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

Computational models for stuctural analysis of retinal images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd

Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease’s development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers

Machine Learning for Glaucoma Assessment using Fundus Images

[ES] Las imágenes de fondo de ojo son muy utilizadas por los oftalmólogos para la evaluación de la retina y la detección de glaucoma. Esta patología es la segunda causa de ceguera en el mundo, según estudios de la Organización Mundial de la Salud (OMS). En esta tesis doctoral, se estudian algoritmos de aprendizaje automático (machine learning) para la evaluación automática del glaucoma usando imágenes de fondo de ojo. En primer lugar, se proponen dos métodos para la segmentación automática. El primer método utiliza la transformación Watershed Estocástica para segmentar la copa óptica y posteriormente medir características clínicas como la relación Copa/Disco y la regla ISNT. El segundo método es una arquitectura U-Net que se usa específicamente para la segmentación del disco óptico y la copa óptica. A continuación, se presentan sistemas automáticos de evaluación del glaucoma basados en redes neuronales convolucionales (CNN por sus siglas en inglés). En este enfoque se utilizan diferentes modelos entrenados en ImageNet como clasificadores automáticos de glaucoma, usando fine-tuning. Esta nueva técnica permite detectar el glaucoma sin segmentación previa o extracción de características. Además, este enfoque presenta una mejora considerable del rendimiento comparado con otros trabajos del estado del arte. En tercer lugar, dada la dificultad de obtener grandes cantidades de imágenes etiquetadas (glaucoma/no glaucoma), esta tesis también aborda el problema de la síntesis de imágenes de la retina. En concreto se analizaron dos arquitecturas diferentes para la síntesis de imágenes, las arquitecturas Variational Autoencoder (VAE) y la Generative Adversarial Networks (GAN). Con estas arquitecturas se generaron imágenes sintéticas que se analizaron cualitativa y cuantitativamente, obteniendo un rendimiento similar a otros trabajos en la literatura. Finalmente, en esta tesis se plantea la utilización de un tipo de GAN (DCGAN) como alternativa a los sistemas automáticos de evaluación del glaucoma presentados anteriormente. Para alcanzar este objetivo se implementó un algoritmo de aprendizaje semi-supervisado.[CA] Les imatges de fons d'ull són molt utilitzades pels oftalmòlegs per a l'avaluació de la retina i la detecció de glaucoma. Aquesta patologia és la segona causa de ceguesa al món, segons estudis de l'Organització Mundial de la Salut (OMS). En aquesta tesi doctoral, s'estudien algoritmes d'aprenentatge automàtic (machine learning) per a l'avaluació automàtica del glaucoma usant imatges de fons d'ull. En primer lloc, es proposen dos mètodes per a la segmentació automàtica. El primer mètode utilitza la transformació Watershed Estocàstica per segmentar la copa òptica i després mesurar característiques clíniques com la relació Copa / Disc i la regla ISNT. El segon mètode és una arquitectura U-Net que s'usa específicament per a la segmentació del disc òptic i la copa òptica. A continuació, es presenten sistemes automàtics d'avaluació del glaucoma basats en xarxes neuronals convolucionals (CNN per les sigles en anglès). En aquest enfocament s'utilitzen diferents models entrenats en ImageNet com classificadors automàtics de glaucoma, usant fine-tuning. Aquesta nova tècnica permet detectar el glaucoma sense segmentació prèvia o extracció de característiques. A més, aquest enfocament presenta una millora considerable del rendiment comparat amb altres treballs de l'estat de l'art. En tercer lloc, donada la dificultat d'obtenir grans quantitats d'imatges etiquetades (glaucoma / no glaucoma), aquesta tesi també aborda el problema de la síntesi d'imatges de la retina. En concret es van analitzar dues arquitectures diferents per a la síntesi d'imatges, les arquitectures Variational Autoencoder (VAE) i la Generative adversarial Networks (GAN). Amb aquestes arquitectures es van generar imatges sintètiques que es van analitzar qualitativament i quantitativament, obtenint un rendiment similar a altres treballs a la literatura. Finalment, en aquesta tesi es planteja la utilització d'un tipus de GAN (DCGAN) com a alternativa als sistemes automàtics d'avaluació del glaucoma presentats anteriorment. Per assolir aquest objectiu es va implementar un algoritme d'aprenentatge semi-supervisat.[EN] Fundus images are widely used by ophthalmologists to assess the retina and detect glaucoma, which is, according to studies from the World Health Organization (WHO), the second cause of blindness worldwide. In this thesis, machine learning algorithms for automatic glaucoma assessment using fundus images are studied. First, two methods for automatic segmentation are proposed. The first method uses the Stochastic Watershed transformation to segment the optic cup and measures clinical features such as the Cup/Disc ratio and ISNT rule. The second method is a U-Net architecture focused on the optic disc and optic cup segmentation task. Secondly, automated glaucoma assessment systems using convolutional neural networks (CNNs) are presented. In this approach, different ImageNet-trained models are fine-tuned and used as automatic glaucoma classifiers. These new techniques allow detecting glaucoma without previous segmentation or feature extraction. Moreover, it improves the performance of other state-of-art works. Thirdly, given the difficulty of getting large amounts of glaucoma-labelled images, this thesis addresses the problem of retinal image synthesis. Two different architectures for image synthesis, the Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) architectures, were analysed. Using these models, synthetic images that were qualitative and quantitative analysed, reporting state-of-the-art performance, were generated. Finally, an adversarial model is used to create an alternative automatic glaucoma assessment system. In this part, a semi-supervised learning algorithm was implemented to reach this goal.The research derived from this doctoral thesis has been supported by the Generalitat Valenciana under the scholarship Santiago Grisolía [GRISOLIA/2015/027].Díaz Pinto, AY. (2019). Machine Learning for Glaucoma Assessment using Fundus Images [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/124351TESI

Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions

Qhaway: una herramienta de apoyo para el diagnóstico del glaucoma con aprendizaje profundo

Propone un método para el diagnóstico del glaucoma basado en un modelo híbrido de modelos DL, con el cual usando imágenes del fondo de ojo de un paciente se consigue hacer el diagnóstico con alta precisión. Se consideró la integración de los dataset públicos de glaucoma HRF, Drishti-GS1, sjchoi86-HRF, RIM-ONE y ACRIMA, con un total de 1707 imágenes (919 normal y 788 glaucoma) del fondo de ojo, un modelo híbrido de Voting sobre los modelos de DL ResNet50 con dos tipos de fine tuning y ResNet50V2, y la implementación usando Keras y Tensor Flow, con lo que se consiguió un diagnóstico con exactitud del 96.55%, sensibilidad del 98.54% y especificidad del 94.32%. Además, los experimentos numéricos muestran que el aprendizaje usando 5 bases de datos permite mejores resultados que por separado, incluso aplicando transfer learning, también muestran que el modelo híbrido voting genera una exactitud superior en 20.69% a la mejor exactitud obtenido por el mejor modelo de DL (DenseNet169) usando un dataset, 13.22% al mejor modelo (ResNet50V2) usando transfer learning con los 5 datasets, y 1.72% al mejor modelo (ResNet50) considerando los 5 dataset

Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis

Background: Glaucoma is a major public health problem that can lead to an optic nerve lesion, requiring systematic screening in the population over 45 years of age. The diagnosis and classification of this disease have had a marked and excellent development in recent years, particularly in the machine learning domain. Multimodal data have been shown to be a significant aid to the machine learning domain, especially by its contribution to improving data driven decision-making. Method: Solving classification problems by combinations of classifiers has made it possible to increase the robustness as well as the classification reliability by using the complementarity that may exist between the classifiers. Complementarity is considered a key property of multimodality. A Convolutional Neural Network (CNN) works very well in pattern recognition and has been shown to exhibit superior performance, especially for image classification which can learn by themselves useful features from raw data. This article proposes a multimodal classification approach based on deep Convolutional Neural Network and Support Vector Machine (SVM) classifiers using multimodal data and multimodal feature for glaucoma diagnosis from retinal fundus images from RIM-ONE dataset. We make use of handcrafted feature descriptors such as the Gray Level Co-Occurrence Matrix, Central Moments and Hu Moments to co-operate with features automatically generated by the CNN in order to properly detect the optic nerve and consequently obtain a better classification rate, allowing a more reliable diagnosis of glaucoma. Results: The experimental results confirm that the combination of classifiers using the BWWV technique is better than learning classifiers separately. The proposed method provides a computerized diagnosis system for glaucoma disease with impressive results comparing them to the main related studies that allow us to continue in this research path