Data mining for AMD screening: A classification based approach

This paper investigates the use of three alternative approaches to classifying retinal images. The novelty of these approaches is that they are not founded on individual lesion segmentation for feature generation, instead use encodings focused on the entire image. Three different mechanisms for encoding retinal image data were considered: (i) time series, (ii) tabular and (iii) tree based representations. For the evaluation two publically available, retinal fundus image data sets were used. The evaluation was conducted in the context of Age-related Macular Degeneration (AMD) screening and according to statistical significance tests. Excellent results were produced: Sensitivity, specificity and accuracy rates of 99% and over were recorded, while the tree based approach has the best performance with a sensitivity of 99.5%. Further evaluation indicated that the results were statistically significant. The excellent results indicated that these classification systems are ideally suited to large scale AMD screening processes

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

Compass Fundus-Guided Perimetry in Geographic Atrophy

To evaluate compass (CMP), a recently introduced device that combines scanning ophthalmoscopy, automated perimetry, and eye tracking, for fundus-guided perimetry (microperimetry) with the purpose of correlating perimetric retinal sensitivity (PRS) and retinal geographic atrophy (GA) features

Multimodal imaging in age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness and affects approximately one in seven Australians aged 50 years and above. Currently, this complex condition is not easily and uniformly assessed. The signs of AMD differ between eyes and also occur in other macular disorders. This hinders accurate diagnosis and classification, which is fundamental to optimal patient care. Ocular imaging and visual function assessment have the potential to minimise the devastating consequences of disease through early detection. However, multiple devices are now commercially available and the impact of these technologies in clinical practice may not be straightforward. For instance, their usefulness may depend on accessibility and the operator’s knowledge and clinical skills. The impact on patient management, as well as alternative models of eye-care delivery, requires clarification. This thesis aims to explore the current and potential utility of imaging technologies (optical coherence tomography, infrared imaging, monochromatic retinal photography and fundus autofluorescence) in the assessment and management of AMD and other diseases of retinal pigment epithelium dysfunction. The findings show that optometrists self-describe high levels of practice competency and make ready use of imaging in everyday practice. However, they also unwittingly demonstrated low awareness of the evidence base in AMD. Furthermore, when their interpretation of images was tested using a series of case vignettes, their diagnostic accuracy as a group improved by only five per cent (from 61 per cent to 66 per cent); their tendency to refer increased by four per cent. These factors might be improved through education. A series of open-access, chair-side reference charts were consequently devised to help optometrists use imaging technologies more effectively in clinical practice. The additive contribution of multimodal structural and functional testing was particularly emphasised. Finally, a novel model of intermediate-tier eye-care in Australia was shown to substantially reduce the number of false positive cases or cases without a specific diagnosis. Interestingly, this model was acclaimed by reviewers as “scoring highly for originality and of international relevance”. Most excitingly, the thesis concludes with future directions regarding collaborative care and multimodal imaging, where detection of disease might be facilitated via a computational approach

DIAGNOSE EYES DISEASES USING VARIOUS FEATURES EXTRACTION APPROACHES AND MACHINE LEARNING ALGORITHMS

Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm)

Metadata-enhanced contrastive learning from retinal optical coherence tomography images

Supervised deep learning algorithms hold great potential to automate screening, monitoring and grading of medical images. However, training performant models has typically required vast quantities of labelled data, which is scarcely available in the medical domain. Self-supervised contrastive frameworks relax this dependency by first learning from unlabelled images. In this work we show that pretraining with two contrastive methods, SimCLR and BYOL, improves the utility of deep learning with regard to the clinical assessment of age-related macular degeneration (AMD). In experiments using two large clinical datasets containing 170,427 optical coherence tomography (OCT) images of 7,912 patients, we evaluate benefits attributed to pretraining across seven downstream tasks ranging from AMD stage and type classification to prediction of functional endpoints to segmentation of retinal layers, finding performance significantly increased in six out of seven tasks with fewer labels. However, standard contrastive frameworks have two known weaknesses that are detrimental to pretraining in the medical domain. Several of the image transformations used to create positive contrastive pairs are not applicable to greyscale medical scans. Furthermore, medical images often depict the same anatomical region and disease severity, resulting in numerous misleading negative pairs. To address these issues we develop a novel metadata-enhanced approach that exploits the rich set of inherently available patient information. To this end we employ records for patient identity, eye position (i.e. left or right) and time series data to indicate the typically unknowable set of inter-image contrastive relationships. By leveraging this often neglected information our metadata-enhanced contrastive pretraining leads to further benefits and outperforms conventional contrastive methods in five out of seven downstream tasks

Evaluation of the potentials for optical coherence tomography (OCT) to detect early signs of retinal neurodegeneration

Among neuroretinal degenerations, glaucoma and age-related macular degeneration (AMD) have become the most frequent reasons for irreversible blindness globally. Among the causes of the elderly and senile dementia, Alzheimer’s disease (AD) has the leading position, the early ocular symptoms of which can potentially be a prognostic factor. The aim of this thesis was the early in vivo ligand-free detection of degenerative changes in the inner and outer retinal layers, which was possible using high-resolution optical coherence tomography (OCT) with the machine learning (ML) algorithms: support vector machine (SVM) and principal component analysis (PCA). Prior to the application of SVM and PCA for the classification of human OCT images, evaluation of the classifiers was performed in the classification of optical phantoms, the accuracy of which was in the range of 82-100%. This was the first attempt to measure the textural properties of various polystyrene and silica beads optical phantoms. To identify optical changes that characterise early apoptosis, OCT imaging of axotomised retinal ganglion cells (RGCs) in ex vivo retinal murine explants was performed. Substantial optical alterations in RGC dendrites in the early stages of apoptosis (up to 2 hours) were detected. ML algorithms correctly classified the retinal texture of the inner plexiform layer (IPL) of transgenic AD mice in all cases, indicating the potential for further investigation in in vivo animal and human studies. Not only the optical signature but also the transparency of the dissected murine retinal explants was investigated. Moreover, ML classification of 3xTg mice IPL layer was studied in terms of optical changes due to the RGD dendritic atrophy. ML classifiers’ accuracy in the detection of early and neovascular AMD was 93-100% for the texture of retinal pigment epithelium, 69-67% for the outer nuclear layer, 70% for the inner segment and 60-90% for the outer segment of photoreceptors. Classification of AMD stages and comparison with the age-matched healthy controls was carried out in the outer retina and RPE. Grey-level co-occurrence, run-length matrices, local binary patterns features were extracted from the IPL of the macula to classify glaucoma OCT images. The accuracy of linear and non-linear SVMs, linear and quadratic discriminant analyses, decision tree and logistic regression was between 55-70%. Based on the classifiers’ precision, recall and F1-score, Gaussian SVM outperformed other ML techniques. In this study, the observation of early glaucomatous subtle optical changes of human IPL was conducted. Also, the significance of various supervised ML algorithms was investigated. Understanding the optical signature of cumulative inherent speckle of OCT scans arising from apoptotic retinal ganglion cells and photoreceptors may provide vital information for the prevention of retinal neurodegeneration