Integrated Analysis of Vascular and Non-Vascular Changes from Color Retinal Fundus Image Sequences

Algorithms are presented for integrated analysis of both vascular and non-vascular changes observed in longitudinal time-series of color retinal fundus images, extending our prior work. A Bayesian model selection algorithm that combines color change information, and image understanding systems outputs in a novel manner is used to analyze vascular changes such as increase/decrease in width, and disappearance / appearance of vessels, as well as non-vascular changes such as appearance/disappearance of different kinds of lesions. The overall system is robust to false changes due to inter- and intra-image non-uniform illumination, imaging artifacts such as dust particles in the optical path, alignment errors and outliers in the training-data. An expert observer validated the algorithms on 54 regions selected from 34 image pairs. The regions were selected such that they represented diverse types of changes of interest, as well as no-change regions. The algorithm achieved a sensitivity of 82 % and a specificity of 86 % on these regions. The proposed system is intended for applications such as retinal screening, image-reading centers, and as an aid in clinical diagnosis, monitoring of disease progression, and quantitative assessment of treatment efficacy

Automated Analysis of Longitudinal Changes in Color Retinal Fundus Images for Monitoring Diabetic Retinopathy," Accepted for publication in the

Automated image analysis algorithms are presented for detection and classification of changes in longitudinal time-series of color retinal fundus images. They are applicable to clinical practice, quantitative scoring of clinical trials, computer-assisted reading centers, and training. This work focuses on diabetes-related changes, although the techniques have broader applicability. Retinal features, including the vasculature, vessel branching/crossover locations, optic disk, and fovea are extracted automatically. The images are registered to sub-pixel accuracy using a 12-dimensional mapping that accounts for the unknown retinal curvature and camera parameters. The images are corrected for non-uniform illumination using a robust homomorphic surface fitting algorithm. The changes in non-vascular regions are segmented using an algorithm that is robust to relevant artifacts such as dust particles in the optical path. They are classified into five clinically significant categories using a Bayesian algorithm constrained by Markov Random Fields. A flicker animation overlaid with change analysis results allows qualitative and quantitative assessment by the user. A multi-observer validation on 43 image pairs from 22 eyes involving non-proliferative and proliferative diabetic retinopathies, showed a 96.83 % change detection rate, a 3.17 % miss rate, and a 17.65 % false alarm rate. The performance in correctly classifying the changes was 97.39 %

Ref # TITB-00244-2006.R2 Automated Retinal Image Analysis over the Internet

Abstract—Retinal clinicians and researchers make extensive use of images, and the current emphasis is on digital imaging of the retinal fundus. The goal of this paper is to introduce a system, known as RIVERS (Retinal Image Vessel Extraction and Registration System), which provides the community of retinal clinicians, researchers, and study directors an integrated suite of advanced digital retinal image analysis tools over the Internet. The capabilities include vasculature tracing and morphometry, joint (simultaneous) montaging of multiple retinal fields, cross-modality registration (color/red-free fundus photographs, and fluorescein angiograms), and generation of flicker animations for visualization of changes from longitudinal image sequences. Each capability has been carefully-validated in our previous research work. The integrated internet-based system can enable significant advances in retina-related clinical diagnosis, visualization of the complete fundus at full resolution from multiple low-angle views, analysis of longitudinal changes, research on the retinal vasculature, and objective, quantitative computer-assisted scoring of clinical trials imagery. It could pave the way for future screening services from optometry facilities. Index Terms—internet-based, registration, alignment, retinal image analysis, vasculature tracing