A review of feature-based retinal image analysis

Retinal imaging is a fundamental tool in ophthalmic diagnostics. The potential use of retinal imaging within screening programs, with consequent need to analyze large numbers of images with high throughput, is pushing the digital image analysis field to find new solutions for the extraction of specific information from the retinal image. The aim of this review is to explore the latest progress in image processing techniques able to recognize specific retinal image features. and potential features of disease. In particular, this review aims to describe publically available retinal image databases, highlight different performance evaluators commonly used within the field, outline current approaches in feature-based retinal image analysis, and to map related trends. This review found two key areas to be addressed for the future development of automatic retinal image analysis: fundus image quality and the affect image processing may impose on relevant clinical information within the images. Performance evaluators of the algorithms reviewed are very promising, however absolute values are difficult to interpret when validating system suitability for use within clinical practice

Patch-based Denoising Algorithms for Single and Multi-view Images

In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is among the fundamental digital image processing tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels. This thesis proposes two patch-based denoising methods for single and multi-view images, respectively. A modification to the block matching 3D algorithm is proposed for single image denoising. An adaptive collaborative thresholding filter is proposed which consists of a classification map and a set of various thresholding levels and operators. These are exploited when the collaborative hard-thresholding step is applied. Moreover, the collaborative Wiener filtering is improved by assigning greater weight when dealing with similar patches. For the denoising of multi-view images, this thesis proposes algorithms that takes a pair of noisy images captured from two different directions at the same time (stereoscopic images). The structural, maximum difference or the singular value decomposition-based similarity metrics is utilized for identifying locations of similar search windows in the input images. The non-local means algorithm is adapted for filtering these noisy multi-view images. The performance of both methods have been evaluated both quantitatively and qualitatively through a number of experiments using the peak signal-to-noise ratio and the mean structural similarity measure. Experimental results show that the proposed algorithm for single image denoising outperforms the original block matching 3D algorithm at various noise levels. Moreover, the proposed algorithm for multi-view image denoising can effectively reduce noise and assist to estimate more accurate disparity maps at various noise levels

Objetivación en el diagnóstico del síndrome de ojo seco. Correlación entre pruebas clínicas

Mantener la estructura y función de la película lagrimal es esencial para que pueda existir una correcta visión y confort ocular. El Síndrome de Ojo Seco (SOS) es una enfermedad de la unidad funcional lagrimal con un diagnóstico controvertido cuya prevalencia ha aumentado mucho en los últimos años, influyendo a la calidad visual y calidad de vida de las personas. La principal finalidad del presente trabajo es objetivizar tests clínicos de diagnóstico de SOS mediante técnicas semiautomáticas y automáticas de procesado de imagen y video, y desarrollar nuevos protocolos de medida, y así como analizar las relaciones entre los diferentes tests. Para ello, tomando como referencia los principales mecanismos de la patogénesis del Síndrome del ojo seco (la inestabilidad y la hiperosmolaridad lagrimal), la presente tesis se divide en tres partes principales: 1) un conjunto de cuatro estudios de la estabilidad de la película lagrimal donde se realizan análisis de diferentes parámetros de la misma y se valida un test automático de medida; 2) un estudio para validar un test de medida semiautomático para evaluar la altura del menisco lagrimal; y finalmente 3) un estudio donde se proponen y evalúan diferentes métodos de medida para el uso de un osmómetro basado en el descenso del punto de congelación aplicado a la película lagrimal. Los estudios de validación demostraron resultados prometedores en los tests automáticos y semiautomáticos para el estudio de la estabilidad y cantidad de lágrima. Se encontraron además relaciones importantes de la estabilidad lagrimal con la cantidad de lípidos producidos a nivel palpebral, el área y evolución de la velocidad de ruptura y la osmolaridad lagrimal, Así mismo, los protocolos relacionados con el osmómetro basado en el punto de congelación, aunque presentan ciertas limitaciones, pueden aplicarse en lágrima con la dilución adecuad

