Automated Identification of Diabetic Retinopathy: A Survey

Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy

Handheld image acquisition with real-time vision for human-computer interaction on mobile applications

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2019Várias patologias importantes manifestam-se na retina, sendo que estas podem ter origem na própria retina ou então provirem de doenças sistémicas. A retinopatia diabética, o glaucoma e a degeneração macular relacionada com a idade são algumas dessas patologias oculares, e também as maiores causas de cegueira nos países desenvolvidos. Graças à maior prevalência que se tem verificado, tem havido uma aposta cada vez maior na massificação do rastreio destas doenças, principalmente na população mais suscetível de as contrair. Visto que a retina é responsável pela formação de imagens, ou seja, pelo sentido da visão, os componentes oculares que estão localizados anteriormente têm de ser transparentes, permitindo assim a passagem da luz. Isto faz com que a retina e, por sua vez, o tecido cerebral, possam ser examinados de forma não-invasiva. Existem várias técnicas de imagiologia da retina, incluindo a angiografia fluoresceínica, a tomografia de coerência ótica e a retinografia. O protótipo EyeFundusScope (EFS) da Fraunhofer é um retinógrafo portátil, acoplado a um smartphone, que permite a obtenção de imagens do fundo do olho, sem que seja necessária a dilatação da pupila. Utiliza um algoritmo de aprendizagem automática para detetar lesões existentes na retina, que estão normalmente associadas a um quadro de retinopatia diabética. Para além disso, utiliza um sistema de suporte à decisão, que indica a ausência ou presença da referida retinopatia. A fiabilidade deste tipo de algoritmos e o correto diagnóstico por parte de oftalmologistas e neurologistas estão extremamente dependentes da qualidade das imagens adquiridas. A consistência da captura portátil, com este tipo de retinógrafos, está intimamente relacionada com uma interação apropriada com o utilizador. De forma a melhorar o contributo prestado pelo utilizador, durante o procedimento habitual da retinografia, foi desenvolvida uma nova interface gráfica de utilizador, na aplicação Android do EFS. A abordagem pretendida consiste em tornar o uso do EFS mais acessível, e encorajar técnicos não especializados a utilizarem esta técnica de imagem médica, tanto em ambiente clínico como fora deste. Composto por vários elementos de interação, que foram criados para atender às necessidades do protocolo de aquisição de imagem, a interface gráfica de utilizador deverá auxiliar todos os utilizadores no posicionamento e alinhamento do EFS com a pupila do doente. Para além disto, poderá existir um controlo personalizado do tempo despendido em aquisições do mesmo olho. Inicialmente, foram desenhadas várias versões dos elementos de interação rotacionais, sendo posteriormente as mesmas implementadas na aplicação Android. Estes elementos de interação utilizam os dados recolhidos dos sensores inerciais, já existentes no smartphone, para transmitir uma resposta em tempo real ao utilizador enquanto este move o EFS. Além dos elementos de interação rotacionais, também foram implementados um temporizador e um indicador do olho que está a ser examinado. Após a implementação de três configurações com as várias versões dos elementos de interação, procedeu-se à realização dos testes de usabilidade. No entanto, antes desta etapa se poder concretizar, foram realizados vários acertos e correções com a ajuda de um olho fantoma. Durante o planeamento dos testes de usabilidade foi estabelecido um protocolo para os diferentes cenários de uso e foi criado um tutorial com as principais cautelas que os utilizadores deveriam ter aquando das aquisições. Os resultados dos testes de usabilidade mostram que a nova interface gráfica teve um efeito bastante positivo na experiência dos utilizadores. A maioria adaptou-se rapidamente à nova interface, sendo que para muitos contribuiu para o sucesso da tarefa de aquisição de imagem. No futuro, espera-se que a combinação dos dados fornecidos pelos sensores inerciais, juntamente com a implementação de novos algoritmos de reconhecimento de imagem, sejam a base de uma nova e mais eficaz técnica de interação em prática clínica. Além disso, a nova interface gráfica poderá proporcionar ao EFS uma aplicação que sirva exclusivamente para efeitos de formação profissional.Many important diseases manifest themselves in the retina, both primary retinal conditions and systemic disorders. Diabetic retinopathy, glaucoma and age-related macular degeneration are some of the most frequent ocular disorders and the leading causes of blindness in developed countries. Since these disorders are becoming increasingly prevalent, there has been the need to encourage high coverage screening among the most susceptible population. As its function requires the retina to see the outside world, the involved optical components must be transparent for image formation. This makes the retinal tissue, and thereby brain tissue, accessible for imaging in a non-invasive manner. There are several approaches to visualize the retina including fluorescein angiography, optical coherence tomography and fundus photography. The Fraunhofer’s EyeFundusScope (EFS) prototype is a handheld smartphone-based fundus camera, that doesn’t require pupil dilation. It employs advanced machine learning algorithms to process the image in search of lesions that are often associated with diabetic retinopathy, making it a pre-diagnostic tool. The robustness of this computer vision algorithm, as well as the diagnose performance of ophthalmologists and neurologists, is strongly related with the quality of the images acquired. The consistency of handheld capture deeply depends on proper human interaction. In order to improve the user’s contribution to the retinal acquisition procedure, a new graphical user interface was designed and implemented in the EFS Acquisition App. The intended approach is to make the EFS easier to use by non-ophthalmic trained personnel, either in a non-clinical or in a clinical environment. Comprised of several interaction elements that were created to suit the needs of the acquisition procedure, the graphical user interface should help the user to position and align the EFS illumination beam with the patient’s pupil as well as keeping track of the time between acquisitions on the same eye. Initially, several versions of rotational interaction elements were designed and later implemented on the EFS Acquisition App. These used data from the smartphone’s inertial sensors to give real-time feedback to the user while moving the EFS. Besides the rotational interactional elements, a time-lapse and an eye indicator were also designed and implemented in the EFS. Usability tests took place, after three assemblies being successfully implemented and corrected with the help of a model eye ophthalmoscope trainer. Also, a protocol for the different use-case scenarios was elaborated, and a tutorial was created. Results from the usability tests, show that the new graphical user interface had a very positive outcome. The majority of users adapted very quickly to the new interface, and for many it contributed for a successful acquisition task. In the future, the grouping of inertial sensors data and image recognition may prove to be the foundations for a more efficient interaction technique performed in clinical practices. Furthermore, the new graphical user interface could provide the EFS with an application for educational purposes

