IMAGE PROCESSING METHOD AND SYSTEM FOR EDGE DETECTION AND LASER EYE SURGERY SYSTEM INCORPORATING THE SAME

• US 20170007112A1
• Filed: 12/15/2015
• Published: 01/12/2017
• Est. Priority Date: 07/08/2015
• Status: Active Grant

• Current Assignee: AMO Development LLC (Johnson & Johnson)
• Original Assignee: OptiMedica Corporation (Johnson & Johnson)
• Inventors: Gonzalez, Javier
• Granted Patent: US 10,548,470 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 3/0025 characterised by electronic signal processing, e.g. eye models
• CPC Class Codes: A61B 3/0033 characterised by user input arrangements
• CPC Class Codes: A61B 3/0041 characterised by display arrangements
• CPC Class Codes: A61B 3/1005 for measuring distances inside the eye, e.g. thickness of the cornea A61B3/11 takes precedence
• CPC Class Codes: A61B 3/102 for optical coherence tomography [OCT]
• CPC Class Codes: A61B 3/1025 for confocal scanning
• CPC Class Codes: A61B 3/14 Arrangements specially adapted for eye photography apparatus or arrangements for taking photographs per se G03B
• CPC Class Codes: A61F 2009/00846 Eyetracking
• CPC Class Codes: A61F 2009/00851 Optical coherence topography [OCT]
• CPC Class Codes: A61F 2009/00889 Capsulotomy
• CPC Class Codes: A61F 9/0084 Laser features or special beam parameters therefor
• CPC Class Codes: G06T 2207/10101 Optical tomography; Optical coherence tomography [OCT]
• CPC Class Codes: G06T 2207/30041 Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/162 involving graph-based methods

Abstract

A method of imaging an object includes obtaining an image data set from a raster scan. The image data set has a plurality of data points, each data point having an associated location and intensity; generating a reduced data set by selectively removing one or more data points from the image data set based upon an assigned probability of retaining the one or more data points in the data set, the assigned probability being a function of the intensity of a data point; generating a triangulation graph as a planar subdivision having faces that are triangles, the vertices of which are the data points and the edges of which are adjacent vertices; and segmenting the triangulated data set by finding a path with lowest cost between that vertex and every other vertex, wherein the cost is a function of the respective intensity of the vertices.

Claims

1. A method of imaging an object comprising:

obtaining an image data set from a raster scan of the object, the image data set comprising a plurality of data points, each data point having a location and intensity associated with it;

generating a reduced data set by selectively removing one or more data points from the image data set based upon an assigned probability of retaining the one or more data points in the data set, the assigned probability being a function of the intensity of a data point;

generating a triangulation graph of the reduced data as a planar subdivision having faces that are triangles, the vertices of which are the data points of the reduced data set and the edges of which are adjacent vertices; and

segmenting the triangulation graph by finding a path with lowest cost between vertices of the triangulation graph, wherein the cost between vertices is a function of the respective intensity of the vertices.

2. The method of claim 1, wherein the object is a human eye.

3. The method of claim 1, wherein the raster scan is conducted by scanning a pulsed laser along the object to be imaged and detecting backreflected light from the object.

4. The method of claim 1, wherein, prior to the selective removing step, reducing the image data set by a reduction factor.

5. The method of claim 1, further comprising truncating at least one of the image data set and the reduced data set by removing data outside nominal biologic limits from the selected data set.

6. The method of claim 1, wherein the planar subdivision is a maximal planar subdivision.

7. The method of claim 6, wherein the triangulation graph is a Delaunay graph.

8. The method of claim 1, wherein the cost associated between a first vertex having an intensity I1 and a second vertex having an intensity I2 is given by the formula

9. The method of claim 1, wherein the lowest cost path is found using a Dijkstra algorithm.

10. The method of claim 1, wherein the segmented data set is displayed on a graphical user interface.

11. A system for imaging an object comprising:

memory for storing a plurality of instructions; and

a processor for executing the instructions to perform a plurality of steps, the instructions comprising;

obtaining an image data set from a raster scan of the object, the image data set comprising a plurality of data points, each data point having a location and intensity associated with it;

generating a reduced data set by selectively removing one or more data points from the image data set based upon an assigned probability of retaining the one or more data points in the data set, the assigned probability being a function of the intensity of a data point;

generating a triangulation graph of the reduced data as a planar subdivision having faces that are triangles, the vertices of which are the data points of the reduced data set and the edges of which are adjacent vertices; and

segmenting the triangulation graph by finding a path with lowest cost between that vertex and every other vertex, wherein the cost is a function of the respective intensity of the vertices.