Deep Learning Techniques for Medical Image Classification

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsIn recent years, artificial intelligence (AI) has been applied in many fields to address complex and critical real-world tasks. Deep learning rises as a subfield of AI, where artificial neural networks (ANN) are used to map complicated functions, which can be challenging even for experienced users. One of the ANN variants is called convolutional neural network (CNN), which has shown great potential in image processing by providing state-of-the-art results for many significant image processing challenges. The medical field can significantly benefit from AI usage, especially in the medical image classification domain. In this doctoral dissertation, we applied different AI techniques to analyze medical images and to give the physicians a second opinion or reduce the time and effort needed for the image classification. Initially, we reviewed several studies that were published to discuss the transfer learning of CNNs. Afterward, we studied different hyperparameters that need to be optimized for CNNs to be trained accurately. Lastly, we proposed a novel CNN architecture to help in the classification of histopathology images

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Wind farm power output prediction based on machine learning recurrent neural networks

Scientists, investors and policy makers have become aware of the importance of providing near accurate prediction of renewable energy. This is why current studies show improvements in methodologies to provide more precise energy predictions. Wind energy is tied to variabilities of weather patterns, especially wind speeds, which are irregular in climates with erratic weather conditions. To predict wind power output, model technologies like autoregressive integrated moving average (ARIMA), variants of ARIMA, hybrid models involving ARIMA and artificial neural networks (ANN), Kalman filters and support vector regressions (SVR) have been applied for wind speed involving short, ultra-short, medium and long terms kind of predictions. ARIMA ensemble with ANN has shown better performance for short and ultra-short terms of two to three hours ahead. On the other hand, SVR, Kalman filters and ensemble of both has recorded good performance for medium-term kinds of wind speed predictions. Recently, neural networks in particular recurrent neural networks (RNN) have reported immense achievement in time series predictions particularly for medium and long-term. This is largely due to its retentive memory-mapping capabilities in fitting sequence in series. These capabilities are short-lived; when the sequence grows over time, the RNN tend to lose correlated information on back-propagation operations. This can lead to errors in the predicted potentials. Therefore, RNNs are exploited for enhanced wind-farm power output prediction. The main contribution of this research is the study of a model involving a combination of RNN regularisation methods using dropout and long short-term memory (LSTM) for wind-power output predictions. In this research, the regularisation method modifies and adapts to the stochastic nature of the wind and is optimised for the wind-farm power output (WFPO) prediction for up to 12-hours ahead – 72-timesteps. This algorithm implements a dropout method to suit the non-deterministic wind speed by applying LSTM to prevent RNN from overfitting. A demonstration for accuracy using the proposed method is performed on a 14-turbine wind farm with up to ten thousand wind samples for model training and five hundred for model validation and testing. The model out performs the ARIMA model with up to 90% accuracy and is expected to be applied to erratic weather condition, especially those observed in an off-shore wind farms

Visual image processing in various representation spaces for documentary preservation

