Automated Change Analysis From Fluorescein Angiograms for Monitoring Wet Macular Degeneration

Detection and analysis of changes from retinal images is important in clinical practice, quantitative scoring of clinical trials, computer-assisted reading centers, and in medical research. This paper presents a fully-automated approach for robust detection and classification of changes in longitudinal time-series of fluorescein angiograms (FA). The changes of interest here are related to the development of choroidal neo-vascularization (CNV) in wet macular degeneration. Specifically, the changes in CNV regions as well as the retinal pigment epithelium (RPE) hypertrophic regions are detected and analyzed to study the progression of disease and effect of treatment. Retinal features including the vasculature, vessel branching/crossover locations, optic disk and location of the fovea are first segmented automatically. The images are then registered to sub-pixel accuracy using a 12-dimensional mapping that accounts for the unknown retinal curvature and camera parameters. Spatial variations in illumination are removed using a surface fitting algorithm that exploits the segmentations of the various features. The changes are identified in the regions of interest and a Bayesian classifier is used to classify the changes into clinically significant classes. The automated change analysis algorithms were found to have a success rate of 83%

[1]  Til Aach,et al.  Bayesian algorithms for adaptive change detection in image sequences using Markov random fields , 1995, Signal Process. Image Commun..

[2]  F. Ferris,et al.  Age-related macular degeneration and blindness due to neovascular maculopathy. , 1984, Archives of ophthalmology.

[3]  Charles V. Stewart,et al.  A Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Badrinath Roysam,et al.  Integrated Analysis of Vascular and Nonvascular Changes From Color Retinal Fundus Image Sequences , 2007, IEEE Transactions on Biomedical Engineering.

[5]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Joni-Kristian Kämäräinen,et al.  Feature representation and discrimination based on Gaussian mixture model probability densities - Practices and algorithms , 2006, Pattern Recognit..

[7]  Chia-Ling Tsai,et al.  The dual-bootstrap iterative closest point algorithm with application to retinal image registration , 2003, IEEE Transactions on Medical Imaging.

[8]  Hong Shen,et al.  Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms , 1999, IEEE Transactions on Information Technology in Biomedicine.

[9]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[10]  Michael H. Goldbaum,et al.  Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels , 2003, IEEE Transactions on Medical Imaging.

[11]  William E. Higgins,et al.  Symmetric region growing , 2003, IEEE Trans. Image Process..

[12]  A. Pinz,et al.  Mapping the human retina , 1996, IEEE Transactions on Medical Imaging.