An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images.

People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra- and intervisit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.

Accuracy assessment of intra- and intervisit fundus image registration for diabetic retinopathy screening.

PURPOSE: We evaluated the accuracy of a recently developed fundus image registration method (Weighted Vasculature Registration, or WeVaR) and compared it to two top-ranked state-of-the-art commercial fundus mosaicking programs (i2k Retina, DualAlign LLC, and Merge Eye Care PACS, formerly named OIS AutoMontage) in the context of diabetic retinopathy (DR) screening. METHODS: Fundus images of 70 diabetic patients who visited the Rotterdam Eye Hospital in 2012 and 2013 for a DR screening program were registered by all three programs. The registration results were used to produce mosaics from fundus photos that were normalized for luminance and contrast to improve the visibility of small details. These mosaics subsequently were evaluated and ranked by two expert graders to assess the registration accuracy. RESULTS: Merge Eye Care PACS had high registration failure rates compared to WeVaR and i2k Retina (P = 8 × 10(-6) and P = 0.002, respectively). WeVaR showed significantly higher registration accuracy than i2k Retina in intravisit (P ≤ 0.0036) and intervisit (P ≤ 0.0002) mosaics. Therefore, fundus mosaics processed by WeVaR were more likely to have a higher score (odds ratio [OR] = 2.5, P = 10(-5) for intravisit and OR = 2.2, P = 0.006 for intervisit mosaics). WeVaR was preferred more often by the graders than i2k Retina (OR = 6.1, P = 7 × 10(-6)). CONCLUSIONS: WeVaR produced intra- and intervisit fundus mosaics with higher registration accuracy than Merge Eye Care PACS and i2k Retina. Merge Eye Care PACS had higher registration failures than the other two programs. Highly accurate registration methods, such as WeVaR, may potentially be used for more efficient human grading and in computer-aided screening systems for detecting DR progression.

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

Statistical atlas based exudate segmentation.

Diabetic macular edema (DME) is characterized by hard exudates. In this article, we propose a novel statistical atlas based method for segmentation of such exudates. Any test fundus image is first warped on the atlas co-ordinate and then a distance map is obtained with the mean atlas image. This leaves behind the candidate lesions. Post-processing schemes are introduced for final segmentation of the exudate. Experiments with the publicly available HEI-MED data-set shows good performance of the method. A lesion localization fraction of 82.5% at 35% of non-lesion localization fraction on the FROC curve is obtained. The method is also compared to few most recent reference methods.