Registration of Color and OCT Fundus Images Using Low-dimensional Step Pattern Analysis
Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are many times being exploited, they have not been applied to color fundus and optic...
Saved in:
Published in | Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 pp. 214 - 221 |
---|---|
Main Authors | , , , , , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
|
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are many times being exploited, they have not been applied to color fundus and optical coherence tomography (OCT) fundus image pairs. OCT fundus images are challenging to register as they are often degraded by speckle noise. The descriptors also demand high dimensionality to adequately represent the features of interest. To this end, this paper presents a registration algorithm coined low-dimensional step pattern analysis (LoSPA), tailored to achieve low dimensionality while providing sufficient distinctiveness to effectively register OCT fundus images with color fundus photographs. The algorithm locates hypotheses of robust corner features based on connecting edges from the edge maps, mainly formed by vascular junctions. It continues with describing the corner features in a rotation invariant manner using step patterns. These customized step patterns are insensitive to intensity changes. We conduct comparative evaluation and LoSPA achieves a higher success rate in registration when compared to the state-of-the-art algorithms. |
---|---|
ISBN: | 3319245708 9783319245706 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-319-24571-3_26 |