3-D retinal curvature estimation.
We study 3-D retinal curvature estimation from multiple images that provides the fundamental geometry of the human retina and could be used for 3-D retina visualization and disease diagnosis purposes. An affine camera model is used for 3-D reconstruction due to its simplicity, linearity, and robustness. A major challenge is that a series of optics is involved in the retinal imaging process, including an actual fundus camera, a digital camera, and the optics of the human eye, all of which cause significant nonlinear distortions in retinal images. In this paper, we develop a new constrained optimization method that considers both the geometric shape of the human retina and nonlinear lens distortions. Moreover, we examine a variety of lens distortion models to approximate the optics of the human eye in order to create a smooth spherical surface for curvature estimation. The experimental results on both synthetic data and real retinal images validate the proposed algorithm.
Chanwimaluang
Thitiporn
T
School of Electrical and Computer Engineering, Oklahoma State University (OSU), Stillwater, OK 74078, USA. thitiporn.chanwimaluang@nectec.or.th
Fan
Guoliang
G
Yen
Gary G
GG
Fransen
Stephen R
SR
D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007908:Lens, Crystalline / Q000033:anatomy & histology; D008954:Models, Biological; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012160:Retina / Q000033:anatomy & histology*