Automated Glaucoma Detection Using Hybrid Feature Extraction in Retinal Fundus Images

Glaucoma is one of the most common causes of blindness. Robust mass screening may help to extend the symptom-free life for affected patients. To realize mass screening requires a cost-effective glaucoma detection method which integrates well with digital medical and administrative processes. To address these requirements, we propose a novel low cost automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The paper discusses a system for the automated identification of normal and glaucoma classes using higher order spectra (HOS), trace transform (TT), and discrete wavelet transform (DWT) features. The extracted features are fed to a support vector machine (SVM) classifier with linear, polynomial order 1, 2, 3 and radial basis function (RBF) in order to select the best kernel for automated decision making. In this work, the SVM classifier, with a polynomial order 2 kernel function, was able to identify glaucoma and normal images with an accuracy of 91.67%, and sensitivity and specificity of 90% and 93.33%, respectively. Furthermore, we propose a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT, and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd

ABCA4-Associated Retinal Degenerations Spare Structure and Function of the Human Parapapillary Retina

PURPOSE. To study the parapapillary retinal region in patients with ABCA4-associated retinal degenerations. METHODS. Patients with Stargardt disease or cone-rod dystrophy and disease-causing variants in the ABCA4 gene were included. Fixation location was determined under fundus visualization, and central cone-mediated vision was measured. Intensity and texture abnormalities of autofluorescence (AF) images were quantified. Parapapillary retina of an eye donor with ungenotyped Stargardt disease was examined microscopically. RESULTS. AF images ranged from normal, to spatially homogenous abnormal increase of intensity, to a spatially heterogenous speckled pattern, to variably sized patches of low intensity. A parapapillary ring of normal-appearing AF was visible at all disease stages. Quantitative analysis of the intensity and texture properties of AF images showed the preserved region to be an annulus, at least 0.6 mm wide, surrounding the optic nerve head. A similar region of relatively preserved photoreceptor nuclei was apparent in the donor retina. In patients with foveal fixation, there was better cone sensitivity at a parapapillary locus in the nasal retina than at the same eccentricity in the temporal retina. In patients with eccentric fixation, ϳ30% had a preferred retinal locus in the parapapillary retina. CONCLUSIONS. Human retinal degenerations caused by ABCA4 mutations spare the structure of retina and RPE in a circular parapapillary region that commonly serves as the preferred fixation locus when central vision is lost. The retina between fovea and optic nerve head could serve as a convenient, accessible, and informative region for structural and functional studies to determine natural history or outcome of therapy in ABCA4-associated disease. (Invest Ophthalmol Vis Sci. 2005; 46:4739 -4746

Analysis of retinal nerve fiber layer for diagnosis of glaucoma

Diplomová práce je zaměřena na vytvoření metodiky kvantifikace vrstvy nervových vláken na fotografiích sítnice. V úvodní části textu je stručně nastíněna medicínská motivace práce včetně zmínky o některých studiích věnujících se dané problematice. Dále text popisuje uvažované texturní příznaky včetně jejich porovnání dle schopnosti kvantifikovat tloušťku vrstvy nervových vláken. Na základě popsaných poznatků byla navržena metodika využití regresních modelů za účelem predikce tloušťky nervových vláken, která byla dále testována na dostupných obrazových datech. Výsledky ukazují, že výstupy regresních modelů dosahují vysoké korelace mezi výstupem predikce a tloušťkou vrstvy nervových vláken měřenou optickou koherentní tomografií. Závěr práce diskutuje využitelnost aplikovaného řešení.The master thesis is focused on creating a methodology for quantification of the nerve fiber layer on photographs of the retina. The introductory part of the text presents a medical motivation of the thesis and mentions several studies dealing with this issue. Furthermore, the work describes available textural features and compares their ability to quantify the thickness of the nerve fiber layer. Based on the described knowledge, the methodology to make different regression models enabling prediction of the retinal nerve fiber layer thickness was developed. Then, the methodology was tested on the available image dataset. The results showed, that the outputs of regression models achieve a high correlation between the predicted output and the retinal nerve fiber layer thickness measured by optical coherence tomography. The conclusion discusses an usability of the applied solution.

