Eye Disease Detection Using Computer Vision

Glaucoma and Diabetic Retinopathy(DR) are among the leading causes of blindness. Belated handling of Cataract can impact the vision causing blindness. Often the scarcity of experts can lead to delayed diagnosis, resulting in untreatable conditions. But detection of these diseases at earliest stage and treatment can aid patient in avoiding vision loss. An automatic disease detection system can help this by providing accurate and early diagnosis. In proposed system, diagnosis will be obtained using image processing and mining techniques on fundus image. Feature extraction using DCT. K-NN classification algorithm will be used to classify the image in a specific class (Normal,Glaucoma,DR or Cataract)

Automatic extraction of retinal features to assist diagnosis of glaucoma disease

Glaucoma is a group of eye diseases that have common traits such as high eye pressure, damage to the Optic Nerve Head (ONH) and gradual vision loss. It affects the peripheral vision and eventually leads to blindness if left untreated. The current common methods of diagnosis of glaucoma are performed manually by the clinicians. Clinicians perform manual image operations such as change of contrast, zooming in zooming out etc to observe glaucoma related clinical indications. This type of diagnostic process is time consuming and subjective. With the advancement of image and vision computing, by automating steps in the diagnostic process, more patients can be screened and early treatment can be provided to prevent any or further loss of vision. The aim of this work is to develop a system called Glaucoma Detection Framework (GDF), which can automatically determine changes in retinal structures and imagebased pattern associated with glaucoma so as to assist the eye clinicians for glaucoma diagnosis in a timely and effective manner. In this work, several major contributions have been made towards the development of the automatic GDF consisting of the stages of preprocessing, optic disc and cup segmentation and regional image feature methods for classification between glaucoma and normal images. Firstly, in the preprocessing step, a retinal area detector based on superpixel classification model has been developed in order to automatically determine true retinal area from a Scanning Laser Ophthalmoscope (SLO) image. The retinal area detector can automatically extract artefacts out from the SLO image while preserving the computational effciency and avoiding over-segmentation of the artefacts. Localization of the ONH is one of the important steps towards the glaucoma analysis. A new weighted feature map approach has been proposed, which can enhance the region of ONH for accurate localization. For determining vasculature shift, which is one of glaucoma indications, we proposed the ONH cropped image based vasculature classification model to segment out the vasculature from the ONH cropped image. The ONH cropped image based vasculature classification model is developed in order to avoid misidentification of optic disc boundary and Peripapillary Atrophy (PPA) around the ONH of being a part of the vasculature area. Secondly, for automatic determination of optic disc and optic cup boundaries, a Point Edge Model (PEM), a Weighted Point Edge Model (WPEM) and a Region Classification Model (RCM) have been proposed. The RCM initially determines the optic disc region using the set of feature maps most suitable for the region classification whereas the PEM updates the contour using the force field of the feature maps with strong edge profile. The combination of PEM and RCM entitled Point Edge and Region Classification Model (PERCM) has significantly increased the accuracy of optic disc segmentation with respect to clinical annotations around optic disc. On the other hand, the WPEM determines the force field using the weighted feature maps calculated by the RCM for optic cup in order to enhance the optic cup region compared to rim area in the ONH. The combination of WPEM and RCM entitled Weighted Point Edge and Region Classification Model (WPERCM) can significantly enhance the accuracy of optic cup segmentation. Thirdly, this work proposes a Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from the existing methods focusing on global features information only, our approach after optic disc localization and segmentation can automatically divide an image into five regions (i.e. optic disc or Optic Nerve Head (ONH) area, inferior (I), superior(S), nasal(N) and temporal(T)). These regions are usually used for diagnosis of glaucoma by clinicians through visual observation only. It then extracts image-based information such as textural, spatial and frequency based information so as to distinguish between normal and glaucoma images. The method provides a new way to identify glaucoma symptoms without determining any geometrical measurement associated with clinical indications glaucoma. Finally, we have accommodated clinical indications of glaucoma including the CDR, vasculature shift and neuroretinal rim loss with the RIFM classification and performed automatic classification between normal and glaucoma images. Since based on the clinical literature, no geometrical measurement is the guaranteed sign of glaucoma, the accommodation of the RIFM classification results with clinical indications of glaucoma can lead to more accurate classification between normal and glaucoma images. The proposed methods in this work have been tested against retinal image databases of 208 fundus images and 102 Scanning Laser Ophthalmoscope (SLO) images. These databases have been annotated by the clinicians around different anatomical structures associated with glaucoma as well as annotated with healthy or glaucomatous images. In fundus images, ONH cropped images have resolution varying from 300 to 900 whereas in SLO images, the resolution is 341 x 341. The accuracy of classification between normal and glaucoma images on fundus images and the SLO images is 94.93% and 98.03% respectively

