Glaucoma risk index: Automated glaucoma detection from color fundus images
Introduction
Glaucoma is one of the most common causes of blindness with a mean prevalence of 2.4% for all ages and of 4.7% for ages above 75 years (Klein et al., 1992). The disease is characterized by the progressive degeneration of optic nerve fibers and astrocytes showing a distinct pathogenetic image of the optic nerve head.
Glaucoma leads to (i) structural changes of the optic nerve head (ONH) and the nerve fiber layer and (ii) a simultaneous functional failure of the visual field. The structural changes are manifested by a slowly diminishing neuroretinal rim indicating a degeneration of axons and astrocytes of the optic nerve (Fig. 1).
As lost capabilities of the optic nerve cannot be recovered, early detection and subsequent treatment is essential for affected patients to preserve their vision (Michelson et al., 2008). Commonly, glaucoma diagnosis is based on the patient’s medical history, intraocular pressure, visual field loss tests and the manual assessment of the ONH via ophthalmoscopy or stereo fundus imaging (Lin et al., 2007). To additionally objectify the glaucoma stage and its progression geometric parameters of the ONH are documented. These geometric parameters measure ONH structures that are changing in case of glaucoma disease: optic disk diameter, optic disk area, cup diameter, rim area, mean cup depth, etc.
This contribution provides a data-driven framework extracting a novel glaucoma parameter from fundus images. Contrary to the established detection techniques, it does not require accurate measurements of geometric ONH structures as it performs a statistical data mining technique on the image patterns themselves. The proposed methodology can be transferred to other domains and might be able to extract further parameters providing new insights to other ophthalmic questions.
Section snippets
Background
The glaucoma disease is characterized by the degeneration of optic nerve fibers and astrocytes that is often accompanied by an increased intraocular pressure. Due to the loss of nerve fibers the Retinal Nerve Fiber Layer (RNFL) thickness is decreasing. In the course of disease, the interconnection between the photoreceptors and the visual cortex is reduced. In the worst case, the visual information of the photoreceptors can no longer be transmitted to the brain and visual field loss up to
The concept of Eigenimages for glaucoma detection
Due to the high variability of the ONH appearance, the established determination of geometric ONH parameters utilized for glaucoma detection is difficult to automate.
We consider the described situation for automated glaucoma detection similar to that one stated by Turk and Pentland (1991) for early face detection methods. Before the 1990’s face detection systems characterized faces by a set of geometric parameters such as normalized distances or ratios between characteristic facial landmarks (
Image preprocessing
The proposed appearance-based approach analyzes the entire input image data to capture the glaucoma characteristics. To emphasize these desired characteristics in the input data, the variations not related to the glaucoma disease are excluded from the images in a preprocessing step. This includes variations due to image acquisition, such as inhomogeneous illumination or different optic nerve head locations, but also retinal structures not directly related to glaucoma, e.g. the vessel tree.
Due
Feature extraction
The performed image preprocessing emphasizes glaucomatous variations among the images and allows a generic and appearance-based feature extraction. The high-dimensional preprocessed images P are statistically compressed by PCA to gain compact, and meaningful features f. To capture complementary image information we propose three different generic image representations with different spatial and frequency resolution for feature extraction.
Classification
In the final classification step (Fig. 3), a glaucoma probability and the associated class label such as “glaucoma” or “not glaucoma” is computed from the three different feature types that will be denoted as the Glaucoma Risk Index (GRI).
Evaluation
Based on the presented fully automated processing procedure illustrated in Fig. 3, we achieved a novel probabilistic index that we refer to as Glaucoma Risk Index (GRI). In order to quantify its ability in detecting glaucoma from color fundus images the performance of GRI is first characterized in more detail by some key figures and a reliability analysis. Furthermore, its performance is compared to (i) that of glaucoma experts and (ii) to medically relevant and well established glaucoma
Discussion
A reliable and competitive scheme for an automatic glaucoma detection was presented. The majority of the subjects were correctly classified as already shown. To give a better impression on the classification outcome, Fig. 9 shows examples of correctly classified and misclassified fundus images.
Fig. 9a–c illustrates correctly classified controls characterized by a typical small cup. In contrast, an expanded cup denotes the correctly classified images of glaucomatous eyes (Fig. 9d–f) which
Conclusion
Based on color fundus photos of the eye, the presented procedure for robust and reliable appearance-based feature extraction allows the automated quantification of the probability to suffer from glaucoma disease.
Due to glaucoma specific preprocessing and the appropriate combination of generic features, we are able to successfully apply the generic data-driven approach for this medical classification task. The proposed two-stage classification scheme helps to combine classifiers of different
Acknowledgements
This contribution was supported by the German National Science Foundation (DFG) in context of Collaborative Research Center 539 subproject A4 (SFB 539 A4), the German Academic Exchange Service (DAAD, Germany) and Hungarian Scholarship Board (MÖB, Hungary). The authors gratefully acknowledge funding of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the DFG in the framework of the excellence initiative. R. Bock was supported by the DFG and SAOT. J. Meier is stipendiary of
References (59)
- et al.
Glaucoma detection with the Heidelberg Retina Tomograph 3
Ophthalmology
(2007) - et al.
Automated segmentation of the optic nerve head for diagnosis of glaucoma
Med. Image Anal.
(2005) - et al.
Prevalence of glaucoma. The Beaver Dam Eye Study
Ophthalmology
(1992) - et al.
Boundary detection of optic disk by a modified ASM method
Pattern Recognit.
(2003) - et al.
Optic nerve head and retinal nerve fiber layer analysis: a report by the American Academy of Ophthalmology
Ophthalmology
(2007) - et al.
Segmentation of blood vessels from red-free and fluorescein retinal images
Med. Image Anal.
(2007) - et al.
Frequency doubling technology perimetry abnormalities as predictors of glaucomatous visual field loss
Am. J. Ophthalmol.
(2004) - et al.
Detection of glaucomatous visual field changes using the Moorfields regression analysis of the Heidelberg retina tomograph
Am. J. Ophthalmol.
(2003) - et al.
Fast detection of the optic disc and fovea in color fundus photographs
Med. Image Anal.
(2009) - et al.
Scanning laser polarimetry with variable and enhanced corneal compensation in normal and glaucomatous eyes
Am. J. Ophthalmol.
(2007)
Diagnostic tools for glaucoma detection and management
Surv. Ophthalmol.
Expert agreement in evaluating the optic disc for glaucoma
Ophthalmology
Identification of early glaucoma cases with the scanning laser ophthalmoscope
Ophthalmology
Optic disk feature extraction via modified deformable model technique for glaucoma analysis
Pattern Recognit.
Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features
Invest. Ophthalmol. Vis. Sci.
An active contour model for segmenting and measuring retinal vessels
IEEE Trans. Med. Imag.
Comparison of HRT-3 glaucoma probability score and subjective stereophotograph assessment for prediction of progression in glaucoma
Invest. Ophthalmol. Vis. Sci.
Biometric study of the disc cup in open-angle glaucoma
Graefes Arch. Clin. Exp. Ophthalmol.
Localization and extraction of the optic disc using the fuzzy circular Hough transform
Lect. Notes Comput. Sci.
Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms
IEEE Trans. Inform. Technol. Biomed.
A computational approach to edge detection
IEEE Trans. Pattern Anal. Mach. Intell.
Technique for detecting serial topographic changes in the optic disc and peripapillary retina using scanning laser tomography
Invest. Ophthalmol. Vis. Sci.
A tutorial on -support vector machines
Appl. Stoch. Models Business Ind.
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
Biometrics
Automated detection of retinal layer structures on optical coherence tomography images
Opt. Express
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