Técnicas de análise de imagens para detecção de retinopatia diabética

Orientadores: Anderson de Rezende Rocha. Jacques WainerTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Retinopatia Diabética (RD) é uma complicação a longo prazo do diabetes e a principal causa de cegueira da população ativa. Consultas regulares são necessárias para diagnosticar a retinopatia em um estágio inicial, permitindo um tratamento com o melhor prognóstico capaz de retardar ou até mesmo impedir a cegueira. Alavancados pela evolução da prevalência do diabetes e pelo maior risco que os diabéticos têm de desenvolver doenças nos olhos, diversos trabalhos com abordagens bem estabelecidas e promissoras vêm sendo desenvolvidos para triagem automática de retinopatia. Entretanto, a maior parte dos trabalhos está focada na detecção de lesões utilizando características visuais particulares de cada tipo de lesão. Além do mais, soluções artesanais para avaliação de necessidade de consulta e de identificação de estágios da retinopatia ainda dependem bastante das lesões, cujo repetitivo procedimento de detecção é complexo e inconveniente, mesmo se um esquema unificado for adotado. O estado da arte para avaliação automatizada de necessidade de consulta é composto por abordagens que propõem uma representação altamente abstrata obtida inteiramente por meio dos dados. Usualmente, estas abordagens recebem uma imagem e produzem uma resposta ¿ que pode ser resultante de um único modelo ou de uma combinação ¿ e não são facilmente explicáveis. Este trabalho objetivou melhorar a detecção de lesões e reforçar decisões relacionadas à necessidade de consulta, fazendo uso de avançadas representações de imagens em duas etapas. Nós também almejamos compor um modelo sofisticado e direcionado pelos dados para triagem de retinopatia, bem como incorporar aprendizado supervisionado de características com representação orientada por mapa de calor, resultando em uma abordagem robusta e ainda responsável para triagem automatizada. Finalmente, tivemos como objetivo a integração das soluções em dispositivos portáteis de captura de imagens de retina. Para detecção de lesões, propusemos abordagens de caracterização de imagens que possibilitem uma detecção eficaz de diferentes tipos de lesões. Nossos principais avanços estão centrados na modelagem de uma nova técnica de codificação para imagens de retina, bem como na preservação de informações no processo de pooling ou agregação das características obtidas. Decidir automaticamente pela necessidade de encaminhamento do paciente a um especialista é uma investigação ainda mais difícil e muito debatida. Nós criamos um método mais simples e robusto para decisões de necessidade de consulta, e que não depende da detecção de lesões. Também propusemos um modelo direcionado pelos dados que melhora significativamente o desempenho na tarefa de triagem da RD. O modelo produz uma resposta confiável com base em respostas (locais e globais), bem como um mapa de ativação que permite uma compreensão de importância de cada pixel para a decisão. Exploramos a metodologia de explicabilidade para criar um descritor local codificado em uma rica representação em nível médio. Os modelos direcionados pelos dados são o estado da arte para triagem de retinopatia diabética. Entretanto, mapas de ativação são essenciais para interpretar o aprendizado em termos de importância de cada pixel e para reforçar pequenas características discriminativas que têm potencial de melhorar o diagnósticoAbstract: Diabetic Retinopathy (DR) is a long-term complication of diabetes and the leading cause of blindness among working-age adults. A regular eye examination is necessary to diagnose DR at an early stage, when it can be treated with the best prognosis and the visual loss delayed or deferred. Leveraged by the continuous expansion of diabetics and by the increased risk that those people have to develop eye diseases, several works with well-established and promising approaches have been proposed for automatic screening. Therefore, most existing art focuses on lesion detection using visual characteristics specific to each type of lesion. Additionally, handcrafted solutions for referable diabetic retinopathy detection and DR stages identification still depend too much on the lesions, whose repetitive detection is complex and cumbersome to implement, even when adopting a unified detection scheme. Current art for automated referral assessment resides on highly abstract data-driven approaches. Usually, those approaches receive an image and spit the response out ¿ that might be resulting from only one model or ensembles ¿ and are not easily explainable. Hence, this work aims at enhancing lesion detection and reinforcing referral decisions with advanced handcrafted two-tiered image representations. We also intended to compose sophisticated data-driven models for referable DR detection and incorporate supervised learning of features with saliency-oriented mid-level image representations to come up with a robust yet accountable automated screening approach. Ultimately, we aimed at integrating our software solutions with simple retinal imaging devices. In the lesion detection task, we proposed advanced handcrafted image characterization approaches to detecting effectively different lesions. Our leading advances are centered on designing a novel coding technique for retinal images and preserving information in the pooling process. Automatically deciding on whether or not the patient should be referred to the ophthalmic specialist is a more difficult, and still hotly debated research aim. We designed a simple and robust method for referral decisions that does not rely upon lesion detection stages. We also proposed a novel and effective data-driven model that significantly improves the performance for DR screening. Our accountable data-driven model produces a reliable (local- and global-) response along with a heatmap/saliency map that enables pixel-based importance comprehension. We explored this methodology to create a local descriptor that is encoded into a rich mid-level representation. Data-driven methods are the state of the art for diabetic retinopathy screening. However, saliency maps are essential not only to interpret the learning in terms of pixel importance but also to reinforce small discriminative characteristics that have the potential to enhance the diagnosticDoutoradoCiência da ComputaçãoDoutor em Ciência da ComputaçãoCAPE