Fundus image analysis for automatic screening of ophthalmic pathologies

En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad. Por lo tanto, el desarrollo de algoritmos para la creación de sistemas de cribado automáticos juga un papel vital en este campo. La presente tesis persigue la identificacion automática del daño retiniano provocado por dos de las patologías más comunes en la sociedad actual: la retinopatía diabética (RD) y la degenaración macular asociada a la edad (DMAE). Concretamente, el objetivo final de este trabajo es el desarrollo de métodos novedosos basados en la extracción de características de la imagen de fondo de ojo y clasificación para discernir entre tejido sano y patológico. Además, en este documento se proponen algoritmos de pre-procesado con el objetivo de normalizar la alta variabilidad existente en las bases de datos publicas de imagen de fondo de ojo y eliminar la contribución de ciertas estructuras retinianas que afectan negativamente en la detección del daño retiniano. A diferencia de la mayoría de los trabajos existentes en el estado del arte sobre detección de patologías en imagen de fondo de ojo, los métodos propuestos a lo largo de este manuscrito evitan la necesidad de segmentación de las lesiones o la generación de un mapa de candidatos antes de la fase de clasificación. En este trabajo, Local binary patterns, perfiles granulométricos y la dimensión fractal se aplican de manera local para extraer información de textura, morfología y tortuosidad de la imagen de fondo de ojo. Posteriormente, esta información se combina de diversos modos formando vectores de características con los que se entrenan avanzados métodos de clasificación formulados para discriminar de manera óptima entre exudados, microaneurismas, hemorragias y tejido sano. Mediante diversos experimentos, se valida la habilidad del sistema propuesto para identificar los signos más comunes de la RD y DMAE. Para ello se emplean bases de datos públicas con un alto grado de variabilidad sin exlcuir ninguna imagen. Además, la presente tesis también cubre aspectos básicos del paradigma de deep learning. Concretamente, se presenta un novedoso método basado en redes neuronales convolucionales (CNNs). La técnica de transferencia de conocimiento se aplica mediante el fine-tuning de las arquitecturas de CNNs más importantes en el estado del arte. La detección y localización de exudados mediante redes neuronales se lleva a cabo en los dos últimos experimentos de esta tesis doctoral. Cabe destacar que los resultados obtenidos mediante la extracción de características "manual" y posterior clasificación se comparan de forma objetiva con las predicciones obtenidas por el mejor modelo basado en CNNs. Los prometedores resultados obtenidos en esta tesis y el bajo coste y portabilidad de las cámaras de adquisión de imagen de retina podrían facilitar la incorporación de los algoritmos desarrollados en este trabajo en un sistema de cribado automático que ayude a los especialistas en la detección de patrones anomálos característicos de las dos enfermedades bajo estudio: RD y DMAE.In last years, the number of blindness cases has been significantly reduced. Despite this promising news, the World Health Organisation estimates that 80% of visual impairment (285 million cases in 2010) could be avoided if diagnosed and treated early. To accomplish this purpose, eye care services need to be established in primary health and screening campaigns should be a common task in centres with people at risk. However, these solutions entail a high workload for trained experts in the analysis of the anomalous patterns of each eye disease. Therefore, the development of algorithms for automatic screening system plays a vital role in this field. This thesis focuses on the automatic identification of the retinal damage provoked by two of the most common pathologies in the current society: diabetic retinopathy (DR) and age-related macular degeneration (AMD). Specifically, the final goal of this work is to develop novel methods, based on fundus image description and classification, to characterise the healthy and abnormal tissue in the retina background. In addition, pre-processing algorithms are proposed with the aim of normalising the high variability of fundus images and removing the contribution of some retinal structures that could hinder in the retinal damage detection. In contrast to the most of the state-of-the-art works in damage detection using fundus images, the methods proposed throughout this manuscript avoid the necessity of lesion segmentation or the candidate map generation before the classification stage. Local binary patterns, granulometric profiles and fractal dimension are locally computed to extract texture, morphological and roughness information from retinal images. Different combinations of this information feed advanced classification