E. Simon Barriga

Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images

PURPOSE To describe and evaluate the performance of an algorithm that automatically classifies images with pathologic features commonly found in diabetic retinopathy (DR) and age-related macular degeneration (AMD). METHODS Retinal digital photographs (N = 2247) of three fields of view (FOV) were obtained of the eyes of 822 patients at two centers: The Retina Institute of South Texas (RIST, San Antonio, TX) and The University of Texas Health Science Center San Antonio (UTHSCSA). Ground truth was provided for the presence of pathologic conditions, including microaneurysms, hemorrhages, exudates, neovascularization in the optic disc and elsewhere, drusen, abnormal pigmentation, and geographic atrophy. The algorithm was used to report on the presence or absence of disease. A detection threshold was applied to obtain different values of sensitivity and specificity with respect to ground truth and to construct a receiver operating characteristic (ROC) curve. RESULTS The system achieved an average area under the ROC curve (AUC) of 0.89 for detection of DR and of 0.92 for detection of sight-threatening DR (STDR). With a fixed specificity of 0.50, the systems sensitivity ranged from 0.92 for all DR cases to 1.00 for clinically significant macular edema (CSME). CONCLUSIONS A computer-aided algorithm was trained to detect different types of pathologic retinal conditions. The cases of hard exudates within 1 disc diameter (DD) of the fovea (surrogate for CSME) were detected with very high accuracy (sensitivity = 1, specificity = 0.50), whereas mild nonproliferative DR was the most challenging condition (sensitivity = 0.92, specificity = 0.50). The algorithm was also tested on images with signs of AMD, achieving a performance of AUC of 0.84 (sensitivity = 0.94, specificity = 0.50).

Detection of neovascularization in the optic disc using an AM-FM representation, granulometry, and vessel segmentation

Neovascularization, defined as abnormal formation of blood vessels in the retina, is a sight-threatening condition indicative of late-stage diabetic retinopathy (DR). Ischemia due to leakage of blood vessels causes the body to produce new and weak vessels that can lead to complications such as vitreous hemorrhages. Neovascularization on the disc (NVD) is diagnosed when new vessels are located within one disc-diameter of the optic disc. Accurately detecting NVD is important in preventing vision loss due to DR. This paper presents a method for detecting NVD in digital fundus images. First, a region of interest (ROI) containing the optic disc is manually selected from the image. By adaptively combining contrast enhancement methods with a vessel segmentation technique, the ROI is reduced to the regions indicated by the segmented vessels. Textural features extracted by using amplitude-modulation frequency-modulation (AM-FM) techniques and granulometry are used to differentiate NVD from a normal optic disc. Partial least squares is used to perform the final classification. Leave-one-out cross-validation was used to evaluate the performance of the system with 27 NVD and 30 normal cases. We obtained an area under the receiver operator characteristic curve (AUC) of 0.85 by using all features, increasing to 0.94 with feature selection.

A Multiscale Optimization Approach to Detect Exudates in the Macula

Pathologies that occur on or near the fovea, such as clinically significant macular edema (CSME), represent high risk for vision loss. The presence of exudates, lipid residues of serous leakage from damaged capillaries, has been associated with CSME, in particular if they are located one optic disc-diameter away from the fovea. In this paper, we present an automatic system to detect exudates in the macula. Our approach uses optimal thresholding of instantaneous amplitude (IA) components that are extracted from multiple frequency scales to generate candidate exudate regions. For each candidate region, we extract color, shape, and texture features that are used for classification. Classification is performed using partial least squares (PLS). We tested the performance of the system on two different databases of 652 and 400 images. The system achieved an area under the receiver operator characteristic curve (AUC) of 0.96 for the combination of both databases and an AUC of 0.97 for each of them when they were evaluated independently.

Fast vessel segmentation in retinal images using multi-scale enhancement and second-order local entropy

Retinal vasculature is one of the most important anatomical structures in digital retinal photographs. Accurate segmentation of retinal blood vessels is an essential task in automated analysis of retinopathy. This paper presents a new and effective vessel segmentation algorithm that features computational simplicity and fast implementation. This method uses morphological pre-processing to decrease the disturbance of bright structures and lesions before vessel extraction. Next, a vessel probability map is generated by computing the eigenvalues of the second derivatives of Gaussian filtered image at multiple scales. Then, the second order local entropy thresholding is applied to segment the vessel map. Lastly, a rule-based decision step, which measures the geometric shape difference between vessels and lesions is applied to reduce false positives. The algorithm is evaluated on the low-resolution DRIVE and STARE databases and the publicly available high-resolution image database from Friedrich-Alexander University Erlangen-Nuremberg (Germany). The proposed method achieved comparable performance to state of the art unsupervised vessel segmentation methods with a competitive faster speed on the DRIVE and STARE databases. For the high resolution fundus image database, the proposed algorithm outperforms an existing approach both on performance and speed. The efficiency and robustness make the blood vessel segmentation method described here suitable for broad application in automated analysis of retinal images.

