A novel Automatic Optic Disc and Cup Image Segmentation System for Diagnosing Glaucoma using RIGA dataset

The optic nerve head (ONH) of the retina is a very important landmark of the fundus and is altered in optic nerve pathology especially glaucoma. Numerous imaging systems are available to capture the retinal fundus and from which some structural parameters can be inferred the retinal fundus camera is one of the most important tools used for this purpose. Currently, the ONH structure examination of the fundus images is conducted by the professionals only by observation. It should be noted that there is a shortage of highly trained professional worldwide. Therefore a reliable and efficient optic disc and cup localization and segmentation algorithms are important for automatic eye disease screening and also for monitoring the progression/remission of the disease Thus in order to develop a system, a retinal fundus image dataset is necessary to train and test the new software systems. The methods for diagnosing glaucoma are reviewed in the first chapter. Various datasets of retinal fundus images that are publically available currently are described and discussed. In the second chapter the techniques for the optic disc and cup segmentations available in the literature is reviewed. While in the third chapter a unique retinal fundus image dataset, called RIGA (retinal images for glaucoma analysis) is presented. In the dataset, the optic disc and cup boundaries are annotated manually by 6 ophthalmologists (glaucoma professionals) independently for total of 4500 images in order to obtain a comprehensive view point as well as to see the variation and agreement between these professionals. Based upon these evaluations, some of the images were filtered based on a statistical analysis in order to increase the reliability. The new optic disc and cup segmentation methodologies are discussed in the fourth chapter. The process starts with a preprocessing step based on a reliable and precise algorithm. Here an Interval Type-II fuzzy entropy based thresholding scheme along with Differential Evolution was applied to determine the location of the optic disc in order to determine the region of interest instead of dealing with the entire image. Then, the processing step is discussed. Two algorithms were applied: one for optic disc segmentation based on an active contour model implemented by level set approach, and the second for optic cup segmentation. For this thresholding was applied to localize the disc. The disc and cup area and centroid are then calculated in order to evaluate them based on the manual annotations of areas and centroid for the filtered images based on the statistical analysis. In the fifth chapter, after segmenting the disc and cup, the clinical parameters in diagnosis of glaucoma such as horizontal and vertical cup to disc ratio (HCDR) and (VCDR) are computed automatically as a post processing step in order to compare the results with the six ophthalmologist’s manual annotations results. The thesis is concluded in chapter six with discussion of future plans

TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation

Medical images from different clinics are acquired with different instruments and settings. To perform segmentation on these images as a cloud-based service we need to train with multiple datasets to increase the segmentation independency from the source. We also require an ef cient and fast segmentation network. In this work these two problems, which are essential for many practical medical imaging applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep neural networks which have been shown to be effective for medical image segmentation. Many different U-Net implementations have been proposed.With the recent development of tensor processing units (TPU), the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud services. In this paper, we study, using Google's publicly available colab environment, a generalized fully con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction. As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to glaucoma detection. To obtain networks with a good performance, independently of the image acquisition source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result of this study, we have developed a set of functions that allow the implementation of generalized U-Nets adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-

Automated fundus image quality assessment and segmentation of optic disc using convolutional neural networks

An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIM-ONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics

A Comparison of Deep Learning Techniques for Glaucoma Diagnosis on Retinal Fundus Images

Glaucoma is one of the serious disorders which cause permanent vision loss if it left undetected. The primary cause of the disease is elevated intraocular pressure, impacting the optic nerve head (ONH) that originates from the optic disc. The variation in optic disc to optic cup ratio helps in early detection of the disease. Manual calculation of Cup to Disc Ratio (CDR) consumes more time and the prediction is also not accurate. Utilizing deep learning for the automatic detection of glaucoma facilitates precise and early identification, significantly enhancing the accuracy of glaucoma detection. The deep learning technique initiates the process by initially pre-processing the image to achieve data augmentation, followed by the segmentation of the optic disc and optic cup from the retinal fundus image. From the segmented Optic Disc (OD)and Optic Cup (OC) feature are selected and CDR calculated. Based on the CDR value the Glaucoma classification is performed. Various deep learning techniques like CNN, transfer learning, algorithm was proposed in early detection of glaucoma. From the comparative analysis glaucoma diagnosis, the proposed deep learning artifact Convolutional Neural Network outperform in early diagnosis of glaucoma providing accuracy of 99.3 8%

Automated retinal analysis

Diabetes is a chronic disease affecting over 2% of the population in the UK [1]. Long-term complications of diabetes can affect many different systems of the body including the retina of the eye. In the retina, diabetes can lead to a disease called diabetic retinopathy, one of the leading causes of blindness in the working population of industrialised countries. The risk of visual loss from diabetic retinopathy can be reduced if treatment is given at the onset of sight-threatening retinopathy. To detect early indicators of the disease, the UK National Screening Committee have recommended that diabetic patients should receive annual screening by digital colour fundal photography [2]. Manually grading retinal images is a subjective and costly process requiring highly skilled staff. This thesis describes an automated diagnostic system based oil image processing and neural network techniques, which analyses digital fundus images so that early signs of sight threatening retinopathy can be identified. Within retinal analysis this research has concentrated on the development of four algorithms: optic nerve head segmentation, lesion segmentation, image quality assessment and vessel width measurements. This research amalgamated these four algorithms with two existing techniques to form an integrated diagnostic system. The diagnostic system when used as a 'pre-filtering' tool successfully reduced the number of images requiring human grading by 74.3%: this was achieved by identifying and excluding images without sight threatening maculopathy from manual screening

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