초록 ▼

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 illuminat

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 which 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.

대표청구항 ▼

1. An automated eye corneal striae recognition system, comprising:a) means for illuminating an eye cornea with light;b) means for capturing an image of the illuminated eye cornea; andc) a computer system including,(i) means for controlling said means for illuminating said eye cornea,(ii) means for r

1. An automated eye corneal striae recognition system, comprising:a) means for illuminating an eye cornea with light;b) means for capturing an image of the illuminated eye cornea; andc) a computer system including,(i) means for controlling said means for illuminating said eye cornea,(ii) means for receiving said image of the eye cornea from said means for capturing said image, and(iii) a processor means for(A) processing said image,(B) detecting a corneal stria object from the processed image if a corneal stria is present, and(C) determining a respective position of the detected corneal stria object relative to the eye cornea. 2. An automated eye corneal striae recognition system according to claim 1, wherein:said means for illuminating said eye cornea includes,(i) a housing adapted to be fastened to the lower end of a microscope, said housing having a plurality of annularly arranged spaced openings adapted to direct light away toward said eye cornea,(ii) a supporting means within said housing for supporting a source of a light beam at each of said openings,(iii) a diffuser means for diffusing each said source of a light beam, and(iv) means for controlling each said source of a light beam individually. 3. An automated eye corneal striae recognition system according to claim 2, wherein:said housing is a generally continuous ring. 4. An automated eye corneal striae recognition system according to claim 2, wherein:each said source of a light beam is a light emitting diode. 5. An automated eye corneal striae recognition system according to claim 4, wherein:said supporting means for each said light emitting diode within said housing is a printed circuit board. 6. An automated eye corneal striae recognition system according to claim 4, wherein:said means for controlling each said light emitting diode provides appropriate electrical current amperage to each said light emitting diode. 7. An automated eye corneal striae recognition system according to claim 2, wherein:each said source of a light beam is an individual light transmitting fiber optic cable conveying light from an external source. 8. An automated eye corneal striae recognition system according to claim 7, wherein:said external source is a light emitting diode coupled to said light transmitting fiber optic cable. 9. An automated eye corneal striae recognition system according to claim 8, wherein:said means for controlling each said light emitting diode provides appropriate electrical current amperage to each said light emitting diode. 10. An automated eye corneal striae recognition system according to claim 2, wherein:said light has a wavelength, and said diffuser means is a polarizer selected for said wavelength. 11. An automated eye corneal striae recognition system according to claim 1, wherein:said light is a monochromatic light. 12. An automated eye corneal striae recognition system according to claim 1, wherein:said means for receiving said image of the eye cornea is an analog video camera. 13. An automated eye corneal striae recognition system according to claim 12, wherein:said analog video camera is attached to an optical port of a microscope of a refractive laser surgery system. 14. An automated eye corneal striae recognition system according to claim 13, further comprising:a bracket adapted to fasten said analog video camera to said microscope of said refractive laser surgery system. 15. An automated eye corneal striae recognition system according to claim 14, further comprising:an imaging lens attached to said analog video camera. 16. An automated eye corneal striae recognition system according to claim 12, wherein:said image of the eye cornea is a digital image. 17. An automated eye corneal striae recognition system according to claim 16, wherein:said means for receiving said digital image of the eye cornea from said analog video camera is an analog frame grabber. 18. An automated eye corneal striae recognition system according to claim 1, wherein:said means f or receiving said image of the eye cornea is a digital video camera. 19. An automated eye corneal striae recognition system according to claim 18, further comprising:a refractive laser surgery system including a microscope having a video camera optical port, said digital video camera being coupled to said port. 20. An automated eye corneal striae recognition system according to claim 19, wherein:a bracket adapted to fasten said digital video camera to said microscope of said refractive laser surgery system. 21. An automated eye corneal striae recognition system according to claim 18, further comprising:an imaging lens attached to said digital video camera. 22. An automated eye corneal striae recognition system according to claim 18, wherein:said image is a digital image. 23. An automated eye corneal striae recognition system according to claim 22, wherein:said means for receiving said digital image of the eye cornea from said digital video camera is a digital frame grabber. 24. An automated eye corneal striae recognition system according to claim 1, wherein:said processor means determines shape characteristics of each said detected corneal stria object. 25. An automated eye corneal striae recognition system according to claim 1, wherein:said processor means detects said corneal stria by selecting a limited region-of-interest area in said image where said corneal stria object may be present. 26. An automated eye corneal striae recognition system according to claim 25, wherein:said processor means applies a corneal stria detection algorithm to said limited region-of-interest area in said image. 27. An automated eye corneal striae recognition system according to claim 26, wherein:said processor means detects said corneal stria object by further applying a means for enhancing corneal stria edges. 28. An automated eye corneal striae recognition system according to claim 27, wherein:said processor means enhances said corneal stria edges by increasing the contrast between a corneal stria object and normal corneal tissue surrounding said corneal stria in said image. 