Analysis of Retinal Image Data to Support Glaucoma Diagnosis

Fundus kamera je široce dostupné zobrazovací zařízení, které umožňuje relativně rychlé a nenákladné vyšetření zadního segmentu oka – sítnice. Z těchto důvodů se mnoho výzkumných pracovišť zaměřuje právě na vývoj automatických metod diagnostiky nemocí sítnice s využitím fundus fotografií. Tato dizertační práce analyzuje současný stav vědeckého poznání v oblasti diagnostiky glaukomu s využitím fundus kamery a navrhuje novou metodiku hodnocení vrstvy nervových vláken (VNV) na sítnici pomocí texturní analýzy. Spolu s touto metodikou je navržena metoda segmentace cévního řečiště sítnice, jakožto další hodnotný příspěvek k současnému stavu řešené problematiky. Segmentace cévního řečiště rovněž slouží jako nezbytný krok předcházející analýzu VNV. Vedle toho práce publikuje novou volně dostupnou databázi snímků sítnice se zlatými standardy pro účely hodnocení automatických metod segmentace cévního řečiště.Fundus camera is widely available imaging device enabling fast and cheap examination of the human retina. Hence, many researchers focus on development of automatic methods towards assessment of various retinal diseases via fundus images. This dissertation summarizes recent state-of-the-art in the field of glaucoma diagnosis using fundus camera and proposes a novel methodology for assessment of the retinal nerve fiber layer (RNFL) via texture analysis. Along with it, a method for the retinal blood vessel segmentation is introduced as an additional valuable contribution to the recent state-of-the-art in the field of retinal image processing. Segmentation of the blood vessels also serves as a necessary step preceding evaluation of the RNFL via the proposed methodology. In addition, a new publicly available high-resolution retinal image database with gold standard data is introduced as a novel opportunity for other researches to evaluate their segmentation algorithms.

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment

Retinal imaging in animal models: Searching for biomarkers of neurodegeneration

There is a pressing need for novel diagnostic and progression biomarkers of neurodegeneration. However, the inability to determine disease duration and stage in patients with Alzheimer’s disease (AD) hinders their discovery. Because animal models of disease allow us to circumvent some of these limitations, they have proven to be of paramount importance in clinical research. Due to the clear optics of the eye, the retina combined with optical coherence tomography (OCT) offers the perfect opportunity to image neurodegeneration in the retina in vivo, non-invasively, directly, quickly, and inexpensively. Based on these premises, our group has worked towards uncovering neurodegeneration-associated changes in the retina of the triple-transgenic mouse model of familial AD (3×Tg-AD). In this work, we present an overview of our work on this topic. We report on thickness variations of the retina and retinal layers/layer aggregates caused by healthy aging and AD-like conditions and discuss the implications of focusing research efforts solely on retinal thickness. We explore what other information is embedded in the OCT data, extracted based on texture analysis and deep-learning approaches, to further identify biomarkers that could be used for early detection and diagnosis. We were able to detect changes in the retina of the animal model of AD as early as 1 month of age. We also discuss our work to develop an optical coherence elastography system to measure retinal elasticity, which can be used in conjunction with conventional OCT. Finally, we discuss the potential application of these technologies in human patients and the steps needed to make OCT a helpful screening tool for the detection of neurodegeneration

The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%

Investigating Tissue Optical Properties and Texture Descriptors of the Retina in Patients with Multiple Sclerosis

PURPOSE: To assess the differences in texture descriptors and optical properties of retinal tissue layers in patients with multiple sclerosis (MS) and to evaluate their usefulness in the detection of neurodegenerative changes using optical coherence tomography (OCT) image segmentation. PATIENTS AND METHODS: 38 patients with MS were examined using Stratus OCT. The raw macular OCT data were exported and processed using OCTRIMA software. The enrolled eyes were divided into two groups, based on the presence of optic neuritis (ON) in the history (MSON+ group, n = 36 and MSON- group, n = 31). Data of 29 eyes of 24 healthy subjects (H) were used as controls. A total of seven intraretinal layers were segmented and thickness as well as optical parameters such as contrast, fractal dimension, layer index and total reflectance were measured. Mixed-model ANOVA analysis was used for statistical comparisons. RESULTS: Significant thinning of the retinal nerve fiber layer (RNFL), ganglion cell/inner plexiform layer complex (GCL+IPL) and ganglion cell complex (GCC, RNFL+GCL+IPL) was observed between study groups in all comparisons. Significant difference was found in contrast in the RNFL, GCL+IPL, GCC, inner nuclear layer (INL) and outer plexiform layer when comparing MSON+ to the other groups. Higher fractal dimension values were observed in GCL+IPL and INL layers when comparing H vs. MSON+ groups. A significant difference was found in layer index in the RNFL, GCL+IPL and GCC layers in all comparisons. A significant difference was observed in total reflectance in the RNFL, GCL+IPL and GCC layers between the three examination groups. CONCLUSION: Texture and optical properties of the retinal tissue undergo pronounced changes in MS even without optic neuritis. Our results may help to further improve the diagnostic efficacy of OCT in MS and neurodegeneration

Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography

Tesis por compendio[ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT.[CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT.[EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data.García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400Compendi

Analysis of retinal nerve fiber layer in fundus images utilizing local binary patterns

Tato práce popisuje metodu LBP (Localy Binary Pattern) v jejích různých formách jako nástroj pro analýzu vrstvy nervových vláken jakožto snímků s texturou a bez textury. První část práce se zabývá vrstvou nervových vláken na sítnici oka, výpadky nervových vláken, a především pak možnostmi snímání sítnice pomocí fundus kamery a vlastnostmi takto získaných dat. Druhá část práce popisuje a vysvětluje metodu LBP využívající lokální binární operátory k popisu textury pomocí histogramů. Z takto vzniklých LBP histogramů je získán soubor příznaků. Různými klasifikačními přístupy lze pak určit, zda nové vzorky patří snímku s výpadkem vrstvy nervových vláken. Tímto se zabývá další část práce. Následně je zkoumána korelace příznaků histogramů těchto snímků s tloušťkou vrstvy nervových vláken v témže místě. Metody popisované v této práci byly otestovány na souboru snímků v programovém prostředí MATLAB a dobré výsledky ukazují, že použitá metoda může najít uplatnění v podpoře diagnostiky glaukomových onemocnění.This work describes LBP (Local Binary Pattern) method in its various forms as a tool for distinguishing images with and without texture. The first part of the essay looks into the retinal nerve fiber layer, loss of the nerve fiber and especially into possibilities of retinal images with help of the fundus camera and into properties of this way received data. Second part of the essay describes and explains the LBP method which uses local binary operators for description of texture by help of histograms. From this way brought force of histograms is possible to gain a complex of features. Due to different classification approaches can then determine if new samples were selected from an image loss of retinal nerve fiber layer (RNFL). This solves the next part of the essay. And then is evaluated the correlation of features of LBP histograms of these images with the thickness of the RNFL in the same place. The methods described in this essay have been tested on a set of images in Matlab program and received results show, that the method can be useful for the diagnosis of glaucoma diseases.

Investigation of the Retinal Biomarkers of Alzheimer’s Disease and Atherosclerosis Using Hyperspectral Images

