An improved classification approach for echocardiograms embedding temporal information

Cardiovascular disease is an umbrella term for all diseases of the heart. At present, computer-aided echocardiogram diagnosis is becoming increasingly beneficial. For echocardiography, different cardiac views can be acquired depending on the location and angulations of the ultrasound transducer. Hence, the automatic echocardiogram view classification is the first step for echocardiogram diagnosis, especially for computer-aided system and even for automatic diagnosis in the future. In addition, heart views classification makes it possible to label images especially for large-scale echo videos, provide a facility for database management and collection. This thesis presents a framework for automatic cardiac viewpoints classification of echocardiogram video data. In this research, we aim to overcome the challenges facing this investigation while analyzing, recognizing and classifying echocardiogram videos from 3D (2D spatial and 1D temporal) space. Specifically, we extend 2D KAZE approach into 3D space for feature detection and propose a histogram of acceleration as feature descriptor. Subsequently, feature encoding follows before the application of SVM to classify echo videos. In addition, comparison with the state of the art methodologies also takes place, including 2D SIFT, 3D SIFT, and optical flow technique to extract temporal information sustained in the video images. As a result, the performance of 2D KAZE, 2D KAZE with Optical Flow, 3D KAZE, Optical Flow, 2D SIFT and 3D SIFT delivers accuracy rate of 89.4%, 84.3%, 87.9%, 79.4%, 83.8% and 73.8% respectively for the eight view classes of echo videos

Developing Ultrasound-Based Computer-Aided Diagnostic Systems Through Statistical Pattern Recognition

Computer-aided diagnosis (CAD) is the use of a computer software to help physicians having a better interpretation of medical images. CAD systems can be viewed as pattern recognition algorithms that identify suspicious signs on a medical image and complement physicians' judgments, by reducing inter-/intra-observer variability and subjectivity. The proposed CAD systems in this thesis have been designed based on the statistical approach to pattern recognition as the most successfully used technique in practice. The main focus of this thesis has been on designing (new) feature extraction and classification algorithms for ultrasound-based CAD purposes. Ultrasound imaging has a broad range of usage in medical applications because it is a safe device which does not use harmful ionizing radiations, it provides clinicians with real-time images, it is portable and relatively cheap. The thesis was concerned with developing new ultrasound-based systems for the diagnosis of prostate cancer (PCa) and myocardial infarction (MI) where these issues have been addressed in two separate parts. In the first part, 1) a new CAD system was designed for prostate cancer biopsy by focusing on handling uncertainties in labels of the ground truth data, 2) the appropriateness of the independent component analysis (ICA) method for learning features from radiofrequency (RF) signals, backscattered from prostate tissues, was examined and, 3) a new ensemble scheme for learning ICA dictionaries from RF signals, backscattered from a tissue mimicking phantom, was proposed. In the second part, 1) principal component analysis (PCA) was used for the statistical modeling of the temporal deformation patterns of the left ventricle (LV) to detect abnormalities in its regional function, 2) a spatio-temporal representation of LV function based on PCA parameters was proposed to detect MI and, 3) a local-to-global statistical shape model based on PCA was presented to detect MI

Bayesian spatial and temporal epidemiology of non-communicable diseases and mortality

Spatial epidemiology combines spatial statistical modelling and disease epidemiology for studying geographic variation in mortality and morbidity. The effects of putative risk factors may be examined using ecological regression models. On the other hand, age-period-cohort models can be used to study the variation of mortality and morbidity through time. Bayesian hierarchical statistical models offer a flexible framework for these studies and enable the estimation of uncertainties in the results. The models are usually estimated using computer-intensive Markov chain Monte Carlo simulations. In this dissertation the first four publications present practical epidemiological studies on geographic variation in non-communicable diseases in Finland. In the last publication we study the long-time variation in all-cause mortality in several European countries. New statistical models are developed for these studies. This work provides new epidemiological information on the geographic variation of acute myocardial infarctions (AMI), ischaemic stroke and parkinsonism in Finland. An extended model for studying shared and disease specific geographic variation is presented using data on AMI and ischaemic stroke incidence. Existing results on the inverse association of water hardness and AMI are refined. New models for interpolation of geochemical data with non-detected values are presented with case studies using real data. Finally, the Bayesian age-period-cohort model is extended with versatile interactions and better prediction ability. The model is then used to study long-term variation in mortality in Europe

An image segmentation and registration approach to cardiac function analysis using MRI

Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent years, significant progress has been made in the care and treatment of patients with such diseases. A crucial factor for this progress has been the development of magnetic resonance (MR) imaging which makes it possible to diagnose and assess the cardiovascular function of the patient. The ability to obtain high-resolution, cine volume images easily and safely has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during the image acquisition process. With the development of advanced MR imaging acquisition technologies, 3D MR imaging is more and more clinically feasible. This recent development has allowed new potentially 3D image analysis technologies to be deployed. However, quantitative analysis of cardiovascular system from the images remains a challenging topic. The work presented in this thesis describes the development of segmentation and motion analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic resonance (CMR) images. The first main contribution of the thesis is the development of a fully automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art techniques. The proposed segmentation technique is capable of generating an accurate 3D segmentation from multiple image sequences. The proposed segmentation technique is robust even in the presence of pathological changes, large anatomical shape variations and locally varying contrast in the images. Another main contribution of this thesis is the development of motion tracking techniques that can integrate motion information from different sources. For example, the radial motion of the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of both longitudinal and circumferential motion. We propose a novel technique based on non-rigid image registration for the myocardial motion estimation using both untagged and 3D tagged MR images. The novel aspect of our technique is its simultaneous use of complementary information from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted to maximise the utility of information from both images. The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard FFD approach, a significant increase in registration accuracy can be observed in datasets with discontinuous motion patterns. Both the segmentation and motion tracking techniques presented in this thesis have been applied to clinical studies. We focus on two important clinical applications that can be addressed by the techniques proposed in this thesis. The first clinical application aims at measuring longitudinal changes in cardiac morphology and function during the cardiac remodelling process. The second clinical application aims at selecting patients that positively respond to cardiac resynchronization therapy (CRT). The final chapter of this thesis summarises the main conclusions that can be drawn from the work presented here and also discusses possible avenues for future research

Analysis of cardiac motion using MRI and nonrigid image registration

Imperial Users onl

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

Advances in Digital Processing of Low-Amplitude Components of Electrocardiosignals

This manual has been published within the framework of the BME-ENA project under the responsibility of National Technical University of Ukraine. The BME-ENA “Biomedical Engineering Education Tempus Initiative in Eastern Neighbouring Area”, Project Number: 543904-TEMPUS-1-2013-1-GR-TEMPUS-JPCR is a Joint Project within the TEMPUS IV program. This project has been funded with support from the European Commission.Навчальний посібник присвячено розробці методів та засобів для неінвазивного виявлення та дослідження тонких проявів електричної активності серця. Особлива увага приділяється вдосконаленню інформаційного та алгоритмічного забезпечення систем електрокардіографії високого розрізнення для ранньої діагностики електричної нестабільності міокарда, а також для оцінки функціонального стану плоду під час вагітності. Теоретичні основи супроводжуються прикладами реалізації алгоритмів за допомогою системи MATLAB. Навчальний посібник призначений для студентів, аспірантів, а також фахівців у галузі біомедичної електроніки та медичних працівників.The teaching book is devoted to development and research of methods and tools for non-invasive detection of subtle manifistations of heart electrical activity. Particular attention is paid to the improvement of information and algorithmic support of high resolution electrocardiography for early diagnosis of myocardial electrical instability, as well as for the evaluation of the functional state of the fetus during pregnancy examination. The theoretical basis accompanied by the examples of implementation of the discussed algorithms with the help of MATLAB. The teaching book is intended for students, graduate students, as well as specialists in the field of biomedical electronics and medical professionals

Health effects of air pollution : innovative approaches for spatio-temporal evaluations

Air pollution is one of the major risk factors to human health, causing both short- and long-term effects and the global burden on mortality is estimated in more than 4 million deaths every year. Most of the evidence on the short-term effects is based on studies conducted in major cities, because data or estimates of air pollutants exposures in non-urban settings have been historically lacking. This is a limitation, because a large fraction of the population lives outside the cities, where the vulnerability profile is different from that of urban populations. In the last decade, several attempts were made to estimate daily concentrations of particulate matter (PM) with high spatial resolution over large geographical domains. However, applications in Italy and Sweden, and on other pollutants as nitrogen dioxide (NO2) and ozone (O3), are almost lacking, leaving a gap in the knowledge of their health effects outside cities. This thesis has been designed to fill this gap, by providing daily estimates of multiple air pollutants at the national level, and exploring the spatial heterogeneity in their health effects. Italy represented a testing ground for the development of innovative mixed-effects regression models which combined PM measurements with satellite data, land-use parameters and meteorological fields, and produced daily estimates of PM10 (PM with diameter smaller than 10 m) for each squared kilometer of the country, and each day in 2006-2012 (Study I). More recently, machine learning methodologies have been tested in the U.S., therefore, we have updated estimates of PM10 till 2015 and produced new estimates of PM2.5 (PM < 2.5 m), using a random forest (RF) algorithm (Study II). We replicated the same approach in Sweden, to which we added models for NO2 and O3, and a few spatiotemporal predictors aimed at capturing sources of air pollutants’ variations missed in the previous studies (Study III). We collected national data on hospital discharges for all Italian public and private hospitals during 2013-2015. We created municipality-specific time-series of daily counts of acute admissions for multiple cardiovascular (CVD) endpoints, which we related to daily mean PM10 and PM2.5 concentrations. We found evidence of adverse effects of PM on total CVD admissions and on specific outcomes such as heart failure and atrial fibrillation. Also, we estimated highest effects at the lowest PM concentrations, also in non-urban municipalities (Study IV). Similarly, we collected daily mortality counts at small area level in the Stockholm county, that we analyzed in relation to daily mean exposure to PM10, PM2.5, NO2 and O3. We found evidence of an association between daily O3 and non-accidental mortality in the year-round analysis, and significant associations with PM and O3 in the warm (April-September) period only. Effects were slightly higher in more densely inhabited areas, but we found associations also in non- urban areas outside the Stockholm city (Study V). In conclusion, we developed novel spatiotemporal models to estimate air pollutant concentrations at fine spatial and temporal resolution in Italy and Sweden. These allowed us to document adverse short-term effects on mortality and morbidity at very low concentrations and in areas (and among populations) previously neglected by epidemiological investigations