Comparison of the effectiveness of three retinal camera technologies for malarial retinopathy detection in Malawi.

The purpose of this study was to test the suitability of three available camera technologies (desktop, portable, and i-phone based) for imaging comatose children who presented with clinical symptoms of malaria. Ultimately, the results of the project would form the basis for a design of a future camera to screen for malaria retinopathy (MR) in a resource challenged environment. The desktop, portable, and i-phone based cameras were represented by the Topcon, Pictor Plus, and Peek cameras, respectively. These cameras were tested on N=23 children presenting with symptoms of cerebral malaria (CM) at a malaria clinic, Queen Elizabeth Teaching Hospital in Malawi, Africa. Each patient was dilated for binocular indirect ophthalmoscopy (BIO) exam by an ophthalmologist followed by imaging with all three cameras. Each of the cases was graded according to an internationally established protocol and compared to the BIO as the clinical ground truth. The reader used three principal retinal lesions as markers for MR: hemorrhages, retinal whitening, and vessel discoloration. The study found that the mid-priced Pictor Plus hand-held camera performed considerably better than the lower price mobile phone-based camera, and slightly the higher priced table top camera. When comparing the readings of digital images against the clinical reference standard (BIO), the Pictor Plus camera had sensitivity and specificity for MR of 100% and 87%, respectively. This compares to a sensitivity and specificity of 87% and 75% for the i-phone based camera and 100% and 75% for the desktop camera. The drawback of all the cameras were their limited field of view which did not allow complete view of the periphery where vessel discoloration occurs most frequently. The consequence was that vessel discoloration was not addressed in this study. None of the cameras offered real-time image quality assessment to ensure high quality images to afford the best possible opportunity for reading by a remotely located specialist.

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.

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.

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 system's 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).

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.