3D Alignment and Change Detection from Uncalibrated Eye Images
Abstract
Analyzing change in the 3D structure of the optic disc over time has long been recognized as central to the diagnosis of glaucoma but has been inadequately addressed by computer vision methods. Currently, clinicians examine stereo pairs from different time instants for interval changes indicative of glaucoma. Due to the clinical procedures in capturing optic disc images, these stereo pairs are usually completely uncalibrated - the camera intrinsics and extrinsics are unknown. Clinicians have to account for these unknown factors and hence their diagnoses of optic disc stability or change are subjective. Changes in the 3D structure of the optic disc are typically accompanied by changes in the 3D structure of blood vessels in that region. Therefore, change in the 3D structure of blood vessels can be used for glaucoma diagnosis. In this paper, we introduce a projective geometry based approach that reconstructs and aligns 3D blood vessel networks given two stereo pairs of optic disc images. We demonstrate that this alignment can identify regions where the vessel structure has changed. Since calibration is unavailable, the 3D structures and the alignment have a projective ambiguity; and hence, we cannot use an absolute threshold on the alignment error to automatically identify change. We have therefore developed an interactive tool that highlights regions with the largest alignment errors. This tool demonstrates the utility of our approach and also can guide clinical observers to optic disc regions where they should look for changes. We believe that our approach can serve as a platform to develop much needed novel tools for glaucoma diagnosis.