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

An in vivo investigation of choroidal vasculature in age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of visual impairment in the developed world. Whilst the pathogenesis is complex and not fully understood, changes to the choroidal vasculature in AMD have been demonstrated using histology. Advances in imaging technology, particularly long-wavelength optical coherence tomography (OCT), allow in vivo visualisation and investigation of this structure. The aim of this work is to determine whether changes to the choroidal vasculature are detectable in AMD using in vivo imaging. This was achieved through the evaluation of parameters for quantifying the structure, and the application of a machine learning approach to automated disease severity classification, based on choroidal appearance. Participants with early AMD (n=25), neovascular AMD (nAMD; n=25), and healthy controls (n=25) underwent imaging with a non-commercial long-wavelength (λc=1040 nm) OCT device. Subfoveal choroidal thickness, choroidal area, and luminal area were significantly lower in the nAMD group than the healthy and early AMD groups, whilst vessel ratio was significantly greater (P<0.05 in all cases). There was no significant difference in visible vessel diameter, choroidal vascularity index, luminal area ratio, or luminal perimeter ratio between the groups. No significant differences were found between the healthy and early AMD groups for any of the eight vascular parameters assessed. Classification of the disease groups based on choroidal OCT images was demonstrated using machine learning techniques. Textural features within the images were extracted using Gabor filters, and K-nearest neighbour, support vector machine, and random forest classifiers were assessed for this classification task. Textural changes were most pronounced in late-stage disease, although attribution to pathology or pharmacological intervention (anti-VEGF treatment) was not possible. Changes were also discernible in the early AMD group, suggesting sensitivity of this approach to detecting vascular involvement in early disease. In conclusion, structural changes to the choroidal vasculature in AMD are detectable in vivo using OCT imaging, demonstrated with both manual and automated analysis techniques. Whilst changes were most prominent in late-stage disease, subtle structural changes in early AMD were identified with texture analysis, warranting further investigation to improve our understanding of choroidal involvement in the pathogenesis of early AMD

Low-level night-time light therapy for age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of visual impairment in the developed world (Wong et al., 2014). The exact causes of AMD are unclear but hypoxia has been implicated (Stefánsson et al., 2011). If hypoxia has a role in the pathogenesis of AMD treatments that mitigate the effect of retinal hypoxia may slow disease progression. This thesis aimed to establish the impact of light therapy, as delivered using a light emitting mask, on the progression of AMD. A phase I/IIa randomised controlled trial was implemented in which 60 participants with early and intermediate AMD were allocated to the intervention or the untreated control group in a 1:1 ratio and monitored over 12 months. The ability of secondary outcome measures (including: rate of cone dark adaptation, 14Hz flicker threshold and chromatic thresholds) to identify the likely risk of progression from early and intermediate AMD to advanced AMD was also assessed in a cross-sectional study evaluating the relationship between each baseline outcome measure and the severity of fundus changes. Sixty participants were recruited of which 47 (20 intervention, 27 control) completed the 12 month follow-up period. No significant difference was found in the change of any parameter between groups apart from the time constant of cone-photoreceptor recovery (cone τ), which was increased to a greater extent in the treated group. An additional 40 participants were recruited to the cross-sectional study (n=100). Measurement of cone τ was identified as the best independent predictor of increased AMD severity based on the AREDS Simplified Severity Scale (Ferris et al., 2005). Although a greater proportion of controls (48%) than mask wearers (38%) showed disease progression over the duration of the trial this difference did not reach statistical significance