Le fait que l'oeil puisse être visualisé de manière non invasive ouvre des possibilités de mesure de biomarqueurs pour le diagnostic de conditions à long terme. Selon de nombreuses études, plusieurs maladies cardiovasculaires et neurodégénératives telles que la maladie d’Alzheimer (AD) et l’athérosclérose (ATH) se manifestent dans la rétine sous forme de modifications morphologiques pathologiques et / ou vasculaires. Des méthodes d'imagerie oculaire en deux dimensions et des techniques de tomographie par cohérence optique (OCT) en trois dimensions ont été développées pour fournir des descriptions des structures rétiniennes. Cependant, les images acquises par ces techniques permettent principalement de mesurer les caractéristiques spatiales et pas la variance relative de l’intensité des pixels sur différentes longueurs d’onde, de sorte que d’importantes caractéristiques liées aux tissus peuvent encore rester à découvrir. Dans cette étude, une caméra rétinienne métabolique hyperspectrale (MHRC) a été utilisée pour permettre l'acquisition d'une série d'images rétiniennes obtenues à des longueurs d'onde spécifiques couvrant le spectre du visible au proche infrarouge (NIR). Dans cette technique, le facteur de transmission, l'absorption et la diffusion de la lumière sont reflétés dans le spectre de la lumière émise par le tissu. Par conséquent, non seulement les caractéristiques spatiales communes mais également les « signatures spectrales » de biomolécules pourraient être révélées. Cela aide à trouver une plus grande variété de caractéristiques spatiales / spectrales pour une investigation plus précise des biomarqueurs rétiniens des maladies. En ce qui concerne les coûts et les limites associés aux diagnostics actuels de l’AD et de l’ATH, le but de cette thèse était d’analyser le contenu en informations d’images rétiniennes hyperspectrales riches en données dans le but de caractériser des informations discriminantes cachées liées aux tissus afin d’identifier des biomarqueurs possibles de ces deux maladies. À cette fin, une combinaison de caractéristiques vasculaires et de mesures de textures spatiales-spectrales ont été extraites de différentes régions anatomiques de la rétine. Dans le contexte de la maladie d'Alzheimer, des images rétiniennes de 20 cas présentant une altération cognitive et de 26 cas normaux cognitivement ont été acquises à l'aide de la caméra MHRC. Le statut amyloïde cérébral a été déterminé à partir de lectures binaires effectuées par un panel de 3 experts noteurs ayant participé à des études de TEP au 18F-Florbetaben. Des caractéristiques de l’image rétinienne ont été calculées, notamment la tortuosité et le diamètre des vaisseaux, ainsi que les mesures de textures spatiales-spectrales sur les artérioles, les veinules et le tissu environnant. Les veinules rétiniennes des sujets amyloïdes positifs (Aβ +) ont présenté une tortuosité moyenne plus élevée par rapport aux sujets amyloïdes négatifs (Aβ-). Le diamètre artériolaire des sujets Aβ + s'est avéré supérieur à celui des sujets Aβ- dans une zone adjacente à la tête du nerf optique. De plus, une différence significative entre les mesures de texture construites sur les artérioles rétiniennes et leurs régions adjacentes a été observée chez les sujets Aβ + par rapport aux Aβ-. Dans le contexte de l'ATH, 60 images rétiniennes de 30 ATH probables sur le plan clinique et 30 cas de contrôle ont été acquises. Les critères d'inclusion pour les sujets souffrant d'ATH comprenaient: l'infarctus du myocarde; angiographie coronaire montrant au moins une sténose coronaire (plus de 50%); et / ou une angioplastie coronaire; et /ou pontage coronaire. Les artérioles rétiniennes des sujets ATH ont montré un rétrécissement significatif par rapport aux sujets témoins. En outre, une différence significative entre les mesures de textures d'images prises sur les artérioles et les veinules rétiniennes et leurs régions adjacentes a été trouvée entre les sujets ATH et les sujets témoins. Nos études transversales ont montré que l’analyse hyperspectrale des images rétiniennes pouvait discerner avec une précision acceptable l’AD et l’ATH des sujets témoins correspondants.----------ABSTRACT The fact that eye can be visualized non-invasively, opens up possibilities to measure biomarkers for diagnosis of long-term conditions. A significant body of literature has demonstrated that many of the neurodegenerative and cardiovascular diseases such as Alzheimer’s disease (AD) and atherosclerosis (ATH) manifest themselves in retina as pathological and/or vasculature morphological changes. Methods for two-dimensional fundus imaging and techniques for three-dimensional optical coherence tomography (OCT) have been developed to provide descriptions of retinal structures. However, images acquired by these techniques mostly allow for measuring the spatial characteristics of the tissue and lack of the relative variances across differing wavelengths, thus important spectral features may remain uncovered. In this study, a Metabolic Hyperspectral Retinal Camera (MHRC) was used that permits the acquisition of a series of retinal images obtained at specific wavelengths covering the visible and near infrared (NIR) spectrum. In this technique, light transmittance, absorption, and scatter are reflected in the spectrum of light emitted from the tissue. Use of MHRC in this study was aimed to extract not only the common spatial features but also “spectral signatures” of biomolecules in retinal tissue. Regarding the costs and limitations of the current diagnostic methods for AD and ATH, the purpose of this thesis was to analyze the information content of data-rich hyperspectral retinal images to characterize tissue-related discriminatory information to identify possible biomarkers of Alzheimer’s disease and atherosclerosis. To this end, a combination of vascular features and spatial/spectral texture measures were extracted from different anatomical regions of the retina. In the context of AD, retinal images from 20 cognitively impaired and 26 cognitively unimpaired cases were acquired using MHRC. The cerebral amyloid status was determined from binary reads by a panel of three expert raters on 18F-Florbetaben PET studies. Our approach did not aim to visualize directly Aβ deposits in the retina but rather to determine a likely amyloid status based on sets of retinal image features highly correlated with the cerebral amyloid status. Retinal image features were calculated including vessels’ tortuosity and diameter. Spatial/spectral texture measures over arterioles, venules, and tissue around were also extracted. Retinal venules of amyloid positive subjects (Aβ+) showed a higher mean tortuosity compared to the amyloid negative (Aβ-) subjects. Arteriolar diameter of Aβ+ subjects was found to be higher than the Aβ- subjects in a zone adjacent to the optical nerve head. Furthermore, a significant difference between spatial/spectral texture measures built over retinal arterioles and surrounding tissues were observed in Aβ+ subjects when compared to the Aβ-. In the context of ATH, 60 retinal images from 30 clinically probable ATH and 30 control cases were acquired. Inclusion criteria for subjects suffering from ATH included: myocardial infarction; coronary angiography showing at least one coronary stenosis (more than 50%); and/or coronary angioplasty; and/or coronary bypass. Retinal arterioles of ATH subjects showed a significant narrowing when compared to control subjects. Moreover, a significant difference between image texture measures taken over retinal arterioles and retinal venules and their adjacent regions was observed between ATH subjects and control subjects. Our cross-sectional studies have shown that hyperspectral retinal image analysis could be used to discriminate AD and ATH from corresponding control subjects based on a non-invasive eye scan