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

Abstract

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).

Figures

Figure 1.

(a–c) FOVs 1, 2, and 3 of a normal retina from the RIST database; (d–f) FOVs 1, 2, and 3 of an abnormal retina from the UTHSCSA database.

Figure 2.

Examples of inadequate quality images that were not used by our algorithm.

Figure 3.

Procedure for classifying the retinal images. First, the green channel of the images is selected. Then, the images are processed by AM-FM to decompose them in their AM-FM estimates. Depending on the test, the images are subdivided in ROIs, the macula or the optic disc region is selected, or the entire image without the optic disc is entered into the block of the feature extraction. Features are obtained for each observation. If the image is represented in ROIs, the k-means method is applied; otherwise, the feature selection and the two-step PLS classifier is applied, to obtain the estimated class for each image.

Figure 4.

Structures in the retina captured by the AM-FM estimates using high values of the IA (blue). (a) Region of a retinal image with pathologies; (b) image representation using medium frequencies, which captures dark and bright lesions as well as vasculature; (c) image representation using high frequencies. Note that this image captures most of the bright lesions; (d) region of a retinal image with normal vessel structure; (e) image representation using a very low frequency filter; (f) Image representation of (d) obtained by taking the difference between the very low and the ultralow frequency scales, in this image the thinner vessels are better represented.

Figure 5.

Examples of structures capture by the AM-FM estimates using high values of the IA in macular regions (the circle encloses an area equal to 1 DD from the fovea). (a) Normal macula, (b) macula with hard exudates, (c) normal retina at high frequency, and (d) retina with exudates at high frequency.

Figure 6.

Examples of structures captured by the AM-FM estimates using high values of the IA for two different optic discs. (a) Normal optic disc, (d) NVD, (b, e) IA of the retinas in (a) and (d) at medium frequencies, (c, f) images of (a) and (d) at high frequencies.

Figure 7.

(a) Retinal region with drusen. (b) Structures captured by the AM-FM estimates using high values of the IA at low frequencies.

Figure 8.

ROC curves for the classification of DR, STDR, and NPDR. (a) RIST (b) UTHSCSA databases.

Figure 9.

Retinal images with IRMA. The presence of IRMA was not detected in the image in (a) by the first grader or in the image in (b) by the second grader. (c, d) Images (a) and (b) with enhancement.

Figure A1.

Conceptual AM-FM analysis for horizontally oriented blood vessel edge: (a) Instantaneous frequencies on top of a vessel-like structure. Histograms are shown for (b) instantaneous frequency, (c) instantaneous amplitude, and (d) instantaneous frequency angle.

Figure A2.

Conceptual AM-FM analysis for a round, dark lesion. (a) Instantaneous frequencies on top of the lesion. Histograms are shown for (b) instantaneous frequency, (c) instantaneous amplitude, and (d) instantaneous frequency angle.