This thesis establishes an advanced image processing framework for the enhancement and restoration of historical document images (HDI) in both intensity (gray-scale or color) and multispectral (MS) representation spaces. It provides three major contributions: 1) the binarization of gray-scale HDI; 2) the visual quality restoration of MS HDI; and 3) automatic reference data (RD) estimation for HDI binarization. HDI binarization is one of the enhancement techniques that produces bi-level information which is easy to handle using methods of analysis (OCR, for instance) and is less computationally costly to process than 256 levels of grey or color images. Restoring the visual quality of HDI in an MS representation space enhances their legibility, which is not possible with conventional intensity-based restoration methods, and HDI legibility is the main concern of historians and librarians wishing to transfer knowledge and revive ancient cultural heritage. The use of MS imaging systems is a new and attractive research trend in the field of numerical processing of cultural heritage documents. In this thesis, these systems are also used for automatically estimating more accurate RD to be used for the evaluation of HDI binarization algorithms in order to track the level of human performance. Our first contribution, which is a new adaptive method of intensity-based binarization, is defined at the outset. Since degradation is present over document images, binarization methods must be adapted to handle degradation phenomena locally. Unfortunately, these methods are not effective, as they are not able to capture weak text strokes, which results in a deterioration of the performance of character recognition engines. The proposed approach first detects a subset of the most probable text pixels, which are used to locally estimate the parameters of the two classes of pixels (text and background), and then performs a simple maximum likelihood (ML) to locally classify the remaining pixels based on their class membership. To the best of our knowledge, this is the first time local parameter estimation and classification in an ML framework has been introduced for HDI binarization with promising results. A limitation of this method in the case with as the intensity-based methods of enhancement is that they are not effective in dealing with severely degraded HDI. Developing more advanced methods based on MS information would be a promising alternative avenue of research. In the second contribution, a novel approach to the visual restoration of HDI is defined. The approach is aimed at providing end users (historians, librarians, etc..) with better HDI visualization, specifically; it aims to restore them from degradations, while keeping the original appearance of the HDI intact. Practically, this problem cannot be solved by conventional intensity-based restoration methods. To cope with these limitations, MS imaging is used to produce additional spectral images in the invisible light (infrared and ultraviolet) range, which gives greater contrast to objects in the documents. The inpainting-based variational framework proposed here for HDI restoration involves isolating the degradation phenomena in the infrared spectral images, and then inpainting them in the visible spectral images. The final color image to visualize is therefore reconstructed from the restored visible spectral images. To the best of our knowledge, this is the first time the inpainting technique has been introduced for MS HDI. The experimental results are promising, and our objective, in collaboration with the BAnQ (Bibliothèque et Archives nationales de Québec), is to push heritage documents into the public domain and build an intelligent engine for accessing them. It is useful to note that the proposed model can be extended to other MS-based image processing tasks. Our third contribution is presented, which is to consider a new problem of RD (reference data) estimation, in order to show the importance of working with MS images rather than gray-scale or color images. RDs are mandatory for comparing different binarization algorithms, and they are usually generated by an expert. However, an expert’s RD is always subject to mislabeling and judgment errors, especially in the case of degraded data in restricted representation spaces (gray-scale or color images). In the proposed method, multiple RD generated by several experts are used in combination with MS HDI to estimate new, more accurate RD. The idea is to include the agreement of experts about labels and the multivariate data fidelity in a single Bayesian classification framework to estimate the a posteriori probability of new labels forming the final estimated RD. Our experiments show that estimated RD are more accurate than an expert’s RD. To the best of our knowledge, no similar work to combine binary data and multivariate data for the estimation of RD has been conducted

Texture Analysis of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images for Characterizing Myocardial Fibrosis and Infarction

