Registration of OCT Fundus Images with Color Fundus Images Based on Invariant Features

Li, Ping; Chen, Qiang; Fan, Wen; Yuan, Songtao

doi:10.1007/978-3-319-68542-7_40

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of fundus imaging, a widely used technique relies on vascular trees and branch point analysis [33,25]. However, accurate segmentation of the vascular trees is challenging and registration often fails on images with few vessels.…”

Section: Related Workmentioning

confidence: 99%

GLAMpoints: Greedily Learned Accurate Match Points

Truong

Apostolopoulos

Mosinska

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

View full text Add to dashboard Cite

We introduce a novel CNN-based feature point detector -Greedily Learned Accurate Match Points (GLAMpoints)learned in a semi-supervised manner. Our detector extracts repeatable, stable interest points with a dense coverage, specifically designed to maximize the correct matching in a specific domain, which is in contrast to conventional techniques that optimize indirect metrics. In this paper, we apply our method on challenging retinal slitlamp images, for which classical detectors yield unsatisfactory results due to low image quality and insufficient amount of low-level features. We show that GLAMpoints significantly outperforms classical detectors as well as state-of-the-art CNN-based methods in matching and registration quality for retinal images.

show abstract

Section: Related Workmentioning

confidence: 99%