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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

(a) The reference image IR (CFP) and the blood vessel ridge image (Ridge_ImageR) superimposed as black skeletons. (b) The target image IT (OFI) and its blood vessel ridge image (Ridge_ImageT) superimposed as red skeletons. (c) Registration result between the two blood vessel ridge images. (d) Registration result between the two original intensity images.
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g002: (a) The reference image IR (CFP) and the blood vessel ridge image (Ridge_ImageR) superimposed as black skeletons. (b) The target image IT (OFI) and its blood vessel ridge image (Ridge_ImageT) superimposed as red skeletons. (c) Registration result between the two blood vessel ridge images. (d) Registration result between the two original intensity images.

Mentions: The Ridge-Branch-Based (RBB) detection algorithm [19,20] is used to detect blood vessel ridges. First we analyze every pixel in the image and extract the ridge pixels. For each ridge pixel we define a local ridge segment and a Segment-Based Ridge Feature vector, based on the local structure information of all the ridge pixels in the ridge segment centered at the investigated ridge pixel. A simple classifier examines the Segment-Based Ridge Features and divides the ridge pixels into blood vessel ridge pixels and non-vessel ridge pixels. Further processing is employed to extend and connect blood vessel ridge branches, as well as to remove isolated small branches. For the details on this algorithm the readers are directed to the reference. The resultant ridge images are denoted by Ridge_ImageR and Ridge_ImageT. Figure 2(a, b)Fig. 2


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)

(a) The reference image IR (CFP) and the blood vessel ridge image (Ridge_ImageR) superimposed as black skeletons. (b) The target image IT (OFI) and its blood vessel ridge image (Ridge_ImageT) superimposed as red skeletons. (c) Registration result between the two blood vessel ridge images. (d) Registration result between the two original intensity images.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

g002: (a) The reference image IR (CFP) and the blood vessel ridge image (Ridge_ImageR) superimposed as black skeletons. (b) The target image IT (OFI) and its blood vessel ridge image (Ridge_ImageT) superimposed as red skeletons. (c) Registration result between the two blood vessel ridge images. (d) Registration result between the two original intensity images.
Mentions: The Ridge-Branch-Based (RBB) detection algorithm [19,20] is used to detect blood vessel ridges. First we analyze every pixel in the image and extract the ridge pixels. For each ridge pixel we define a local ridge segment and a Segment-Based Ridge Feature vector, based on the local structure information of all the ridge pixels in the ridge segment centered at the investigated ridge pixel. A simple classifier examines the Segment-Based Ridge Features and divides the ridge pixels into blood vessel ridge pixels and non-vessel ridge pixels. Further processing is employed to extend and connect blood vessel ridge branches, as well as to remove isolated small branches. For the details on this algorithm the readers are directed to the reference. The resultant ridge images are denoted by Ridge_ImageR and Ridge_ImageT. Figure 2(a, b)Fig. 2

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