Automatic Screening and Classification of Diabetic Retinopathy Eye Fundus Image

Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage

A NOVEL APPROACH FOR FINDING DIABETIC MELLITUS USING ENSEMBLE MODEL FOR AN OPTIMIZED CLASSIFICATION

  Diabetic mellitus is a chronic disease caused by hyperglycemia which should be treated with high care and medications. The objective of this work is to identify and classify the severity of the diabetic disease using the training data set. This is caused due to the defect in insulin secretion that may affect several organs in the body. Blood pressure and diabetic mellitus are the common twin diseases occurred in about 69.2 million people living in India around 8.7% of the population as per the data resealed in the year 2015. Correct diet, regular exercise will control disease to a great extent. In this research paper the applied methodology is a concurrent classifier for the diabetic mellitus and the results are analyzed with the supervised learning. From the University of California and Irvine repository related attributes for the diabetic mellitus are carefully measured through the ensemble classifier and the results are categorized in the dataset. This work results that boosting can be made to the dataset for obtaining accurate results and classifications. In the conclusion, ensemble methodology is the well proven methodology from the year 1993. For forecasting in N†number of domains, so for the ensemble classifier produces 93% of the accurate results are made. An audit can be made on the results and suggestions are given to the patients for taking medications with the help of medical practitioners

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications

Eye Disease Prediction Among Corporate Employees using Machine Learning Techniques

In the IT sector, employees use systems for more than 6 hs, so they are affected by many health problems. Mostly In the IT sector, employees are affected with eye diseases like eye strain, eye pain, burning sensation, double vision, blurring of vision, and frequent watering. The major goal of this research is to identify the different types of eye problems encountered, the symptoms present, and the population afflicted by eye diseases in order to accurately forecast outcomes using a Machine learning techniques for real-time data sets.Additional Notes: Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved

Automated Retinal Lesion Detection via Image Saliency Analysis

Background and objective:The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion detection method by adapting the concepts of saliency. Methods :Retinal images are firstly segmented as superpixels, two new saliency feature representations: uniqueness and compactness, are then derived to represent the superpixels. The pixel level saliency is then estimated from these superpixel saliency values via a bilateral filter. These extracted saliency features form a matrix for low-rank analysis to achieve saliency detection. The precise contour of a lesion is finally extracted from the generated saliency map after removing confounding structures such as blood vessels, the optic disc, and the fovea. The main novelty of this method is that it is an effective tool for detecting different abnormalities at pixel-level from different modalities of retinal images, without the need to tune parameters. Results:To evaluate its effectiveness, we have applied our method to seven public datasets of diabetic and malarial retinopathy with four different types of lesions: exudate, hemorrhage, microaneurysms, and leakage. The evaluation was undertaken at pixel-level, lesion-level, or image-level according to ground truth availability in these datasets. Conclusions:The experimental results show that the proposed method outperforms existing state-of-the-art ones in applicability, effectiveness, and accuracy

