Systems and methods for automated enhancement of retinal images

• US 8,885,901 B1
• Filed: 04/30/2014
• Issued: 11/11/2014
• Est. Priority Date: 10/22/2013
• Status: Active Grant

• Current Assignee: Eyenuk, Inc.
• Original Assignee: Eyenuk, Inc.
• Inventors: Solanki, Kaushal Mohanlal, Amai Ramachandra, Chaithanya, Bhat Krupakar, Sandeep
• Primary Examiner(s): Chen, Wenpeng
• Application Number:
  US14/266,688
• Time in Patent Office: 195 Days
• Field of Search: None
• US Class Current: 382/128
• CPC Class Codes: A61B 3/0025 characterised by electronic signal processing, e.g. eye models
• CPC Class Codes: A61B 3/12 for looking at the eye fundus, e.g. ophthalmoscopes A61B3/13 takes precedence
• CPC Class Codes: A61B 3/14 Arrangements specially adapted for eye photography apparatus or arrangements for taking photographs per se G03B
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/583 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/5866 using information manually generated, e.g. tags, keywords, comments, manually generated location and time information
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/20032 Median filtering
• CPC Class Codes: G06T 2207/20036 Morphological image processing
• CPC Class Codes: G06T 2207/30041 Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 2207/30104 Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06T 3/14 Transformations for image registration, e.g. adjusting or mapping for alignment of images
• CPC Class Codes: G06T 3/18 Image warping, e.g. rearranging pixels individually
• CPC Class Codes: G06T 3/40 Scaling of whole images or parts thereof, e.g. expanding or contracting
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/94 based on local image properties, e.g. for local contrast enhancement
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/0014 : using an image reference approach
• CPC Class Codes: G06T 7/0016 : involving temporal comparison
• CPC Class Codes: G06V 10/267 : by performing operations on regions, e.g. growing, shrinking or watersheds
• CPC Class Codes: G06V 10/44 : Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
• CPC Class Codes: G06V 10/50 : by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/758 : Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 2201/03 : Recognition of patterns in medical or anatomical images
• CPC Class Codes: G06V 40/14 : Vascular patterns
• CPC Class Codes: G06V 40/18 : Eye characteristics, e.g. of the iris
• CPC Class Codes: G06V 40/193 : Preprocessing; Feature extraction
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 : for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

Claims

1. A computing system for enhancing a retinal image, the computing system comprising:

one or more hardware computer processors; and

one or more storage devices configured to store software instructions configured for execution by the one or more hardware computer processors in order to cause the computing system to;

access a medical retinal image for enhancement, the medical retinal image related to a subject;

compute a median filtered image I_Back,S with a median computed over a geometric shape, at single or multiple scales;

determine whether intensity at a first pixel location in the medical retinal image I(x,y) is lower than intensity at a same position in the median filtered image I_Back,S(x,y) for generating an enhanced image;

if the intensity at the first pixel location is lower, then set a value at the first pixel location in the enhanced image to a value around a middle of a minimum and a maximum intensity value for the medical retinal image C_mid scaled by a ratio of intensity at medical retinal image to intensity in the median filtered image as expressed by

2. The computing system of claim 1, wherein the computing system is further configured to automatically identify one or more abnormalities in the enhanced image.

3. The computing system of claim 1, wherein the computing system is further configured to automatically identify one or more anatomical structures in the enhanced image.

4. The computing system of claim 1, wherein the computing system is further configured to automatically analyze a medical condition of the subject.

5. The computing system of claim 1, wherein the geometric shape is one or more of:

a circle, a square, or a regular polygon.

6. The computing system of claim 1, wherein the geometric shape is a circle with a pre-defined radius.

7. The computing system of claim 1, wherein the geometric shape is a square with a pre-defined size.

8. The computing system of claim 1, wherein the computing system is further configured to automatically perform at least one of image registration, lesion localization, screening, quality assessment, interest region detection, or descriptor computation.

9. A computer-implemented method for enhancing a retinal image, the method comprising:

as implemented by one or more computing devices configured with specific executable instructions;

accessing a medical retinal image for enhancement, the medical retinal image related to a subject;

computing a median filtered image I_Back,S with a median computed over a geometric shape, at single or multiple scales;

determining whether intensity at a first pixel location in the medical retinal image I(x,y) is lower than intensity at a same position in the median filtered image I_Back,S(x,y) for generating an enhanced image;

if the intensity at the first pixel location is lower, then setting a value at the first pixel location in the enhanced image to a value around a middle of a minimum and a maximum intensity value for the medical retinal image C_mid scaled by a ratio of intensity at medical retinal image to intensity in the median filtered image as expressed by

10. The computer implemented method of claim 9, further comprising:

automatically identifying one or more abnormalities in the enhanced image.

11. The computer implemented method of claim 9, further comprising:

automatically identifying one or more anatomical structures in the enhanced image.

12. The computer implemented method of claim 9, further comprising:

automatically analyzing a medical condition of the subject.

13. The computer implemented method of claim 9, wherein the geometric shape is one or more of:

a circle, a square, or a regular polygon.

14. The computer implemented method of claim 9, wherein the geometric shape is a circle with a pre-defined radius.

15. The computer implemented method of claim 9, wherein the geometric shape is a square with a pre-defined size.

16. The computer implemented method of claim 9, further comprising:

automatically performing at least one of image registration, lesion localization, screening, quality assessment, interest region detection, or descriptor computation.

17. Non-transitory computer storage that stores executable program instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising:

accessing a medical retinal image for enhancement, the medical retinal image related to a subject;

computing a median filtered image I_Back,S with a median computed over a geometric shape, at single or multiple scales;

determining whether intensity at a first pixel location in the medical retinal image I(x,y) is lower than intensity at a same position in the median filtered image I_Back,S(x,y) for generating an enhanced image;

if the intensity at the first pixel location is lower, then setting a value at the first pixel location in the enhanced image to a value around a middle of a minimum and a maximum intensity value for the medical retinal image C_mid scaled by a ratio of intensity at medical retinal image to intensity in the median filtered image as expressed by

18. The non-transitory computer storage of claim 17, further comprising:

automatically identifying one or more abnormalities in the enhanced image.

19. The non-transitory computer storage of claim 17, further comprising:

automatically identifying one or more anatomical structures in the enhanced image.

20. The non-transitory computer storage of claim 17, further comprising:

automatically analyzing a medical condition of the subject.

21. The non-transitory computer storage of claim 17, wherein the geometric shape is one or more of:

a circle, a square, or a regular polygon.

22. The non-transitory computer storage of claim 17, wherein the geometric shape is a circle with a pre-defined radius.

23. The non-transitory computer storage of claim 17, wherein the geometric shape is a square with a pre-defined size.

24. The non-transitory computer storage of claim 17, further comprising:

automatically performing at least one of image registration, lesion localization, screening, quality assessment, interest region detection, or descriptor computation.