Le tiers de la population aux États-Unis est affecté par des cardiomyopathies. Lorsque le muscle du coeur, le myocarde, est altéré par la maladie, la santé du patient est détériorée et peut même entrainer la mort. Les maladies ischémiques sont le résultat d’artères coronariennes bloquées (sténose), limitant l’apport sanguin vers le myocarde. Les cardiomyopathies non-ischémiques sont les maladies dues à d’autres causes que des sténoses. Les fibres de collagène (fibrose) s’infiltrent dans le muscle cardiaque dans le but de maintenir la forme et les fonctions cardiaques lorsque la structure du myocarde est affectée par des cardiomyopathies. Ce principe, nécessaire au fonctionnement du coeur en présence de maladies, devient mal adapté et mène à des altérations du myocarde aux conséquences négatives, par exemple l’augmentation de la rigidité du myocarde. Une partie du diagnostic clinique lors de cardiomyopathies consiste à évaluer la fibrose dans le coeur avec différentes modalités d’imagerie. Les fibres de collagène s’infiltrent et s’accumulent dans la zone extracellulaire du myocarde ou peuvent remplacer progressivement les cardiomyocytes compromises. L’infiltration de fibrose dans le myocarde peut possiblement être réversible, ce qui rend sa détection particulièrement importante pour le clinicien. Différents tests diagnostiques existent pour aider le clinicien à établir l’état du patient en présence de cardiomyopathies. L’imagerie par résonance magnétique (IRM) est une modalité d’imagerie qui offre une haute résolution pour la visualisation du myocarde. Parmi les séquences disponibles avec cette modalité, l’imagerie par rehaussement tardif (RT) augmente le contraste du signal existant entre les tissus sains et les tissues malades du myocarde. Il s’agit d’images en pondération T1 avec administration d’agent de contraste qui se propage dans la matrice extracellulaire et résulte en un rehaussement du signal à cet endroit. Les images IRM RT permettent d’évaluer la présence et l’étendue des dommages au myocarde. Le clinicien peut évaluer la sévérité des cardiomyopathies et poser un pronostique à l’aide de ces images. La détection de fibrose diffuse dans ces images peut informer le clinicien sur l’état du patient et est un important marqueur de cardiomyopathies. Il est important d’établir l’occurrence de l’infarctus en présence de maladies ischémiques. En effet, l’approche interventionnelle varie selon que le clinicien fait face à une ischémie aigue ou chronique. Lors du diagnostic, Il serait donc bénéfique de différencier les infarctus du myocarde aigu de ceux chronique. Ceci s’est avéré difficile à l’aide des images IRM RT où l’intensité du signal ou la taille des régions sont similaires dans les deux types d’ischémie. Le but de la présente thèse est donc d’appliquer les méthodes d’analyse de texture à des images IRM RT afin de détecter la présence de fibrose diffuse dans le myocarde et de plus de déterminer l’âge de l’infarctus du myocarde. La première étude portait sur la détection de fibrose diffuse dans le myocarde à l’aide de l’analyse de texture appliquée à des images IRM RT afin d’établir si un lien existe entre la variation du signal d’intensité et la structure sous-jacente du myocarde. La présence de collagène dans le myocarde augmente avec l’âge et nous avons utilisé un modèle animal de rats jeunes et âgés. Nous avons fait une étude ex-vivo afin d’obtenir des images IRM RT de haute résolution avec absence de mouvement et ainsi permettre une comparaison des images avec des coupes histologiques des coeurs imagés. Des images IRM RT ont été acquises sur vingt-quatre animaux. Les coupes histologiques ont été traitées avec la méthode utilisant un marqueur ‘picrosirius red’ qui donne une teinte rouge au collagène. La quantification de la fibrose obtenue avec les images IRM RT a été comparée à la quantification obtenue sur les coupes histologiques. Ces quantifications ont de plus été comparées à l’analyse de texture appliquée aux images IRM RT. La méthode de texture a été appliquée en créant des cartes de texture basées sur la valeur de Contraste, cette mesure étant obtenue par des calculs statistiques sur la matrice de cooccurrence. Les régions montrant une plus grande complexité de signal d’intensité sur les images IRM RT ont été rehaussées avec les cartes de textures. Un calcul de régression linéaire a permis d’étudier le lien entre les différentes méthodes de quantification. Nous avons trouvés que la quantification de fibrose dans le myocarde à l’aide de l’analyse de texture appliquée sur des images IRM RT concordait avec le niveau de collagène identifié avec les images IRM et avec les coupes histologiques. De plus, nous avons trouvés que l’analyse de texture rehausse la présence de fibrose diffuse dans le myocarde. La seconde étude a pour but de discriminer les infarctus aigus du myocarde de ceux qui sont chroniques sur des images IRM RT de patients souffrant de cardiomyopathies ischémiques. Vingt-deux patients ont subi l’imagerie IRM (12 avec infarctus aigu du myocarde et 12 avec infarctus chronique). Une segmentation des images a permis d’isoler les différentes zones du myocarde, soit la zone d’infarctus, la zone grise au rebord de l’infarctus et la zone du myocarde sain, dans les deux groupes de patients. L’analyse de texture s’est faite dans ces régions en comparant les valeurs obtenues dans les deux groupes. Nous avons obtenu plus de valeurs de texture discriminantes dans la zone grise, en comparaison avec la région du myocarde sain, où aucune valeur de texture n’était significativement différente, et à la zone d’infarctus, où seule la valeur de texture statistique Moyenne était différentes dans les deux groupes. La zone grise a déjà fait l’objet d’études ayant établis cette région comme composée de cardiomyocytes sains entremêlés avec des fibres de collagène. Notre étude montre que cette région peut exhiber des différences structurelles entre les infarctus aigus du myocarde et ceux qui sont chroniques et que l’analyse de texture a réussi à les détecter. L’étude de la présence de collagène dans le myocarde est importante pour le clinicien afin qu’il puisse faire un diagnostic adéquat du patient et pour qu’il puisse faire un choix de traitement approprié. Nous avons montrés que l’analyse de texture sur des images IRM RT de patients peut différencier et même permettre la classification des ischémies aigues des ischémies chroniques, ce qui n’était pas possible avec uniquement ce type d’images. Nous avons de plus démontrés que l’analyse de texture d’images IRM RT permettait d’évaluer le contenu de fibrose diffuse dans un modèle animal de haute résolution avec validation histologique. Une telle relation entre les résultats d’analyse de texture d’images IRM RT et la structure sous-jacente du myocarde n’avait pas été étudiée dans la littérature. Notre méthode pourra être améliorée en effectuant d’autres calculs statistiques sur la matrice de cooccurrence, en testant d’autres méthodes d’analyse de texture et en appliquant notre méthode à de nouvelles séquences d’acquisition IRM, tel les images en pondération T1. D’autres améliorations possibles pourraient porter sur une évaluation de matrice de cooccurrence avec voisinage circulaire suivant la forme du myocarde sur les tranches d’images IRM RT. Plusieurs matrice de cooccurrence pourraient aussi être évaluées en fonction de la position dans l’espace du voisinage afin d’intégrer une composante directionnelle dans les calculs de texture. D’autres études sont nécessaires afin d’établir si une analyse de texture des images IRM RT pourrait différencier le stade de la fibrose pour un même patient lors d’une étude de suivi. De même, d’autres études sont nécessaires afin de valider l’utilisation de texture sur des scanners IRM différents. Établir l’âge de l’infarctus du myocarde permettra de planifier les interventions thérapeutiques et d’évaluer le pronostique pour le patient.