Deep learning analysis of eye fundus images to support medical diagnosis

Machine learning techniques have been successfully applied to support medical decision making of cancer, heart diseases and degenerative diseases of the brain. In particular, deep learning methods have been used for early detection of abnormalities in the eye that could improve the diagnosis of different ocular diseases, especially in developing countries, where there are major limitations to access to specialized medical treatment. However, the early detection of clinical signs such as blood vessel, optic disc alterations, exudates, hemorrhages, drusen, and microaneurysms presents three main challenges: the ocular images can be affected by noise artifact, the features of the clinical signs depend specifically on the acquisition source, and the combination of local signs and grading disease label is not an easy task. This research approaches the problem of combining local signs and global labels of different acquisition sources of medical information as a valuable tool to support medical decision making in ocular diseases. Different models for different eye diseases were developed. Four models were developed using eye fundus images: for DME, it was designed a two-stages model that uses a shallow model to predict an exudate binary mask. Then, the binary mask is stacked with the raw fundus image into a 4-channel array as an input of a deep convolutional neural network for diabetic macular edema diagnosis; for glaucoma, it was developed three deep learning models. First, it was defined a deep learning model based on three-stages that contains an initial stage for automatically segment two binary masks containing optic disc and physiological cup segmentation, followed by an automatic morphometric features extraction stage from previous segmentations, and a final classification stage that supports the glaucoma diagnosis with intermediate medical information. Two late-data-fusion methods that fused morphometric features from cartesian and polar segmentation of the optic disc and physiological cup with features extracted from raw eye fundus images. On the other hand, two models were defined using optical coherence tomography. First, a customized convolutional neural network termed as OCT-NET to extract features from OCT volumes to classify DME, DR-DME and AMD conditions. In addition, this model generates images with highlighted local information about the clinical signs, and it estimates the number of slides inside a volume with local abnormalities. Finally, a 3D-Deep learning model that uses OCT volumes as an input to estimate the retinal thickness map useful to grade AMD. The methods were systematically evaluated using ten free public datasets. The methods were compared and validated against other state-of-the-art algorithms and the results were also qualitatively evaluated by ophthalmology experts from Fundación Oftalmológica Nacional. In addition, the proposed methods were tested as a diagnosis support tool of diabetic macular edema, glaucoma, diabetic retinopathy and age-related macular degeneration using two different ocular imaging representations. Thus, we consider that this research could be potentially a big step in building telemedicine tools that could support medical personnel for detecting ocular diseases using eye fundus images and optical coherence tomography.Las técnicas de aprendizaje automático se han aplicado con éxito para apoyar la toma de decisiones médicas sobre el cáncer, las enfermedades cardíacas y las enfermedades degenerativas del cerebro. En particular, se han utilizado métodos de aprendizaje profundo para la detección temprana de anormalidades en el ojo que podrían mejorar el diagnóstico de diferentes enfermedades oculares, especialmente en países en desarrollo, donde existen grandes limitaciones para acceder a tratamiento médico especializado. Sin embargo, la detección temprana de signos clínicos como vasos sanguíneos, alteraciones del disco óptico, exudados, hemorragias, drusas y microaneurismas presenta tres desafíos principales: las imágenes oculares pueden verse afectadas por artefactos de ruido, las características de los signos clínicos dependen específicamente de fuente de adquisición, y la combinación de signos locales y clasificación de la enfermedad no es una tarea fácil. Esta investigación aborda el problema de combinar signos locales y etiquetas globales de diferentes fuentes de adquisición de información médica como una herramienta valiosa para apoyar la toma de decisiones médicas en enfermedades oculares. Se desarrollaron diferentes modelos para diferentes enfermedades oculares. Se desarrollaron cuatro modelos utilizando imágenes de fondo de ojo: para DME, se diseñó un modelo de dos etapas que utiliza un modelo superficial para predecir una máscara binaria de exudados. Luego, la máscara binaria se apila con la imagen de fondo de ojo original en una matriz de 4 canales como entrada de una red neuronal convolucional profunda para el diagnóstico de edema macular diabético; para el glaucoma, se desarrollaron tres modelos de aprendizaje profundo. Primero, se definió un modelo de aprendizaje profundo basado en tres etapas que contiene una etapa inicial para segmentar automáticamente dos máscaras binarias que contienen disco óptico y segmentación fisiológica de la copa, seguido de una etapa de extracción de características morfométricas automáticas de segmentaciones anteriores y una etapa de clasificación final que respalda el diagnóstico de glaucoma con información médica intermedia. Dos métodos de fusión de datos tardíos que fusionaron características morfométricas de la segmentación cartesiana y polar del disco óptico y la copa fisiológica con características extraídas de imágenes de fondo de ojo crudo. Por otro lado, se definieron dos modelos mediante tomografía de coherencia óptica. Primero, una red neuronal convolucional personalizada denominada OCT-NET para extraer características de los volúmenes OCT para clasificar las condiciones DME, DR-DME y AMD. Además, este modelo genera imágenes con información local resaltada sobre los signos clínicos, y estima el número de diapositivas dentro de un volumen con anomalías locales. Finalmente, un modelo de aprendizaje 3D-Deep que utiliza volúmenes OCT como entrada para estimar el mapa de espesor retiniano útil para calificar AMD. Los métodos se evaluaron sistemáticamente utilizando diez conjuntos de datos públicos gratuitos. Los métodos se compararon y validaron con otros algoritmos de vanguardia y los resultados también fueron evaluados cualitativamente por expertos en oftalmología de la Fundación Oftalmológica Nacional. Además, los métodos propuestos se probaron como una herramienta de diagnóstico de edema macular diabético, glaucoma, retinopatía diabética y degeneración macular relacionada con la edad utilizando dos representaciones de imágenes oculares diferentes. Por lo tanto, consideramos que esta investigación podría ser potencialmente un gran paso en la construcción de herramientas de telemedicina que podrían ayudar al personal médico a detectar enfermedades oculares utilizando imágenes de fondo de ojo y tomografía de coherencia óptica.Doctorad