Aspects of structural and functional assessment in open angle glaucoma

Early detection of glaucoma is a prerequisite for effective management of the disease. The study was concerned with aspects of structural and functional assessment in open angle glaucoma. The major part of the study was concerned with the utilization of digital stereoscopic imaging of the optic nerve head in the detection of open angle glaucoma (OAG). Specifically, it addressed possible sources of variability that confound the diagnosis of glaucoma and are associated with the monoscopic, as opposed to stereoscopic, observation of the optic nerve head (ONH) the limited diagnostic value of the features of the peripapillary retina accompanying glaucomatous damage and the between-observer variation in the subjective evaluation of the ONH. The study utilised a dataset of magnification corrected digital images from 51 normal individuals and from 113 patients with OAG. Misdiagnosis of glaucoma was associated with discrepancies in the evaluation of the rim area due to the monoscopic presentation of the ONH masking the presence of focal rim loss, otherwise evident with stereoscopic observation. The frequency and patterns of distribution of the alpha and beta peripapillary atrophy (PPA) were confirmed among normal and glaucomatous eyes but meaningful conclusions on the diagnostic value of PPA were hindered by the clinically broad criteria of this feature. Regression analysis of the global and sectorial rim areas for the discrimination of glaucomatous damage compared favourably with the subjective glaucoma diagnosis by expert observers. The remaining part of the study was concerned with the evaluation of the Total and Pattern Deviation probability analysis in short-wavelength perimetry (SWAP). The material comprised the Humphrey Field Analyzer single field print-outs from standard automated perimetry (SAP) and from SWAP of 53 normal individuals 18 patients with cataract, 22 with OHT and 55 with OAG. Focal visual field loss derived by SWAP was markedly less compared to SWAP indicating wider limits of normality for SWAP. Considerable caution should be exercised before the use of SWAP

Retinal imaging tool for assessment of the parapapillary atrophy and the optic disc

Ophthalmic diseases such as glaucoma are associated with progressive changes in the structure of the optic disc (OD) and parapapillary atrophy (PPA). These structural changes may therefore have relevance to other systemic diseases. The size and location of OD and PPA can be used as registration landmarks for monitoring changes in features of the fundus of the eye. Retinal vessel evaluation, for example, can be used as a biomarker for the effects of multiple systemic diseases, or co-morbidities. This thesis presents the first computer-aided measuring tool that detects and quantifies the progression of PPA automatically on a 2D retinal fundus image in the presence of image noise. An automated segmentation system is described that can detect features of the optic nerve. Three novel approaches are explored that extract the PPA and OD region approximately from a 2D fundus image. The OD region is segmented using (i) a combination of active contour and morphological operations, (ii) a modified Chan-Vese algorithm and (iii) a combination of edge detection and ellipse fitting methods. The PPA region is identified from the presence of bright pixels in the temporal zone of the OD, and segmented using a sequence of techniques, including a modified Chan-Vese approach, thresholding, scanning filter and multi-seed region growing methods. The work demonstrates for the first time how the OD and PPA regions can be identified and quantified from 2D fundus images using a standard fundus camera

Funduse sinine ja lähi-infrapuna autofluorestsentsuuring autosoom-retsessiivse Stardgardti tõve, koroidereemia, PROM1-maakuli düstroofia ja okulaarse albinismi patsientidel

Väitekirja elektrooniline versioon ei sisalda publikatsiooneFunduse sinine ja lähi-infrapuna autofluorestsentsuuring autosoom-retsessiivse Stardgardti tõve, koroidereemia, PROM1-maakuli düstroofia ja okulaarse albinismi patsientidel Pärilikud võrkkestahaigused on juhtivaks nägemiskaotuse põhjuseks tööealise elanikkonna seas arenenud riikides. Tegemist on kliiniliselt ja geneetiliselt väga heterogeense haiguste grupiga, mistõttu diagnostika ja haiguse patogeneesi uurimine on olnud vaevarikas. Võrkkesta piltdiagnostika on oluline mitte-invasiivne meetod haiguste diagnoosimiseks ja uurimiseks. Konfokaalne skanneeriv laseroftalmoskoop valgustab võrkkesta erineva lainepikkusega laserkiirega ning salvestab tagasikiirgavat valgust luues silmapõhjast pildi. Funduse autofluorestsents (AF) uuringul kasutatakse ära silmapõhja enda naturaalseid fluorofoore. Lipofustsiini ergastamiseks kasutatakse sinise spektri laserkiirt (sinine AF) ja melaniini jaoks lähipuna laserkiirt (lähipuna AF). Nende fluorofooride jaotus ja kogus silmapõhjas muutub erinevate haigusprotsesside mõjul ning need muutused on tuvastatavad AF uuringul. Antud doktoritöös uurisime sinise ja lähipuna AF uuringu pilte autosoom-retsesiivse Stargardti tõve (STGD1), koroidereemia, PROM1-maakuli düstroofia ning okulaarse albinismi patsientidel. Töö eesmärgiks oli paremini mõista sinise ja lähipuna AF signaali allikaid erinevate haigusseisundite korral, kus võrkkesta fluorofooride jaotus ning kogused on muutunud. Lisaks kvalitatiivsele piltide hindamisele kasutamise kvantitatiivset AF signaali tugevuse mõõtmist hindamaks lipofustsiini ja melaniini taset. Uurimustöös näitasime, et melaniin on lähipuna AF signaali peamiseks allikaks. Lisaks näitasime, et melanin võib kaudselt moduleerida lipofustsiinist tuleneva sinise AF signaali, sest okulaarse albinismi kandjate hüpopigmenteeritud võrkkesta alade sinise AF signal oli tavapärasest kõrgem. AF signaali tugevuse mõõtmisel leidsime, et lipofustsiini kuhjumine võrkkestas põhjustab lisaks sinise AF signaali tõusule ka lähipuna AF signaali tõusu STGD1 patsientidel. Kvantitatiivsel analüüsil näitasime ka, et PROM1-maakuli düstroofia patsientide sinise AF signaal oli võrreldav terve silmapõhja signaali tugevusega, eristades seda fenotüübiliselt sarnasest STGD1 haigusest ning viidates ka sellele, et lipofustsiini üleliigne kuhjumine ei ole antud haigusele omane mehhanism. Koroidereemia ja STGD1 haigete uurimisel leidsime, et pigmentepiteeli rakkude kärbumine on nähtav AF signaali hääbumisena, samas lähipuna AF uuringaitab tuvastada varasemaid muutusi kui sinine AF uuring. Lipofustsiin ja melanin on mõlemad olulised võrkkesta rakkude seisundi biomarkerid, mida on võimalik mitte-invasiivsel moel AF uuringu abil analüüsida ning hinnata haiguse progressiooni.Inherited retinal diseases are the leading cause of visual impairment among the working age-group in the developed countries. Because of genetic and phenotypical heterogeneity, diagnosis and understanding pathogenesis of inherited retinal disease has been challenging. Retinal imaging studies which are noninvasive, are an invaluable source of information. Fundus autofluorescence (FAF) utilizes natural fluorophores to create an image of the retina. Lipofuscin is the primary source for short-wavelength autofluorescence (SW-AF) and melanin for near-infrared autofluorescence (NIR-AF). The amount and distribution of these fluorophores changes in the different disease processes and is detectable in FAF images. In this study we analyzed SW-AF and NIR-AF images in cases of genetically confirmed recessive Stargardt disease (STGD1), choroideremia, PROM1-macular disease and ocular albinism. The aim was to qualitatively describe FAF in conditions with varying levels of lipofuscin or melanin as well as to quantify FAF signal intensities. We also aimed at finding new clinical implications for autofluorescence imaging in evaluating inherited retinal disease. We confirmed that melanin is the major source of NIR-AF signal by analyzing ocular albinism carriers and mice models with varying fundus pigmentation, but we also found that presence of melanin can modulate SW-AF signal strength. As a novel finding we confirmed that lipofuscin contributes to NIR-AF signal intensity in cases with excessive bisretinoid lipofuscin levels like seen in STGD1. The analysis of choroideremia and STGD1 patients showed that retinal pigment epithelium atrophy causes loss of signal in both SW-AF and NIR-AF, but NIR-AF could be more sensitive in detecting early cell degeneration. Quantifying the autofluorescence signal intensity helps to further understand disease processes as it is an indirect measure for levels of retinal fluorophores. We showed PROM1-macular dystrophy does not present with elevated levels of SW-AF indicating that excessive lipofuscin accumulation is likely not part of its disease mechanism. That knowledge is valuable in differentiating it from phenotypically similar STGD1 or when developing therapeutic approaches. Lipofuscin and melanin are both valuable retinal biomarkers for evaluating retinal health by using non-invasive autofluorescence imaging.https://www.ester.ee/record=b555738

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