In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM) methods for discriminating between normal and pathological retinal images. The method presented in this paper is tested using standard images from the early treatment diabetic retinopathy study. We use 120 regions of 40 × 40 pixels containing four types of lesions commonly associated with diabetic retinopathy (DR) and two types of normal retinal regions that were manually selected by a trained analyst. The region types included microaneurysms, exudates, neovascularization on the retina, hemorrhages, normal retinal background, and normal vessels patterns. The cumulative distribution functions of the instantaneous amplitude, the instantaneous frequency magnitude, and the relative instantaneous frequency angle from multiple scales are used as texture feature vectors. We use distance metrics between the extracted feature vectors to measure interstructure similarity. Our results demonstrate a statistical differentiation of normal retinal structures and pathological lesions based on AM-FM features. We further demonstrate our AM-FM methodology by applying it to classification of retinal images from the MESSIDOR database. Overall, the proposed methodology shows significant capability for use in automatic DR screening.
Researchers have sought to gain greater insight into the mechanisms of the retina and the optic disc at high spatial resolutions that would enable the visualization of small structures such as photoreceptors and nerve fiber bundles. The sources of retinal image quality degradation are aberrations within the human eye, which limit the achievable resolution and the contrast of small image details. To overcome these fundamental limitations, researchers have been applying adaptive optics (AO) techniques to correct for the aberrations. Today, deformable mirror based adaptive optics devices have been developed to overcome the limitations of standard fundus cameras, but at prices that are typically unaffordable for most clinics. In this paper we demonstrate a clinically viable fundus camera with auto-focus and astigmatism correction that is easy to use and has improved resolution. We have shown that removal of low-order aberrations results in significantly better resolution and quality images. Additionally, through the application of image restoration and super-resolution techniques, the images present considerably improved quality. The improvements lead to enhanced visualization of retinal structures associated with pathology.
The purpose of this paper is to present a novel approach for extracting image-based features for classifying age-related macular degeneration (AMD) in digital retinal images. 100 retinal images were classified by an ophthalmologist into 12 categories based on the visual characteristics of the disease. Independent Component Analysis (ICA) was used to extract features at different spatial scales to be used as input to a classifier. The classification used a type of regression, partial least squares. In this experiment ICA replicated the ophthalmologist's visual classification by correctly assigning all 12 images from two of the classes.
In the United States and most of the western world, the leading causes of vision impairment and blindness are age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma. In the last decade, research in automatic detection of retinal lesions associated with eye diseases has produced several automatic systems for detection and screening of AMD, DR, and glaucoma. However. advanced, sight-threatening stages of DR and AMD can present with lesions not commonly addressed by current approaches to automatic screening. In this paper we present an automatic eye screening system based on multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions that addresses not only the early stages, but also advanced stages of retinal and optic nerve disease. Ten different experiments were performed in which abnormal features such as neovascularization, drusen, exudates, pigmentation abnormalities, geographic atrophy (GA), and glaucoma were classified. The algorithm achieved an accuracy detection range of [0.77 to 0.98] area under the ROC curve for a set of 810 images. When set to a specificity value of 0.60, the sensitivity of the algorithm to the detection of abnormal features ranged between 0.88 and 1.00. Our system demonstrates that, given an appropriate training set, it is possible to use a unique algorithm to detect a broad range of eye diseases.