Automated image quality evaluation of retinal fundus photographs in diabetic retinopathy screening

This paper presents a system that can automatically determine whether the quality of a retinal image is sufficient for computer-based diabetic retinopathy (DR) screening. The system integrates global histogram features, textural features, and vessel density, as well as a local non-reference perceptual sharpness metric. A partial least square (PLS) classifier is trained to distinguish low quality images from normal quality images. The system was evaluated on a large, representative set of 1884 non-mydriatic retinal images from 412 subjects. An area under the ROC curve of 96% was achieved.

Automatic system for diabetic retinopathy screening based on AM-FM, partial least squares, and support vector machines

Diabetic retinopathy (DR) is a disease that affects over 170 million people worldwide. In the United States, it is estimated that over 10 million diabetics do not receive the recommended annual eye examinations, significantly increasing their risk of vision loss. In this paper we present an automatic system to detect the presence of DR by analyzing a photograph of the central field of the retina. The system applies Amplitude Modulation-Frequency Modulation (AM-FM) for feature extraction, and partial least squares (PLS) and support vector machines (SVM) for classification. We tested the system on a total of 400 images, and obtained an area under the ROC curve (AUC) of 0.86, and corresponding sensitivity/specificity of 98%/68%. We also tested the accuracy of the system for patients needing immediate referral to a specialist, obtaining an AUC of 0.98.

Independent component analysis using prior information for signal detection in a functional imaging system of the retina

Independent component analysis (ICA) is a statistical technique that estimates a set of sources mixed by an unknown mixing matrix using only a set of observations. For this purpose, the only assumption is that the sources are statistically independent. In many applications, some information about the nature of the unknown signals is available. In this paper we show a method for incorporating prior information about the mixing matrix to increase the levels of detection of responses to visual stimuli. Experimentally, our method matches the performance of known ICA algorithms for high SNR and can greatly improve the performance for low levels of SNR or low levels of signal-to-background ratio (SBR). For the problem of signal extraction, we have achieved detection for signals as small as 0.01% (-40 dB SBR) in hybrid live/synthetic data simulations. In experiments using a functional imager of the retina, measured changes in reflectance in response to visual stimulus are in the order of 0.1-1% of the total pixel intensity value, which makes the functional signal difficult to detect by standard methods. The results of the analysis show that using ICA-P signal levels of 0.1% can be detected. The approach also generalizes the standard Infomax algorithm which can be thought of as a special case of ICA-P when the confidence parameter or a tolerance value is zero. For in vivo animal experiments, we show that signal detection agreement over a range of confidence values parameters can be used to establish reflectance changes in response to the visual stimulus.

Automated retinal vessel type classification in color fundus images

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

Multiscale AM-FM decompositions with GPU acceleration for diabetic retinopathy screening

A Computer Aided Diagnosis system based on multiscale amplitude-modulation frequency-modulation (AM-FM) methods has been recently developed for discriminating between normal and pathological retinal images. The original Matlab implementation of this system required large amounts of computational time and memory resources that would not permit real-time patient consultation. In this manuscript, we present a new implementation of the multiscale AM-FM decomposition, converted from MATLAB code into C/CUDA (Compute Unified Device Architecture) code, in order to take advantage of the graphics processing units (GPU) to significantly reduce the running time and memory resources.

Computational basis for risk stratification of peripheral neuropathy from thermal imaging

The goal of this paper is to present a computer-based system for analyzing thermal images in the detection of preclinical stages of peripheral neuropathy (PN) or diabetic foot. Today, vibration perception threshold (VPT) and sensory tests with a monofilament are used as simple, noninvasive methods for identifying patients who have lost sensation in their feet. These tests are qualitative and are ineffective in stratifying risk for PN in a diabetic patient. In our system a cold stimulus applied to the foot causes a thermoregulatory and corresponding microcirculation response of the foot. A thermal video monitors the recovery of the microcirculation in the foot plantar. Thermal videos for 8 age-matched subjects were analyzed. Six sites were tracked and an average thermal emittance calculated. Characteristics of the recovery curve were extracted using coefficients from an exponential curve fitting process and compared among subjects. The magnitude of the recovery was significantly different for the two classes of subjects. Our system shows evidence of differences between both groups, which could lead to a quantitative test to screen and diagnose peripheral neuropathy.