OBJECTIVE QUALITY METRIC FOR OCULAR WAVEFRONT MEASUREMENTS

• US 20110007270A1
• Filed: 07/02/2010
• Published: 01/13/2011
• Est. Priority Date: 07/06/2009
• Status: Active Grant

• Current Assignee: Alcon Incorporated
• Original Assignee: WaveTec Vision Systems, Inc. (Alcon Incorporated)
• Inventors: Hall, Max, Sarver, Edwin Jay, Padrick, Thomas D.
• Granted Patent: US 8,876,290 B2
• Time in Patent Office: Days
• US Class Current: 351/206
• CPC Class Codes: A61B 3/0025 characterised by electronic signal processing, e.g. eye models
• CPC Class Codes: A61B 3/1015 for wavefront analysis

Abstract

A system and method for determining an objective quality metric for image data collected by a wavefront aberrometer. The method may include quantifying a plurality of characteristics of the image data and calculating the objective quality metric based on the quantified characteristics of the image data. The objective quality metric can be a weighted sum of the quantified characteristics of the image data. The weightings for the weighted sum can be determined based on subjective quality metrics assigned to a set of training image data by a human expert.

Claims

1. In a wavefront aberrometer system, a method comprising:

obtaining one or more images that are indicative of a patient'"'"'s ocular aberrations;

determining a plurality of characteristics of the one or more images;

determining one or more objective quality metrics for the one or more images, the one or more objective quality metrics being based on an aggregate of the plurality of characteristics of the one or more images;

selecting at least a portion of the one or more images based on the one or more objective quality metrics; and

determining one or more ocular aberration measurements using the selected portion of the one or more images.

2. The method of claim 1, further comprising determining a objective quality metric for each of the one or more images.

3. The method of claim 1, further comprising:

selecting a single image based on one of the one or more objective quality metrics; and

determining the one or more ocular aberration measurements based on the single selected image.

4. The method of claim 1, wherein the one or more objective quality metrics are calculated using one or more parameters determined from a plurality of training images representative of ocular aberrations.

5. The method of claim 4, wherein the one or more parameters are determined based on subjective quality metrics assigned to the plurality of training images by a human expert.

6. The method of claim 1, wherein determining one or more characteristics of each of the one or more images comprises analyzing a spatial-domain representation of each of the plurality of images.

7. The method of claim 1, wherein determining one or more characteristics of each of the one or more images comprises analyzing a frequency-domain representation of each of the plurality of images.

8. The method of claim 1, wherein the one or more objective quality metrics are quantitative.

9. The method of claim 8, wherein the one or more objective quality metrics are each a scalar value.

10. The method of claim 1, wherein each of the plurality of characteristics is quantified as the output of one or more mathematical functions that each receives one or more of the images as an input.

11. The method of claim 10, wherein each of the one or more objective quality metrics comprises a combination of the outputs of the one or more mathematical functions.

12. The method of claim 11, wherein each of the one or more objective quality metrics comprises a weighted sum of the outputs of the one or more mathematical functions.

13. The method of claim 12, wherein the weightings of the weighted sum are selected based on subjective quality metrics assessed to a plurality of test images by a human expert.

14. The method of claim 13, wherein the weightings of the weighted sum are selected based on the error between one or more objective quality metrics calculated for the plurality of test images and the corresponding one or more subjective quality metrics assessed by the human expert.

15. The method of claim 14, wherein the weightings are selected using a simulated annealing process.

16. The method of claim 1, wherein one of the plurality of characteristics comprises the degree of centration of a feature in each of the one or more images.

17. The method of claim 1, wherein one of the plurality of characteristics comprises the variation in an ocular aberration measurement determined based on each of the one or more images.

18. The method of claim 1, wherein one of the plurality of characteristics comprises the width of a spectral peak identified in a frequency-domain representation of each of the one or more images.

19. The method of claim 18, wherein one of the plurality of characteristics comprises the variation in width of the spectral peak when taken in more than one direction.

20. The method of claim 1, wherein one of the plurality of characteristics comprises the image contrast of a feature of each of the one or more images.

21. The method of claim 20, wherein one of the plurality of characteristics comprises a representative image contrast value determined from a sub-portion of the feature.

22. The method of claim 1, wherein at least one of the one or more images comprises a fringe pattern.

23. The method of claim 22, wherein at least one of the one or more images comprises a Moiré

fringe pattern.

24. The method of claim 23, wherein the Moiré

fringe pattern comprises a Talbot-Moiré

fringe pattern.

25. The method of claim 23, wherein the Moiré

fringe pattern comprises a Hartmann-Moiré

fringe pattern.

26. The method of claim 1, wherein at least one of the one or more images comprises a Shack-Hartmann spot pattern.

27. The method of claim 1, wherein the one or more ocular aberration measurements comprises the spherical power, the cylindrical power, or the cylindrical axis of the eye.

28. The method of claim 27, further comprising outputting the one or more ocular aberration measurements for use in selecting an intraocular lens to be implanted in the patient'"'"'s eye during cataract surgery, or for positioning an intraocular lens during cataract surgery.

29. An ophthalmic device, comprising:

a wavefront aberrometer; and

a processing module communicatively coupled to the wavefront aberrometer, the processing module being programmed to perform a method comprising,

obtaining one or more images that are indicative of a patient'"'"'s ocular aberrations;

determining a plurality of characteristics of the one or more images;

determining one or more objective quality metrics for the one or more images, the one or more objective quality metrics being based on an aggregate of the plurality of characteristics of the one or more images;

selecting at least a portion of the one or more images based on the one or more objective quality metrics; and

determining one or more ocular aberration measurements using the selected portion of the one or more images.

30. A computer readable medium with instructions that, when read by a computer, cause the computer to perform a method comprising:

obtaining one or more images that are indicative of a patient'"'"'s ocular aberrations;

determining a plurality of characteristics of the one or more images;

determining one or more objective quality metrics for the one or more images, the one or more objective quality metrics being based on an aggregate of the plurality of characteristics of the one or more images;

selecting at least a portion of the one or more images based on the one or more objective quality metrics; and

determining one or more ocular aberration measurements using the selected portion of the one or more images.