29. An automated eye corneal striae recognition system according to claim 28, wherein:said image includes a plurality of intensity values, and the contrast is increased by said processor means applying an edge detection operator to the intensity values in said image to produce a bimodal histogram of the intensity values. 30. An automated eye corneal striae recognition system according to claim 29, wherein:said processor means detects said corneal stria object by further applying a threshold function to said bimodal histogram to create a binary representation of said image. 31. An automated eye corneal striae recognition system according to claim 30, wherein:said processor means detects said corneal stria object by further applying an outer gradient operator to said binary representation. 32. An automated eye corneal striae recognition system according to claim 30, wherein:said processor means searches said binary representation for an object having a size within a size range of a set of corneal striae objects. 33. An automated eye corneal striae recognition system according to claim 30, wherein:said processor means processes said binary representation to ensure that said object, within said size range of said set of corneal striae objects, possesses corneal striae shape attributes. 34. An automated eye corneal striae recognition system according to claim 25, wherein:said processor means searches said limited region-of-interest area in said image for objects correlating highly with one of several known corneal striae object patterns. 35. An automated eye corneal striae recognition system according to claim 1, further comprising:d) display means for displaying indications of said detected corneal stria object. 36. An automated eye corneal striae recognition system according to claim 1, wherein:said processor means saves a position indication of said respective position of ea ch corneal stria object detected in said image. 37. An automated eye corneal striae recognition system according to claim 36, wherein:said processor means determines and saves shape characteristic profile information for each detected corneal striae object in said image. 38. An automated eye corneal striae recognition system according to claim 37, further comprising:d) display means for displaying indications of said detected corneal stria object,wherein said processor means uses said saved position indications and said shape characteristic profile information for each detected corneal stria object to highlight each said corneal stria object in said image on said display means. 39. An automated eye corneal striae recognition system according to claim 38, wherein:said display means is a high contrast video display, and said processor means highlights each said detected corneal stria object in said image by outlining each said detected corneal stria object with a high contrast color. 40. An automated eye corneal striae recognition system according to claim 1, further comprising:d) display means for displaying indications of said detected corneal striae object,wherein said computer system includes a means for sending a video signal, and wherein when each said corneal stria object is detected and highlighted, said means for sending said video signal sends a video signal containing each highlighted corneal stria object to said display means. 41. An automated eye corneal striae recognition system according to claim 1, further comprising:d) a laser generator for performing refractive laser surgery on the eye cornea. 42. A method for automatically detecting corneal striae, said method comprising:a) illuminating the eye cornea with light;b) obtaining an image of said illuminated eye cornea;c) processing said image;d) determining from said processed image whether one or more corneal striae objects are present; ande) if one or more corneal striae objects are present, determining the position of each said corneal stria object and providing an indication of the position of each said corneal stria object relative to the cornea. 43. A method according to claim 42, further comprising:if one or more corneal striae objects are present, determining a shape of each said corneal stria object. 44. A method according to claim 43, further comprising:providing an indication of the shape of each said corneal stria object. 45. A method according to claim 42, wherein:said image is processed digitally. 46. A method according to claim 42, wherein:said light is monochromatic light. 47. A method according to claim 42, wherein:said light has a wavelength between 840 to 930 nm. 48. A method according to claim 42, wherein:said processing includes(i) defining a limited region-of-interest in said image for detecting the eye corneal striae objects,(ii) processing image data from the limited region-of-interest by an edge detection operator 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, and(iv) searching the binary image for objects having corneal striae size and shape characteristics. 49. A method according to claim 48, further comprising:(v) after applying the threshold function and before providing a characterization process, processing said binary representation by an outer gradient operator. 50. A method for automatically detecting corneal striae, said method comprising:a) illuminating the cornea with a light beam directed toward the cornea at a first angle;b) obtaining an image of the cornea;c) processing said image;d) determining from said processed image whether one or more corneal striae objects are present; ande) repeating steps a)-d) with light beams directed toward the cornea at distinct angles. 51. A method according to claim 50, further comprising:f) if one or more corneal striae objects are present, determining the position of each said corneal striae object and providing an indication of the position of each said corneal striae object relative to the cornea. 52. A method according to claim 50, wherein:each said light beam is monochromatic. 53. A method according to claim 52, wherein:each said light beam has a wavelength between 840 to 930 nm. 54. A method according to claim 50, wherein:each said light beam is created by a light emitting diode. 55. A method according to claim 50, wherein:each said light beam is a circular beam, and said beams are projected onto the cornea in a pattern in which each said light beam overlaps another said light beam. 56. A method according to claim 55, wherein:said pattern is circular.