System for automatically detecting eye corneal striae using projected and reflected shapes

• US 20020159621A1
• Filed: 03/06/2002
• Published: 10/31/2002
• Est. Priority Date: 04/26/2001
• Status: Abandoned Application

• Current Assignee: Memphis Eye & Cataract Associates Ambulatory Surgery Center
• Original Assignee: Memphis Eye & Cataract Associates Ambulatory Surgery Center
• Inventors: Williams, Roy E., Callies, Brian M., Freeman, James F.
• Application Number:
  US10/091,621
• Publication Number:
  US 20020159621A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 3/13 Ophthalmic microscopes
• CPC Class Codes: A61F 2009/00853 Laser thermal keratoplasty or radial keratotomy
• CPC Class Codes: A61F 2009/00872 Cornea
• CPC Class Codes: A61F 9/008 using laser
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

An automated eye corneal striae detection system for use with a refractive laser system includes a cornea illuminator, a video camera interface, a computer, and a video display for showing possible eye corneal striae to the surgeon. The computer includes an interface to control the corneal illuminator, a video frame grabber that extracts images of the eye cornea from the video camera, and is programmed to detect and recognize eye corneal striae. The striae detection algorithm finds possible cornea striae, determines their location, or position, on the cornea and analyzes their shape. After all possible eye corneal striae are detected and analyzed, they are displayed for the surgeon on an external video display. The surgeon can then make a determination as to whether the corneal LASIK flap should be refloated, adjusted or smoothed again.

Claims

1. An automated eye corneal striae recognition system, comprising:

a) means for projecting an illuminating shape on an eye cornea;

b) means for moving the projected illuminating shape and the eye cornea relative to each other;

c) means for capturing a plurality of images of the illuminated eye cornea, each of said images corresponding to different positions of said shape relative to the eye; and

d) a computer system including,

(i) means for controlling said means for scanning the eye cornea,

(ii) means for receiving said images of the eye cornea from said means for capturing said plurality of said images, and

(iii) a processor means for

(A) processing said image,

(B) detecting corneal striae objects from the processed images if corneal striae are present, and

(C) determining respective positions of the detected corneal striae objects.

2. An automated eye corneal striae recognition system according to claim 1, further comprising:

e) means for indicating to a medical practitioner the respective positions of the detected corneal striae objects relative to the eye cornea.

3. An automated eye corneal striae recognition system according to claim 1, wherein:

said means for moving the projected illuminating shape and the eye cornea relative to each other scans said illuminating shape and said cornea relative to each other.

4. An automated eye corneal striae recognition system according to claim 1, wherein:

said means for moving the projected illuminating shapes and the eye cornea relative to each other rotates said illuminating shape relative to the cornea.

5. An automated eye corneal striae recognition system according to claim 4, wherein:

said illuminating shape comprises at least one line.

6. An automated eye corneal striae recognition system according to claim 4, wherein:

said illuminating shape comprises crosshairs.

7. An automated eye corneal striae recognition system according to claim 4, wherein:

said illuminating shape is non-linear.

8. An automated eye corneal striae recognition system according to claim 1, wherein:

said means for projecting includes a mask element adapted to optically define said shape.

9. An automated eye corneal striae recognition system according to claim 1, wherein:

said means for projecting includes a laser diode and one or more optical shaping elements.

10. An automated eye corneal striae recognition system according to claim 1, further comprising:

a microscope focused on the cornea, said microscope including an optical port, wherein said means for projecting includes shape generating elements coupled to said optical port.

11. An automated eye corneal striae recognition system according to claim 10, wherein:

said shape generating elements include a line generating laser diode.

12. An automated eye corneal striae recognition system according to claim 10, wherein:

said shape generating elements include an illumination source and a mask.

13. An automated eye corneal striae recognition system according to claim 12, wherein:

wherein said mask is a slit mask.

14. An automated eye corneal striae recognition system according to claim 1, wherein:

said light is monochromatic.

15. An automated eye corneal striae recognition system according to claim 1, further comprising:

e) a laser generator for performing refractive laser surgery on the eye cornea.

17. A method according to claim 16, wherein:

said predetermined shape is comprises at least one line.

18. A method according to claim 16, wherein:

said predetermined shape comprises crosshairs.

19. A method according to claim 16, wherein:

said predetermined shape is non-linear.

20. A method according to claim 16, further comprising:

e) moving said light in said predetermined shape and the cornea relative to each other; and

f) repeating steps (b)-(d).

21. A method according to claim 20, wherein:

said moving comprises scanning said light in said predetermined shape and said cornea relative to each other.

22. A method according to claim 20, wherein:

said moving comprises rotating said light in said predetermined shape relative to the cornea.

23. A method according to claim 16, wherein:

wherein said processing includes

(i) defining a limited region-of-interest in said image for detecting the corneal striae objects,

(ii) processing image data from the limited region-of-interest by shape characteristic information such that a bimodal image is produced,

(iii) applying a threshold function to said bimodal image such that a binary representation of said image is created,

(iv) searching the binary representation image for shapes having dimensions substantially similar to said predetermined shapes, and

(v) subtracting ideal shapes having predefined dimensions from said shapes located in the binary representation image such that possible corneal striae objects are identified.

16. A method for automatically detecting corneal striae, said method including:

a) projecting light in a predetermined shape onto a cornea, said light adapted to be reflected by the cornea;

b) obtaining an image of a cornea location illuminated by said light;

c) processing said image; and

d) determining from said processed image whether one or more corneal striae objects are present.