----------ABSTRACT A third of the United States population is affected by cardiomyopathies. Impairment of the heart muscle, the myocardium, puts the patient’s health at risk and could ultimately lead to death. Ischemic cardiomyopathies result from lack of blood (ischemia) reaching the myocardium from blocked coronary arteries. Non-ischemic cardiomyopathies are diseases from other etiology than ischemia. Often collagen fibers infiltrate the heart (fibrosis), as a means to maintain its shape and function in the presence of disease that affects the myocardial cellular structure. This necessary phenomenon ultimately becomes maladaptive and results in the heart’s impairment. Part of the heart’s involvement in disease can be assessed through the analysis of myocardial fibrosis. Cardiomyopathy diagnosis involves the investigation of the presence of myocardial fibrosis, either infiltrative, defined as the increased presence of collagen protein in the extracellular space, or replacement fibrosis, when collagen fibers progressively replace diseased cardiomyocytes. The infiltrative fibrosis is believed to be reversible in some instances and consequently, myocardial fibrosis analysis has decisional impact on the interventional procedure that would benefit the health of the patient. The heart contracts and relaxes as it pumps blood to the rest of the body, an action directly impaired by myocardial damage. Any myocardial involvement should be assessed by the clinician to identify the severity of the myocardial damage, establish a prognosis and plan therapeutic intervention. Different diagnostic tests are required to image the myocardium and help the clinician in the diagnostic process. Cardiac magnetic resonance (CMR) imaging has emerged as a high resolution imaging modality that offers precise structural analysis of the heart. Among the different imaging sequences available with CMR, late gadolinium enhancement (LGE) shows the myocardium and enhances any impairments that may exist with the use of a contrast agent. It is a T1-weighted image with extracellular contrast agent (CA) administration. Increased signal intensity in the infarct scar is created from the CA dynamics. LGE CMR imaging offers information on the scar size and its location. The clinician can estimate the severity of the disease and establish prognosis with LGE CMR images. In ischemic cardiomyopathy, it is important to establish the occurrence of the infarction and know the age of the infarct to plan surgical intervention. Differentiation of acute from chronic MI is therefore important in the diagnostic process. In LGE CMR the level of signal intensity or the size of infarction are both similar in acute or in chronic MI. It has therefore been challenging to distinguish acute MI from chronic MI scars with LGE CMR images alone. The aim of this thesis was to investigate texture analysis of LGE CMR images to determine if acute MI could be distinguished from chronic MI and to detect increased presence of diffuse myocardial fibrosis in the myocardium. The first study was performed to investigate if texture analysis of LGE CMR images could detect variations in the presence of diffuse myocardial fibrosis and if the underlying myocardial structure could be related to the texture measures. Collagen content increased with aging and we used an animal model of young versus old rat. An ex-vivo animal model was necessary to allow for higher image resolution in LGE CMR images and to perform validation of our texture measures with histology images. Twenty four animals were scanned for LGE CMR images and texture analysis was applied to the heart images. Histology slices were stained with picrosirius red and collagen fibers were isolated based on their color content. LGE CMR quantification was compared to histological slices of the heart stained with the picrosirius red method. Texture analysis of LGE CMR images was also compared to the original LGE CMR image quantification and to histology. Texture analysis was done by creating contrast texture maps extracted from Haralick’s gray level co-occurrence matrix (GLCM). Regions of complex signal intensity combination were enhanced in LGE CMR images and in contrast texture maps. Regression analysis was performed to assess the level of agreement between the different analysis methods. We found that LGE CMR images could assess the different levels of collagen content in the different aged animal model, and that moreover texture analysis enhanced those differences. The location of enhancement from texture analysis images corresponded to location of increased collagen content in the old compared to the young rat hearts. Histological validation was shown for texture analysis applied to LGE CMR images to assess myocardial fibrosis. Our second study aimed at discriminating acute versus chronic MI from LGE CMR patient images alone through the use of texture analysis. Twenty two patients who had LGE CMR images were included in our study (12 acute and 12 chronic MI). Regional segmentation was performed and texture features were compared in those regions between both groups of patient. Texture analysis resulted in significantly different values between the two groups. More specifically the peri-infarct zone had the most number of discriminative features compared to the remote myocardium which had none and to the infarct core where only the mean features was significantly different. The border zone has been shown to be composed of healthy cardiomyocytes intermingled with the scar’s collagen fibers. Our study indicates this region might exhibit structural differences in the myocardium in acute from chronic MI patients that texture analysis of LGE CMR images can detect. Characterization of myocardial collagen content is important while clinicians analyze the state of the patient since it influences the course of action required to treat cardiomyopathies. LGE CMR images have been thoroughly used and validated to characterize focal myocardial scar, however it was limited in characterizing the age of infarction or quantifying diffuse collagen content. We have shown texture analysis of LGE CMR images alone can differentiate and even classify, acute from chronic MI patients, which was not previously possible. Characterization of myocardial infarction according to age will prove important in planning therapeutic interventions in clinical practice. Moreover, we have established texture analysis as a means to characterize the myocardium and detect variation in fibrosis content from high resolution LGE CMR images with histology validation. To our knowledge, such a relation between texture analysis of LGE CMR images and the underlying myocardial structure had not been done previously. Improvements could be done to our method, as we can increase the number of texture features that were analyzed from the GLCM, include other texture analysis methods such as the run-length matrix, and apply our method to other CMR imaging sequences such as T1 mapping. Adapting the GLCM to the heart could also be investigated, such as considering circular GLCM computation to consider the round shape of the myocardium in the short axis LGE CMR image slices. Directional GLCM could also be computed individually and analyzed for any myocardial or collagen fiber orientation indication. Further analysis is also required to establish if texture analysis could differentiate the age of MI in the same individual through a follow-up study. The measures of texture analysis from LGE CMR images obtained through different CMR scanners remains to be investigated as well. Knowing the age of infarct and evaluating the presence of diffuse myocardial fibrosis will help the clinician plan therapeutic interventions and establish a prognosis for the patient

