Diagnostic assistance system to detect progression of diabetic retinopathy (DR) in fundus images of follow up examinations of patients with diabetes

Abstract Purpose: An important step in detecting and monitoring DR is regular screening by fundus images. The aim is to identify patients with sight-threatening DR timely to be able to initiate adequate treatment and thus to prevent loss of vision (blindness). We developed an algorithm to detect progression between follow up examinations in order to minimize the time consuming diagnostic of the images by an ophthalmologist. Methods: Digital fundus images of 12 patients (4 female, 8 male) with diabetes (10 type 1, 2 type 2) were retrospectively selected from the records of the Department of Ophthalmology of the Oulu University Hospital. Red-free fundus images of each eye were clinically graded for DR, and eyes with progression were included. A 5-step classification was used: no DR, mild DR, moderate DR, severe non-proliferative DR or proliferative DR. There were at least 5 cases presenting every transition, e.g. from no DR to mild DR. A total of 158 grayscale fundus images of 24 eyes were included. The mean age at time of the first examination was 34 ± 15 years, and 43 ± 13 years at the latest examination. The number of examinations varied between 4 and 9 per eye. For each eye, the progression map of all possible combinations of two individual fundus images were calculated. First, the two images were roughly registered with help of a similarity transformation. A finer registration was implemented patchwise together with the adjustment of the contrast between them as a second step. The next step consisted of the calculation of the difference map between the two images. The remaining noise from the background was filtered as a last step by considering the local noise level from the two source images. Results: The transition between the different grades of DR was correctly detected (true positive) in 91% of the instances, and the absence of transition (true negative) in 94%. In 6% of the cases the algorithm signaled progression without clinically detectable change of DR grade (false positive), and in 9% (false negative) the algorithm was not able to detect clinically detected progression. Conclusions: The results demonstrate that the algorithm developed for the detection of progression of DR in fundus images does reliably highlight changes between the images, and has the potential to reduce the time needed for evaluation of images by an ophthalmologist

The impact of the image conversion factor and image centration on retinal vessel geometric characteristics

Abstract Background: This study aims to use fundus image material from a long-term retinopathy follow-up study to identify problems created by changing imaging modalities or imaging settings (e.g., image centering, resolution, viewing angle, illumination wavelength). Investigating the relationship of image conversion factor and imaging centering on retinal vessel geometric characteristics (RVGC), offers solutions for longitudinal retinal vessel analysis for data obtained in clinical routine. Methods: Retinal vessel geometric characteristics were analyzed in scanned fundus photographs with Singapore-I-Vessel-Assessment using a constant image conversion factor (ICF) and an individual ICF, applying them to macula centered (MC) and optic disk centered (ODC) images. The ICF is used to convert pixel measurements into μm for vessel diameter measurements and to establish the size of the measuring zone. Calculating a constant ICF, the width of all analyzed optic disks is included, and it is used for all images of a cohort. An individual ICF, in turn, uses the optic disk diameter of the eye analyzed. To investigate agreement, Bland-Altman mean difference was calculated between ODC images analyzed with individual and constant ICF and between MC and ODC images. Results: With constant ICF (n = 104 eyes of 52 patients) the mean central retinal equivalent was 160.9 ± 17.08 μm for arteries (CRAE) and 208.7 ± 14.7.4 μm for veins (CRVE). The individual ICFs resulted in a mean CRAE of 163.3 ± 15.6 μm and a mean CRVE of 219.0 ± 22.3 μm. On Bland–Altman analysis, the individual ICF RVGC are more positive, resulting in a positive mean difference for most investigated parameters. Arteriovenous ratio (p = 0.86), simple tortuosity (p = 0.08), and fractal dimension (p = 0.80) agreed well between MC and ODC images, while the vessel diameters were significantly smaller in MC images (p < 0.002). Conclusion: Scanned images can be analyzed using vessel assessment software. Investigations of individual ICF versus constant ICF point out the asset of utilizing an individual ICF. Image settings (ODC vs. MC) were shown to have good agreement