Microaneurysm detection with radon transform-based classification on retina images.

The creation of an automatic diabetic retinopathy screening system using retina cameras is currently receiving considerable interest in the medical imaging community. The detection of microaneurysms is a key element in this effort. In this work, we propose a new microaneurysms segmentation technique based on a novel application of the radon transform, which is able to identify these lesions without any previous knowledge of the retina morphological features and with minimal image preprocessing. The algorithm has been evaluated on the Retinopathy Online Challenge public dataset, and its performance compares with the best current techniques. The performance is particularly good at low false positive ratios, which makes it an ideal candidate for diabetic retinopathy screening systems.

Automatic detection of retina disease: robustness to image quality and localization of anatomy structure.

The automated detection of diabetic retinopathy and other eye diseases in images of the retina has great promise as a low-cost method for broad-based screening. Many systems in the literature which perform automated detection include a quality estimation step and physiological feature detection, including the vascular tree and the optic nerve / macula location. In this work, we study the robustness of an automated disease detection method with respect to the accuracy of the optic nerve location and the quality of the images obtained as judged by a quality estimation algorithm. The detection algorithm features microaneurysm and exudate detection followed by feature extraction on the detected population to describe the overall retina image. Labeled images of retinas ground-truthed to disease states are used to train a supervised learning algorithm to identify the disease state of the retina image and exam set. Under the restrictions of high confidence optic nerve detections and good quality imagery, the system achieves a sensitivity and specificity of 94.8% and 78.7% with area-under-curve of 95.3%. Analysis of the effect of constraining quality and the distinction between mild non-proliferative diabetic retinopathy, normal retina images, and more severe disease states is included.

Statistical characterization and segmentation of drusen in fundus images.

Age related Macular Degeneration (AMD) is a disease of the retina associated with aging. AMD progression in patients is characterized by drusen, pigmentation changes, and geographic atrophy, which can be seen using fundus imagery. The level of AMD is characterized by standard scaling methods, which can be somewhat subjective in practice. In this work we propose a statistical image processing approach to segment drusen with the ultimate goal of characterizing the AMD progression in a data set of longitudinal images. The method characterizes retinal structures with a statistical model of the colors in the retina image. When comparing the segmentation results of the method between longitudinal images with known AMD progression and those without, the method detects progression in our longitudinal data set with an area under the receiver operating characteristics curve of 0.99.

Geographic atrophy segmentation in infrared and autofluorescent retina images using supervised learning.

Geographic Atrophy (GA) of the retinal pigment epithelium (RPE) is an advanced form of atrophic age-related macular degeneration (AMD) and is responsible for about 20% of AMD-related legal blindness in the United States. Two different imaging modalities for retinas, infrared imaging and autofluorescence imaging, serve as interesting complimentary technologies for highlighting GA. In this work we explore the use of neural network classifiers in performing segmentation of GA in registered infrared (IR) and autofluorescence (AF) images. Our segmentation achieved a performance level of 82.5% sensitivity and 92.9% specificity on a per-pixel basis using hold-one-out validation testing. The algorithm, feature extraction, data set and experimental results are discussed and shown.

Practical considerations for optic nerve location in telemedicine.

The projected increase in diabetes in the United States and worldwide has created a need for broad-based, inexpensive screening for diabetic retinopathy (DR), an eye disease which can lead to vision impairment. A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion / anomaly detection is a low-cost way of achieving broad-based screening. In this work we report on the effect of quality estimation on an optic nerve (ON) detection method with a confidence metric. We report on an improvement of the method using a data set from an ophthalmologist practice then show the results of the method as a function of image quality on a set of images from an on-line telemedicine network collected in Spring 2009 and another broad-based screening program. We show that the fusion method, combined with quality estimation processing, can improve detection performance and also provide a method for utilizing a physician-in-the-loop for images that may exceed the capabilities of automated processing.

Elliptical local vessel density: a fast and robust quality metric for retinal images.

A great effort of the research community is geared towards the creation of an automatic screening system able to promptly detect diabetic retinopathy with the use of fundus cameras. In addition, there are some documented approaches for automatically judging the image quality. We propose a new set of features independent of field of view or resolution to describe the morphology of the patient's vessels. Our initial results suggest that these features can be used to estimate the image quality in a time one order of magnitude shorter than previous techniques.

Generalized filtering in acoustooptic systems using area modulation.

We report on a technique for implementing general filtering operations in acoustooptic signal processing systems. We use a binary recording method called area modulation to reduce linearity problems associated with spatial light modulators that operate by controlling the amplitude of light. We present an analysis of this method, we report on experiments with an acoustooptic system to verify the analysis, using photographic film and a liquid crystal display to implement the area modulation masks, and we discuss the limitations of the technique.