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Registration of OCT fundus images with color fundus photographs based on blood vessel ridges.

Li Y, Gregori G, Knighton RW, Lujan BJ, Rosenfeld PJ - Opt Express (2011)

Bottom Line: Blood vessel ridges are taken as features for registration.Based on this distance a similarity function between the pair image is defined.The average root mean square errors for the affine model are 31 µm (normal) and 59 µm (eyes with disease).

View Article: PubMed Central - PubMed

Affiliation: Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida 33136, USA. yli@med.miami.edu

ABSTRACT
This paper proposes an algorithm to register OCT fundus images (OFIs) with color fundus photographs (CFPs). This makes it possible to correlate retinal features across the different imaging modalities. Blood vessel ridges are taken as features for registration. A specially defined distance, incorporating information of normal direction of blood vessel ridge pixels, is designed to calculate the distance between each pair of pixels to be matched in the pair image. Based on this distance a similarity function between the pair image is defined. Brute force search is used for a coarse registration and then an Iterative Closest Point (ICP) algorithm for a more accurate registration. The registration algorithm was tested on a sample set containing images of both normal eyes and eyes with pathologies. Three transformation models (similarity, affine and quadratic models) were tested on all image pairs respectively. The experimental results showed that the registration algorithm worked well. The average root mean square errors for the affine model are 31 µm (normal) and 59 µm (eyes with disease). The proposed algorithm can be easily adapted to registration for other modality retinal images.

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Related in: MedlinePlus

Given an investigated pixel on Ridge_ImageR, its matching pixel on Ridge_ImageT is the pixel closest to it. The Euclidian distance x with the penalty from normal direction difference θ, produces the modified distance y, which is used to classify the pair of matching pixels as a “successfully matched pair” (when y is less than the threshold ) or a “non-successfully matched pair” (when y is greater than the threshold ).
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g003: Given an investigated pixel on Ridge_ImageR, its matching pixel on Ridge_ImageT is the pixel closest to it. The Euclidian distance x with the penalty from normal direction difference θ, produces the modified distance y, which is used to classify the pair of matching pixels as a “successfully matched pair” (when y is less than the threshold ) or a “non-successfully matched pair” (when y is greater than the threshold ).

Mentions: Given a pixel on the reference ridge image, its matching pixel on the target ridge image is the pixel that is closest to it. The two pixels form one pair of matching pixels (Fig. 3Fig. 3


Registration of OCT fundus images with color fundus photographs based on blood vessel ridges.

Li Y, Gregori G, Knighton RW, Lujan BJ, Rosenfeld PJ - Opt Express (2011)

Given an investigated pixel on Ridge_ImageR, its matching pixel on Ridge_ImageT is the pixel closest to it. The Euclidian distance x with the penalty from normal direction difference θ, produces the modified distance y, which is used to classify the pair of matching pixels as a “successfully matched pair” (when y is less than the threshold ) or a “non-successfully matched pair” (when y is greater than the threshold ).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368356&req=5

g003: Given an investigated pixel on Ridge_ImageR, its matching pixel on Ridge_ImageT is the pixel closest to it. The Euclidian distance x with the penalty from normal direction difference θ, produces the modified distance y, which is used to classify the pair of matching pixels as a “successfully matched pair” (when y is less than the threshold ) or a “non-successfully matched pair” (when y is greater than the threshold ).
Mentions: Given a pixel on the reference ridge image, its matching pixel on the target ridge image is the pixel that is closest to it. The two pixels form one pair of matching pixels (Fig. 3Fig. 3

Bottom Line: Blood vessel ridges are taken as features for registration.Based on this distance a similarity function between the pair image is defined.The average root mean square errors for the affine model are 31 µm (normal) and 59 µm (eyes with disease).

View Article: PubMed Central - PubMed

Affiliation: Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida 33136, USA. yli@med.miami.edu

ABSTRACT
This paper proposes an algorithm to register OCT fundus images (OFIs) with color fundus photographs (CFPs). This makes it possible to correlate retinal features across the different imaging modalities. Blood vessel ridges are taken as features for registration. A specially defined distance, incorporating information of normal direction of blood vessel ridge pixels, is designed to calculate the distance between each pair of pixels to be matched in the pair image. Based on this distance a similarity function between the pair image is defined. Brute force search is used for a coarse registration and then an Iterative Closest Point (ICP) algorithm for a more accurate registration. The registration algorithm was tested on a sample set containing images of both normal eyes and eyes with pathologies. Three transformation models (similarity, affine and quadratic models) were tested on all image pairs respectively. The experimental results showed that the registration algorithm worked well. The average root mean square errors for the affine model are 31 µm (normal) and 59 µm (eyes with disease). The proposed algorithm can be easily adapted to registration for other modality retinal images.

Show MeSH
Related in: MedlinePlus