algorithms formulated to optimally discriminate exudates, microaneurysms, haemorrhages and healthy tissues. Through several experiments, the ability of the proposed system to identify DR and AMD signs is validated using different public databases with a large degree of variability and without image exclusion. Moreover, this thesis covers the basics of the deep learning paradigm. In particular, a novel approach based on convolutional neural networks is explored. The transfer learning technique is applied to fine-tune the most important state-of-the-art CNN architectures. Exudate detection and localisation tasks using neural networks are carried out in the last two experiments of this thesis. An objective comparison between the hand-crafted feature extraction and classification process and the prediction models based on CNNs is established. The promising results of this PhD thesis and the affordable cost and portability of retinal cameras could facilitate the further incorporation of the developed algorithms in a computer-aided diagnosis (CAD) system to help specialists in the accurate detection of anomalous patterns characteristic of the two diseases under study: DR and AMD.En els últims anys el nombre de casos de ceguera s'ha reduït significativament. A pesar d'este fet, l'Organització Mundial de la Salut estima que un 80% dels casos de pèrdua de visió (285 milions en 2010) poden ser evitats si es diagnostiquen en els seus estadis més primerencs i són tractats de forma efectiva. Per a complir esta proposta es pretén que els servicis d'atenció primària incloguen un seguiment oftalmològic dels seus pacients així com fomentar campanyes de garbellament en centres regentats per persones d'alt risc. No obstant això, estes solucions exigixen una alta càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia. Per tant, el desenrotllament d'algoritmes per a la creació de sistemes de garbellament automàtics juga un paper vital en este camp. La present tesi perseguix la identificació automàtica del dany retiniano provocat per dos de les patologies més comunes en la societat actual: la retinopatia diabètica (RD) i la degenaración macular associada a l'edat (DMAE) . Concretament, l'objectiu final d'este treball és el desenrotllament de mètodes novedodos basats en l'extracció de característiques de la imatge de fons d'ull i classificació per a discernir entre teixit sa i patològic. A més, en este document es proposen algoritmes de pre- processat amb l'objectiu de normalitzar l'alta variabilitat existent en les bases de dades publiques d'imatge de fons d'ull i eliminar la contribució de certes estructures retinianas que afecten negativament en la detecció del dany retiniano. A diferència de la majoria dels treballs existents en l'estat de l'art sobre detecció de patologies en imatge de fons d'ull, els mètodes proposats al llarg d'este manuscrit eviten la necessitat de segmentació de les lesions o la generació d'un mapa de candidats abans de la fase de classificació. En este treball, Local binary patterns, perfils granulometrics i la dimensió fractal s'apliquen de manera local per a extraure informació de textura, morfologia i tortuositat de la imatge de fons d'ull. Posteriorment, esta informació es combina de diversos modes formant vectors de característiques amb els que s'entrenen avançats mètodes de classificació formulats per a discriminar de manera òptima entre exsudats, microaneurismes, hemorràgies i teixit sa. Per mitjà de diversos experiments, es valida l'habilitat del sistema proposat per a identificar els signes més comuns de la RD i DMAE. Per a això s'empren bases de dades públiques amb un alt grau de variabilitat sense exlcuir cap imatge. A més, la present tesi també cobrix aspectes bàsics del paradigma de deep learning. Concretament, es presenta un nou mètode basat en xarxes neuronals convolucionales (CNNs) . La tècnica de transferencia de coneixement s'aplica per mitjà del fine-tuning de les arquitectures de CNNs més importants en l'estat de l'art. La detecció i localització d'exudats per mitjà de xarxes neuronals es du a terme en els dos últims experiments d'esta tesi doctoral. Cal destacar que els resultats obtinguts per mitjà de l'extracció de característiques "manual" i posterior classificació es comparen de forma objectiva amb les prediccions obtingudes pel millor model basat en CNNs. Els prometedors resultats obtinguts en esta tesi i el baix cost i portabilitat de les cambres d'adquisión d'imatge de retina podrien facilitar la incorporació dels algoritmes desenrotllats en este treball en un sistema de garbellament automàtic que ajude als especialistes en la detecció de patrons anomálos característics de les dos malalties baix estudi: RD i DMAE.Colomer Granero, A. (2018). Fundus image analysis for automatic screening of ophthalmic pathologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99745TESI

