2-D registration and 3-D shape inference of the retinal fundus from fluorescein images

• Published in: Medical image analysis Vol. 12; no. 2; pp. 174 - 190
• Main Authors: Choe, Tae Eun, Medioni, Gerard, Cohen, Isaac, Walsh, Alexander C., Sadda, SriniVas R.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2008
• Subjects: 3-D reconstruction; Algorithms; Artificial Intelligence; Fluorescein; Fluorescein Angiography - instrumentation; Fluorescein Angiography - methods; Global registration; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Mutual information; Pattern Recognition, Automated - methods; Phantoms, Imaging; Reproducibility of Results; Retinal fundus image; Sensitivity and Specificity; Subtraction Technique; Global registration; Retinal fundus image; Mutual information; 3-D reconstruction
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2007.10.002

This study presents methods to 2-D registration of retinal image sequences and 3-D shape inference from fluorescein images. The Y-feature is a robust geometric entity that is largely invariant across modalities as well as across the temporal grey level variations induced by the propagation of the dye in the vessels. We first present a Y-feature extraction method that finds a set of Y-feature candidates using local image gradient information. A gradient-based approach is then used to align an articulated model of the Y-feature to the candidates more accurately while optimizing a cost function. Using mutual information, fitted Y-features are subsequently matched across images, including colors and fluorescein angiographic frames, for registration. To reconstruct the retinal fundus in 3-D, the extracted Y-features are used to estimate the epipolar geometry with a plane-and-parallax approach. The proposed solution provides a robust estimation of the fundamental matrix suitable for plane-like surfaces, such as the retinal fundus. The mutual information criterion is used to accurately estimate the dense disparity map. Our experimental results validate the proposed method on a set of difficult fluorescein image pairs.