12. The system of claim 11, wherein the object is a human eye.

13. The system of claim 11, wherein the instructions include instructions for reducing the image data set by a reduction factor prior to the selective removing the one or more data points.

14. The system of claim 11, wherein the instruction include instructions for truncating at least one of the image data set and the reduced data set by removing data outside nominal biologic limits from the selected data set.

15. The system of claim 11, wherein the planar subdivision is a maximal planar subdivision.

16. The system of claim 11, wherein the triangulation graph is a Delaunay graph.

17. The system of claim 11, wherein a cost associated with a first vertex having an intensity I₁ and a second vertex having an intensity I₂ is given by the formula

18. The system of claim 11, wherein the instructions comprise instructions for finding the lowest cost path using a Dijkstra algorithm.

19. The system of claim 11, wherein the instructions include instructions for displaying the segmented data on a graphical user interface.

20. A laser eye surgical system comprising the system of claim 11.

21. A laser surgical system for imaging an object, comprising:

a laser source for generating a pulsed laser beam;

an imaging system comprising a detector;

shared optics configured for directing the pulsed laser beam to an object to be sampled and confocally deflecting back-reflected light from the object to the detector;

a controller operatively coupled to the laser source, the imaging system and the shared optics, the controller configured to;

(a) scan the pulsed laser beam in a raster scan along the object to be imaged;

(b) collect an image data set corresponding to the intensity of the back-reflected light from each of the laser pulses, the image data set comprising a plurality of data points, each data point having a location and intensity associated with it;

(c) generate a reduced data set by selectively removing one or more data points from the image data set based upon an assigned probability of retaining the one or more data points in the data set, the assigned probability being a function of the intensity of a data point;

(d) generate a triangulation graph of the reduced data as a planar subdivision having faces that are triangles, the vertices of which are the data points of the reduced data set and the edges of are adjacent vertices; and

(e) segment the triangulation graph by finding a path with lowest cost between that vertex and every other vertex, wherein the cost is a function of the respective Intensity of the vertices.

22. The system of claim 21, wherein the object is a human eye.

23. The system of claim 21, wherein the controller is configured to reduce the image data by a reduction factor prior to the selective removal.

24. The system of claim 21, wherein the controller is configured to truncate at least one of the image data set and the reduced data set by removing data outside nominal biologic limits from the selected data set.

25. The system of claim 21, wherein the planar subdivision is a maximal planar subdivision.

26. The system of claim 21, wherein the triangulation graph is a Delaunay graph.

27. The system of claim 21, wherein a cost associated with a first vertex having an intensity I₁ and a second vertex having an intensity I₂ is given by the formula

28. The system of claim 21, wherein the controller is configured to find the lowest cost using a Dijkstra algorithm.

29. The system of claim 21, wherein the controller is configured to display the segmented data on a graphical user interface.

30. A computer-readable, non-transitory medium comprising a computer program which causes a computer to execute a process comprising:

obtaining an image data set from a raster scan of the object, the image data set comprising a plurality of data points, each data point having a location and intensity associated with it;

generating a reduced data set by selectively removing one or more data points from the image data set based upon an assigned probability of retaining the one or more data points in the data set, the assigned probability being a function of the intensity of a data point;

generating a triangulation graph of the reduced data as a planar subdivision having faces that are triangles, the vertices of which are the data points of the reduced data set and the edges of which are adjacent vertices; and

segmenting the triangulation graph by finding a path with lowest cost between vertices of the triangulation graph, wherein the cost between vertices is a function of the respective intensity of the vertices.