Blood vessel detection in retinal images and its application in diabetic retinopathy screening

In this dissertation, I investigated computing algorithms for automated retinal blood vessel detection. Changes in blood vessel structures are important indicators of many diseases such as diabetes, hypertension, etc. Blood vessel is also very useful in tracking of disease progression, and for biometric authentication. In this dissertation, I proposed two algorithms to detect blood vessel maps in retina. The first algorithm is based on integration of a Gaussian tracing scheme and a Gabor-variance filter. This algorithm traces the large blood vessel in retinal images enhanced with adaptive histogram equalization. Small vessels are traced on further enhanced images by a Gabor-variance filter. The second algorithm is called a radial contrast transform (RCT) algorithm, which converts the intensity information in spatial domain to a high dimensional radial contrast domain. Different feature descriptors are designed to improve the speed, sensitivity, and expandability of the vessel detection system. Performances comparison of the two algorithms with those in the literature shows favorable and robust results. Furthermore, a new performance measure based on central line of blood vessels is proposed as an alternative to more reliable assessment of detection schemes for small vessels, because the significant variations at the edges of small vessels need not be considered. The proposed algorithms were successfully tested in the field for early diabetic retinopathy (DR) screening. A highly modular code library to take advantage of the parallel processing power of multi-core computer architecture was tested in a clinical trial. Performance results showed that our scheme can achieve similar or even better performance than human expert readers for detection of micro-aneurysms on difficult images

Segmentation of Optic Disc in Fundus Images using Convolutional Neural Networks for Detection of Glaucoma

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a difficult task due to various anatomical structures like blood vessel, optic cup, optic disc, macula and fovea. Blood vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, microaneurysms and macular degeneration. The segmentation of optic disc and optic cup from retinal images is used to calculate an important indicator, cup-to disc ratio( CDR) accurately to help the professionals in the detection of Glaucoma in fundus images.In this proposed work, an automated segmentation of anatomical structures in fundus images such as blood vessel and optic disc is done using Convolutional Neural Networks (CNN) . A Convolutional Neural Network is a composite of multiple elementary processing units, each featuring several weighted inputs and one output, performing convolution of input signals with weights and transforming the outcome with some form of nonlinearity. The units are arranged in rectangular layers (grids), and their locations in a layer correspond to pixels in an input image. The spatial arrangement of units is the primary characteristics that makes CNNs suitable for processing visual information; the other features are local connectivity, parameter sharing and pooling of hidden units. The advantage of CNN is that it can be trained repeatedly so more features can be found. An average accuracy of 95.64% is determined in the classification of blood vessel or not. Optic cup is also segmented from the optic disc by Fuzzy C Means Clustering (FCM). This proposed algorithm is tested on a sample of hospital images and CDR value is determined. The obtained values of CDR is compared with the given values of the sample images and hence the performance of proposed system in which Convolutional Neural Networks for segmentation is employed, is excellent in automated detection of healthy and Glaucoma images

Deep learning for diabetic retinopathy analysis : a review, research challenges, and future directions

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR