A Method of Drusen Measurement Based on the Geometry of Fundus Reflectance

BACKGROUND: The hallmarks of age-related macular degeneration, the leading cause of blindness in the developed world, are the subretinal deposits known as drusen. Drusen identification and measurement play a key role in clinical studies of this disease. Current manual methods of drusen measurement are laborious and subjective. Our purpose was to expedite clinical research with an accurate, reliable digital method. METHODS: An interactive semi-automated procedure was developed to level the macular background reflectance for the purpose of morphometric analysis of drusen. 12 color fundus photographs of patients with age-related macular degeneration and drusen were analyzed. After digitizing the photographs, the underlying background pattern in the green channel was leveled by an algorithm based on the elliptically concentric geometry of the reflectance in the normal macula: the gray scale values of all structures within defined elliptical boundaries were raised sequentially until a uniform background was obtained. Segmentation of drusen and area measurements in the central and middle subfields (1000 μm and 3000 μm diameters) were performed by uniform thresholds. Two observers using this interactive semi-automated software measured each image digitally. The mean digital measurements were compared to independent stereo fundus gradings by two expert graders (stereo Grader 1 estimated the drusen percentage in each of the 24 regions as falling into one of four standard broad ranges; stereo Grader 2 estimated drusen percentages in 1% to 5% intervals). RESULTS: The mean digital area measurements had a median standard deviation of 1.9%. The mean digital area measurements agreed with stereo Grader 1 in 22/24 cases. The 95% limits of agreement between the mean digital area measurements and the more precise stereo gradings of Grader 2 were -6.4 % to +6.8 % in the central subfield and -6.0 % to +4.5 % in the middle subfield. The mean absolute differences between the digital and stereo gradings 2 were 2.8 +/- 3.4% in the central subfield and 2.2 +/- 2.7% in the middle subfield. CONCLUSIONS: Semi-automated, supervised drusen measurements may be done reproducibly and accurately with adaptations of commercial software. This technique for macular image analysis has potential for use in clinical research

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and TechnologyDrusen are common features in the ageing macula, caused by accumulation of extracellular materials beneath the retinal surface, visible in retinal fundus images as yellow spots. In the ophthalmologists’ opinion, the evaluation of the total drusen area, in a sequence of images taken during a treatment, will help to understand the disease progression and effectiveness. However, this evaluation is fastidious and difficult to reproduce when performed manually. A literature review on automated drusen detection showed that the works already published were limited to techniques of either adaptive or global thresholds which showed a tendency to produce a significant number of false positives. The purpose for this work was to propose an alternative method to automatically quantify drusen using advanced digital image processing techniques. This methodology is based on a detection and modelling algorithm to automatically quantify drusen. It includes an image pre-processing step to correct the uneven illumination by using smoothing splines fitting and to normalize the contrast. To quantify drusen a detection and modelling algorithm is adopted. The detection uses a new gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. These are then fitted by Gaussian functions, to produce a model of the image, which is used to compute the affected areas. To validate the methodology, two software applications, one for semi-automated (MD3RI) and other for automated detection of drusen (AD3RI), were implemented. The first was developed for Ophthalmologists to manually analyse and mark drusen deposits, while the other implemented algorithms for automatic drusen quantification.Four studies to assess the methodology accuracy involving twelve specialists have taken place. These compared the automated method to the specialists and evaluated its repeatability. The studies were analysed regarding several indicators, which were based on the total affected area and on a pixel-to-pixel analysis. Due to the high variability among the graders involved in the first study, a new evaluation method, the Weighed Matching Analysis, was developed to improve the pixel-to-pixel analysis by using the statistical significance of the observations to differentiate positive and negative pixels. From the results of these studies it was concluded that the methodology proposed is capable to automatically measure drusen in an accurate and reproducible process. Also, the thesis proposes new image processing algorithms, for image pre-processing, image segmentation,image modelling and images comparison, which are also applicable to other image processing fields

Retinal Fundus Anjiyografi Görüntülerinde Drusen Alanlarının Otomatik Tespiti ve Hesaplanması

