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Robust multiscale stereo matching from fundus images with radiometric differences
Journal article   Open access

Robust multiscale stereo matching from fundus images with radiometric differences

Li Tang, Mona K Garvin, Kyungmoo Lee, Wallace L M Alward, Young H Kwon and Michael D Abràmoff
IEEE transactions on pattern analysis and machine intelligence, Vol.33(11), pp.2245-2258
11/2011
DOI: 10.1109/TPAMI.2011.69
PMCID: PMC3580181
PMID: 21464502

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Abstract

A robust multiscale stereo matching algorithm is proposed to find reliable correspondences between low contrast and weakly textured retinal image pairs with radiometric differences. Existing algorithms designed to deal with piecewise planar surfaces with distinct features and Lambertian reflectance do not apply in applications such as 3D reconstruction of medical images including stereo retinal images. In this paper, robust pixel feature vectors are formulated to extract discriminative features in the presence of noise in scale space, through which the response of low-frequency mechanisms alter and interact with the response of high-frequency mechanisms. The deep structures of the scene are represented with the evolution of disparity estimates in scale space, which distributes the matching ambiguity along the scale dimension to obtain globally coherent reconstructions. The performance is verified both qualitatively by face validity and quantitatively on our collection of stereo fundus image sets with ground truth, which have been made publicly available as an extension of standard test images for performance evaluation.
Algorithms Imaging, Three-Dimensional - statistics & numerical data Models, Biological Depth Perception Humans Tomography, Optical Coherence - statistics & numerical data Fundus Oculi Ophthalmoscopy - statistics & numerical data Retina - anatomy & histology

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