Mapping of retinal parameters from combined fundus image and three-dimensional optical coherence tomography
A second retinal characterization data set is mapped to a first retinal characterization dataset. The first retinal characterization dataset is displayed as a first graphical map. The second retinal characterization dataset is displayed as a second graphical map which is mapped to the first graphical map. The second graphical map may be warped and morphed onto the first graphical map. Retinal characterization datasets may be derived either from a fundus image or from a retinal parameter dataset calculated from a three-dimensional optical coherence tomography scan of a retina. Retinal parameter datasets may characterize parameters such as retinal thickness. In an embodiment, a fundus image is warped and morphed onto a retinal surface topographical map.
1. A method for mapping a second retinal characterization dataset to a first retinal characterization dataset, comprising the steps of:
displaying on a display a first graphical map based at least in part on the first retinal characterization dataset, wherein the first graphical map corresponds to a retinal region;
generating a second graphical map based at least in part on the first graphical map and on the second retinal characterization dataset; and
warping and morphing said second graphical map onto said first graphical map, wherein said second graphical map, after warping and morphing, corresponds to said retinal region.
2. The method of claim 1 , wherein
said first retinal characterization dataset is a fundus image; and
said second retinal characterization dataset is a retinal parameter dataset.
3. The method of claim 2 , wherein
said retinal parameter dataset is calculated from a three-dimensional optical coherence tomography (3-D OCT) volume dataset registered to said fundus image.
4. The method of claim 2 , wherein said retinal parameter dataset characterizes at least one of:
retinal thickness; and
retinal layer thickness.
5. The method of claim 1 , wherein
said first retinal characterization dataset is a retinal parameter dataset; and
said second retinal characterization dataset is a fundus image.
6. The method of claim 5 , wherein
said retinal parameter dataset is calculated from a 3-D OCT volume dataset registered to said fundus image.
7. The method of claim 5 , wherein said retinal parameter dataset characterizes at least one of:
retinal thickness;
retinal layer thickness; and
surface topography.
8. The method of claim 1 , wherein
said first retinal characterization dataset is a first retinal parameter dataset; and
said second retinal characterization dataset is a second retinal parameter dataset.
9. The method of claim 8 , wherein:
said first retinal parameter dataset and said second retinal parameter dataset are calculated from a 3-D OCT volume dataset registered to a fundus image.
10. The method of claim 8 , wherein said first retinal parameter dataset and said second retinal parameter dataset characterize at least one of:
retinal thickness; and
retinal layer thickness.
11. An apparatus for mapping a second retinal characterization dataset to a first retinal characterization dataset, comprising:
means for displaying a first graphical map based at least in part on the first retinal characterization dataset, wherein the first graphical map corresponds to a retinal region;
means for generating a second graphical map based at least in part on the first graphical map and on the second retinal characterization dataset; and
means for warping and morphing said second graphical map onto said first graphical map, wherein said second graphical map, after warping and morphing, corresponds to said retinal region.
12. The apparatus of claim 11 , wherein
said first retinal characterization dataset is a fundus image; and
said second retinal characterization dataset is a retinal parameter dataset.
13. The apparatus of claim 12 , wherein
said retinal parameter dataset is calculated from a three-dimensional optical coherence tomography (3-D OCT) volume dataset registered to said fundus image.
14. The apparatus of claim 12 , wherein said retinal parameter dataset characterizes at least one of:
retinal thickness; and
retinal layer thickness.
15. The apparatus of claim 11 , wherein
said first retinal characterization dataset is a retinal parameter dataset; and
said second retinal characterization dataset is a fundus image.
16. The apparatus of claim 15 , wherein
said retinal parameter dataset is calculated from a 3-D OCT volume dataset registered to said fundus image.
17. The apparatus of claim 15 , wherein said retinal parameter dataset characterizes at least one of:
retinal thickness;
retinal layer thickness; and
surface topography.
18. The apparatus of claim 11 , wherein
said first retinal characterization dataset is a first retinal parameter dataset; and
said second retinal characterization dataset is a second retinal parameter dataset.
19. The apparatus of claim 18 , wherein:
said first retinal parameter dataset and said second retinal parameter dataset are calculated from a 3-D OCT volume dataset registered to a fundus image.
20. The apparatus of claim 18 , wherein said first retinal parameter dataset and said second retinal parameter dataset characterize at least one of:
retinal thickness; and
retinal layer thickness.
21. A non-transitory computer readable medium storing computer instructions for mapping a second retinal characterization dataset to a first retinal characterization dataset, the computer instructions defining the steps of:
displaying on a display a first graphical map based at least in part on the first retinal characterization dataset, wherein the first graphical map corresponds to a retinal region;
generating a second graphical map based at least in part on the first graphical map and on the second retinal characterization dataset; and
warping and morphing said second graphical map onto said first graphical map, wherein said second graphical map, after warping and morphing, corresponds to said retinal region.
22. The non-transitory computer readable medium of claim 21 , wherein
said first retinal characterization dataset is a fundus image; and
said second retinal characterization dataset is a retinal parameter dataset.
23. The non-transitory computer readable medium of claim 22 , wherein
said retinal parameter dataset is calculated from a three-dimensional optical coherence tomography (3-D OCT) volume dataset registered to said fundus image.
24. The non-transitory computer readable medium of claim 22 , wherein said retinal parameter dataset characterizes at least one of:
retinal thickness; and
retinal layer thickness.
25. The non-transitory computer readable medium of claim 21 , wherein
said first retinal characterization dataset is a retinal parameter dataset; and
said second retinal characterization dataset is a fundus image.
26. The non-transitory computer readable medium of claim 25 , wherein
said retinal parameter dataset is calculated from a 3-D OCT volume dataset registered to said fundus image.
27. The non-transitory computer readable medium of claim 25 , wherein said retinal parameter dataset characterizes at least one of:
retinal thickness;
retinal layer thickness; and
surface topography.
28. The non-transitory computer readable medium of claim 21 , wherein
said first retinal characterization dataset is a first retinal parameter dataset; and
said second retinal characterization dataset is a second retinal parameter dataset.
29. The non-transitory computer readable medium of claim 28 , wherein:
said first retinal parameter dataset and said second retinal parameter dataset are calculated from a 3-D OCT volume dataset registered to a fundus image.
30. The non-transitory computer readable medium of claim 28 , wherein said first retinal parameter dataset and said second retinal parameter dataset characterize at least one of:
retinal thickness; and
retinal layer thickness.