Computer aided detection (CAD) systems are widely used in the analysis of biomedical images. In this paper, we present a novel CAD system to detect age-related macular degeneration (ARMD) on retinal fundus fluorescein angiography (FFA) images, and we provide an areal size calculation of pathogenic drusen regions. The purpose of this study is to enable identification and areal size calculation of ARMD-affected regions with the developed CAD system; hence, we aim to discover the condition of the disease as well as facilitate long-term patient follow-up treatment. With the aid of this system, assessing the marked regions will take less time for ophthalmologists and observing the progress of the treatment will be a simpler process. The CAD system consists of four stages, a) preprocessing, b) segmentation, c) region of interest detection and d)feature extraction and drusen area detection. Detection through CAD and calculation of drusen regions were performed with a dataset composed of 75 images. The results obtained from the developed CAD system were examined by a specialist ophthalmologist, and the performance criteria of the CAD system are reported as conclusions. As a result, with 66 correct detections and 9 incorrect detections, the developed CAD system achieved an accuracy rate of 88%.Bilgisayar destekli tespit (BDT) sistemleri biyomedikal görüntülerin analizinde geniş bir kullanım alanına sahiptir. Bu çalışmada retinal fundus anjiyografi görüntüleri üzerinde yaşa bağlı makula dejenerasyonu (YBMD) hastalığının tespiti için bir BDT sistemi gerçekleştirilmiş ve patojenik drusen alanlarının büyüklüğünün hesaplanması sağlanmıştır. Çalışmanın amacı YBMD hastalığının görüldüğü alanların tespitinin ve büyüklüğünü hesaplamanın yanında hastalığa karşı uygulanan tedavinin sonucunun takibini de sağlamaktır. Geliştirilen sistemin yardımıyla optalmoloji uzmanları işaretlenen alanları kısa sürede tespit edebilecek ve hastalığın tedaviye verdiği cevabı basit bir şekilde gözlemleyebileceklerdir. Geliştirilen BDT sistemi 4 aşamadan oluşmaktadır, a) önişleme aşaması, b) bölütleme aşaması, c) ilgi alanı tespiti ve d) öznitelik çıkarma ve tespit aşaması. Geliştirilen BDT sistemi 75 görüntüden oluşan bir verisetiyle test edilmiştir. BST sisteminin elde ettiği sonuçlar bir optalmoloji uzmanıyla karşılaştırılarak sonuç bölümünde sunulmuştur. Geliştirilen BDT sistemi 66 doğru, 9 hatalı tespit yaparak %88 doğruluk oranı sağlamıştır

ACHIKO-D350: A dataset for early AMD detection and drusen segmentation

Age related macular degeneration is the third leading cause of global blindness. Its prevalence is increasing in these years for the coming of ”aging population”. Early detection and grading can prevent it from becoming severe and protect vision. Drusen is an important indicator for AMD. Thus automatic drusen detection and segmentation has attracted much research attention in the past years. However, a barrier handicapping the research of drusen segmentation is the lack of a public dataset and test platform. To address this issue, in this paper, we publish a dataset, named ACHIKO-D350, with manually marked drusen boundary. ACHIKO-D350 includes 254 healthy fundus images and 96 fundus images with drusen. The images with drusen cover a wide range of types, including images with sparsely distributed drusen or clumped drusen, images of poor quality, and both well macular centered images and mis-centered images. ACHIKO-D350 will be used for performance evaluation of drusen segmentation methods. It will facilitate an objective evaluation and comparison

Adaptive Super-Candidate Based Approach for Detection and Classification of Drusen on Retinal Fundus Images

Identification and characterization of drusen is essential for the severity assessment of age-related macular degeneration (AMD). Presented here is a novel super-candidate based approach, combined with robust preprocessing and adaptive thresholding for detection of drusen, resulting in accurate segmentation with the mean lesion-level overlap of 0.75, even in cases with non-uniform illumination, poor contrast and con- founding anatomical structures. We also present a feature based lesion- level discrimination analysis between hard and soft drusen. Our method gives sensitivity of 80% for high specificity above 90% and high sensitivity of 95% for specificity of 70% on representative pathological databases (STARE and ARIA) for both detection and discrimination

In Vivo Multimodal Imaging of Drusenoid Lesions in Rhesus Macaques.

Nonhuman primates are the only mammals to possess a true macula similar to humans, and spontaneously develop drusenoid lesions which are hallmarks of age-related macular degeneration (AMD). Prior studies demonstrated similarities between human and nonhuman primate drusen based on clinical appearance and histopathology. Here, we employed fundus photography, spectral domain optical coherence tomography (SD-OCT), fundus autofluorescence (FAF), and infrared reflectance (IR) to characterize drusenoid lesions in aged rhesus macaques. Of 65 animals evaluated, we identified lesions in 20 animals (30.7%). Using the Age-Related Eye Disease Study 2 (AREDS2) grading system and multimodal imaging, we identified two distinct drusen phenotypes - 1) soft drusen that are larger and appear as hyperreflective deposits between the retinal pigment epithelium (RPE) and Bruchs membrane on SD-OCT, and 2) hard, punctate lesions that are smaller and undetectable on SD-OCT. Both exhibit variable FAF intensities and are poorly visualized on IR. Eyes with drusen exhibited a slightly thicker RPE compared with control eyes (+3.4 μm, P=0.012). Genetic polymorphisms associated with drusenoid lesions in rhesus monkeys in ARMS2 and HTRA1 were similar in frequency between the two phenotypes. These results refine our understanding of drusen development, and provide insight into the absence of advanced AMD in nonhuman primates