analysis of the
spatial patterns, and the functional behaviour of endothelial cells, viewed under
microscopy, with applications to the understanding of atherosclerosis.
Initially, a radial search approach to segmentation was attempted in order to
trace the cell and nuclei boundaries using a maximum likelihood algorithm; it
was found inadequate to detect the weak cell boundaries present in the available
data. A parametric cell shape model was then introduced to fit an equivalent
ellipse to the cell boundary by matching phase-invariant orientation fields of the
image and a candidate cell shape. This approach succeeded on good quality
images, but failed on images with weak cell boundaries. Finally, a support
vector machines based method, relying on a rich set of visual features, and a
small but high quality training dataset, was found to work well on large numbers
of cells even in the presence of strong intensity variations and imaging noise.
Using the segmentation results, several standard shear-stress dependent parameters
of cell morphology were studied, and evidence for similar behaviour
in some cell shape parameters was obtained in in-vivo cells and their nuclei.
Nuclear and cell orientations around immature and mature aortas were broadly
similar, suggesting that the pattern of flow direction near the wall stayed approximately
constant with age. The relation was less strong for the cell and
nuclear length-to-width ratios.
Two novel shape analysis approaches were attempted to find other properties
of cell shape which could be used to annotate or characterise patterns, since a
wide variability in cell and nuclear shapes was observed which did not appear
to fit the standard parameterisations. Although no firm conclusions can yet be
drawn, the work lays the foundation for future studies of cell morphology.
To draw inferences about patterns in the functional response of cells to flow,
which may play a role in the progression of disease, single-cell analysis was performed
using calcium sensitive florescence probes. Calcium transient rates were
found to change with flow, but more importantly, local patterns of synchronisation
in multi-cellular groups were discernable and appear to change with flow.
The patterns suggest a new functional mechanism in flow-mediation of cell-cell
calcium signalling
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 âvulnerable plaqueâ. 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
Ultrasound and computed tomography cardiac image registration
As the trend of the medical intervention moves towards becoming minimally invasive, the role of medical imaging has grown increasingly important. Medical images acquired from a variety of imaging modalities require image preprocessing, information extraction and data analysis algorithms in order for the potentially useful information to be delivered to clinicians so as to facilitate better diagnosis, treatment planning and surgical intervention. This thesis investigates the employment of an affine registration method to register the pre-operative Computed Tomography (CT) and intra-operative Ultrasound cardiac images. The main benefit of registering Ultrasound and CT cardiac images is to compensate the weaknesses and combine the advantages from both modalities. However, the multimodal registration is a complex and challenging task since there is no specific relationship between the intensity values of the corresponding pixels. Image preprocessing methods such as image denoising, edge detection and contour delineation are implemented to obtain the salient and significant features before the registration process. The features-based Scale Invariant Feature Transform (SIFT) method and homography transformation are then applied to find the transformation that aligns the floating image to the reference image. The registration results of three different patient datasets are assessed by the objective performance measures to ensure that the clinically meaningful result are obtained. Furthermore, the relationship between the preoperative CT image and the transformed intra-operative Ultrasound image are evaluated using joint histogram, MI and NMI. Although the proposed framework falls slightly short of achieving the perfect compensation of cardiac movements and deformation, it can be legitimately implemented as an initialisation step for further studies in dynamic and deformable cardiac registration
Implementing decision tree-based algorithms in medical diagnostic decision support systems
As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems.
Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks.
We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospitalâs dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models
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