Design, Development and Implementation Framework for a Postgraduate Non-Surgical Aesthetics Curriculum

Non-surgical aesthetics (NSA) procedures are primarily performed in private clinics away from traditional teaching hospital settings, establishing structured training and education in these procedures during residency training has been challenging. The objective of this study was to design and develop an evidence-based postgraduate curriculum in non-surgical aesthetics. It necessitated determining the current state of training and education for NSA procedures in postgraduate clinical education. Following a design-based research approach, a subsequent systematic literature review and a cross-sectional global-needs assessment study established the need for such a curriculum. Subsequent literature reviews and series of global Delphi studies have informed and guided the design and development of the conceptual framework, core curriculum content and finally, the implementation framework to facilitate the smooth delivery of the programme. The research also incorporated pilot studies for teaching methodology, assessment strategies like “objective structured practical examination (OSPE) and objective structured clinical examination (OSCE)”, which has shown to be very effective. The conceptual framework for curriculum design and development in NSA emerged from the global Delphi study. The conceptual framework is anchored on critical thinking and uses enquiry-based learning to develop information mastery, skills, and values and attitude. Moreover, relevant threshold concepts guided the construction of learning outcomes mapped against the core curriculum. The finding of this study is a crucial first step in bringing an evidence-based structure to training and education in NSA. This thesis will act as a ‘blueprint’ for the policymakers and program directors while curating a postgraduate programme in NSA