An automated image processing system for the detection of photoreceptor cells in adaptive optics retinal images

The rapid progress in Adaptive Optics (AO) imaging, in the last decades, has had a transformative impact on the entire approach underpinning the investigations of retinal tissues. Capable of imaging the retina in vivo at the cellular level, AO systems have revealed new insights into retinal structures, function, and the origins of various retinal pathologies. This has expanded the field of clinical research and opened a wide range of applications for AO imaging. The advances in image processing techniques contribute to a better observation of retinal microstructures and therefore more accurate detection of pathological conditions. The development of automated tools for processing images obtained with AO allows for objective examination of a larger number of images with time and cost savings and thus facilitates the use of AO imaging as a practical and efficient tool, by making it widely accessible to the clinical ophthalmic community. In this work, an image processing framework is developed that allows for enhancement of AO high-resolution retinal images and accurate detection of photoreceptor cells. The proposed framework consists of several stages: image quality assessment, illumination compensation, noise suppression, image registration, image restoration, enhancement and detection of photoreceptor cells. The visibility of retinal features is improved by tackling specific components of the AO imaging system, affecting the quality of acquired retinal data. Therefore, we attempt to fully recover AO retinal images, free from any induced degradation effects. A comparative study of different methods and evaluation of their efficiency on retinal datasets is performed by assessing image quality. In order to verify the achieved results, the cone packing density distribution was calculated and correlated with statistical histological data. From the performed experiments, it can be concluded that the proposed image processing framework can effectively improve photoreceptor cell image quality and thus can serve as a platform for further investigation of retinal tissues. Quantitative analysis of the retinal images obtained with the proposed image processing framework can be used for comparison with data related to pathological retinas, as well as for understanding the effect of age and retinal pathology on cone packing density and other microstructures

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Noise Estimation, Noise Reduction and Intensity Inhomogeneity Correction in MRI Images of the Brain

Rician noise and intensity inhomogeneity are two common types of image degradation that manifest in the acquisition of magnetic resonance imaging (MRI) system images of the brain. Many noise reduction and intensity inhomogeneity correction algorithms are based on strong parametric assumptions. These parametric assumptions are generic and do not account for salient features that are unique to specific classes and different levels of degradation in natural images. This thesis proposes the 4-neighborhood clique system in a layer-structured Markov random field (MRF) model for noise estimation and noise reduction. When the test image is the only physical system under consideration, it is regarded as a single layer Markov random field (SLMRF) model, and as a double layer MRF model when the test images and classical priors are considered. A scientific principle states that segmentation trivializes the task of bias field correction. Another principle states that the bias field distorts the intensity but not the spatial attribute of an image. This thesis exploits these two widely acknowledged scientific principles in order to propose a new model for correction of intensity inhomogeneity. The noise estimation algorithm is invariant to the presence or absence of background features in an image and more accurate in the estimation of noise levels because it is potentially immune to the modeling errors inherent in some current state-of-the-art algorithms. The noise reduction algorithm derived from the SLMRF model does not incorporate a regularization parameter. Furthermore, it preserves edges, and its output is devoid of the blurring and ringing artifacts associated with Gaussian and wavelet based algorithms. The procedure for correction of intensity inhomogeneity does not require the computationally intensive task of estimation of the bias field map. Furthermore, there is no requirement for a digital brain atlas which will incorporate additional image processing tasks such as image registration

Development and analysis of Magnetic Resonance Imaging acquisition and reconstruction methods for functional and structural investigation of cardiac and lung tissues.

The imaging of the lung and of the heart are often challenging in magnetic resonance due to the motion of the organs. In order to avoid motion artifacts it is possible to make the acquisition fast enough to fit in the breath-hold, or use some motion management methods in free breathing. A fast image acquisition can be obtained with non-Cartesian acquisition schemes, which require specialized reconstruction methods. In this work the least-squares non-uniform fast Fourier transform (LS-NUFFT) was compared to the standard gridding (GR) taking the direct summation method (DS) as reference. LS-NUFFT obtained lower root mean square error (RMSE), but heavier geometric information loss. The performance improvement of the LS-NUFFT was studied using three regularization methods. The truncated SVD reduced the RMSE of the simple regularization-free LS-NUFFT. Alternatively, the scan time can be shortened with some FOV reduction techniques. For cardiac imaging, the inner volume (IV) reduced-FOV selection was explored for the myocardial T2 mapping. The FOV reduction successfully avoided aliasing and provided a scan time reduction from about 23s to 15s. However, undesired stimulated echoes caused an overestimation in the T2 of about 20%. The effects of the inner volume excitation on the T2 mapping were described and clarified. Finally, motion management was explored for lung imaging in free-breathing, using a non-Cartesian acquisition trajectory. The rotating ultra-fast sequence (RUFIS) was demonstrated to be very suitable for the short T2* lung tissue. The respiratory motion was addressed with three methods: prospective triggering (PT), prospective gating (PG) and retrospective gating (RG). All methods were able to reconstruct a 3D high-resolution dataset. PG and RG could achieve 1.2 mm isotropic resolution in clinically reasonable scan time (~6min). The RG sequence could reconstruct multiple phases of the respiration cycle at cost of higher scan time

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

Surgical Management of Gastroesophageal Reflux in Children: Risk Stratification and Prediction of Outcomes

Introduction: Since the 1980s fundoplication, an operation developed for adults with hiatus hernia and reflux symptoms, has been performed in children with gastroesophageal reflux disease (GORD). When compared to adult outcomes, paediatric fundoplication has resulted in higher failure and revision rates. In the first chapter we explore differences in paradigm, patient population and outcomes. Firstly, symptoms are poorly defined and are measured by instruments of varying quality. Secondly, neurological impairment (NI), prematurity and congenital anomalies (oesophageal atresia, congenital diaphragmatic hernia) are prevalent in children. / Purpose: To develop methods for stratifying paediatric fundoplication risk and predicting outcomes based on symptom profile, demographic factors, congenital and medical history. / Methods: Study objectives are addressed in three opera: a symptom questionnaire development (TARDIS:REFLUX), a randomised controlled trial (RCT) and a retrospective database study (RDS). TARDIS: REFLUX: In the second chapter, digital research methods are used to design and validate a symptom questionnaire for paediatric GORD. The questionnaire is a market-viable smartphone app hosted on a commercial platform and trialed in a clinical pilot study. / RCT: In the third chapter, the REMOS trial is reported. The trial addresses the subset of children with NI and feeding difficulties. Participants are randomized to gastrostomy with or without fundoplication. Notably, pre- and post-operative reflux is quantified using pH-impedance. / RDS: In the fourth chapter, data mining and machine learning strategies are applied to a retrospective paediatric GORD database. Predictive modelling techniques applied include logistic regression, decision trees, random forests and market basket analysis. / Results and conclusion: This work makes two key contributions. Firstly, an effective methodology for development of digital research tools is presented here. Secondly, a synthesis is made of literature, the randomised controlled trial and retrospective database modelling. The resulting product is an evidence-based algorithm for the surgical management of children with GORD