Fundus imagemosaic based on the SIFT feature

Abstract

Fundus image mosaic can expand the visual field of view and provide auxiliary diagnosis for fundus diseases. Due to the low contrast and uneven grayscale of fundus images, traditional methods based on gray characteristic are no longer applicable. We propose a fundus image mosaic algorithm based on Scale-invariant feature transform (SIFT), along with a quantitative evaluation criterion. We detect feature points in Gaussian scale space and generate feature descriptors with scale invariance for all feature points. The matching feature point pairs are obtained by evaluating the similarity of their feature descriptors and outliers are eliminated using Random sample consensus (RANSAC) algorithm employed the projective transformation model. After that, the transformation matrix can be calculated based on the obtained matching point pairs. Finally, the multi-view fundus images are fused based on the transformation matrix. To the best of our knowledge, there is no quantitative evaluation reported for fundus image mosaic. Here we propose a criterion based on manual marker to evaluate the mosaic results. The experimental results show that the average accuracy is 2.27±0.5 pixels. Our proposed method has great potential to be applied in existing computer-assisted diagnostic system of ophthalmologic diseases.