Method and system for real-time automatic abnormality detection for in vivo images

• US 20050075537A1
• Filed: 10/06/2003
• Published: 04/07/2005
• Est. Priority Date: 10/06/2003
• Status: Abandoned Application

• Current Assignee: CareStream Health Incorporated
• Original Assignee: Eastman Kodak Company
• Inventors: Chen, Shoupu, Ray, Lawrence A., Cahill, Nathan D., Goodgame, Marvin M.
• Application Number:
  US10/679,711
• Publication Number:
  US 20050075537A1
• Time in Patent Office: Days
• US Class Current: 600/109
• CPC Class Codes: A61B 1/000094 extracting biological structures
• CPC Class Codes: A61B 1/041 Capsule endoscopes for imaging
• CPC Class Codes: A61B 1/273 for the upper alimentary canal, e.g. oesophagoscopes, gastroscopes
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/073 Intestinal transmitters
• CPC Class Codes: A61B 5/14539 for measuring pH
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10068 Endoscopic image
• CPC Class Codes: G06T 2207/20032 Median filtering
• CPC Class Codes: G06T 2207/30028 Colon; Small intestine
• CPC Class Codes: G06T 5/002 Denoising; Smoothing noise processing or correction adapted to be used in an image pickup device containing and electronic image sensor H04N23/81, H04N25/60 - H04N25/67
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06T 7/60 Analysis of geometric attributes

Abstract

A digital image processing method for real-time automatic abnormality detection of in vivo images, comprising the steps of: acquiring images using an in vivo video camera system; forming an in vivo video camera system examination bundlette; transmitting the examination bundlette to proximal in vitro computing device(s); processing the transmitted examination bundlette; automatically identifying abnormalities in the transmitted examination bundlette; and setting off alarming signals to a local site provided that suspected abnormalities have been identified.

Claims

1. A digital image processing method for real-time automatic abnormality detection of in vivo images, comprising the steps of:

a) forming an examination bundlette of a patient that includes real-time captured in vivo images;

b) processing the examination bundlette;

c) automatically detecting one or more abnormalities in the examination bundlette based on predetermined criteria for the patient; and

d) signaling an alarm provided that the one or more abnormalities in the examination bundlette have been detected.

2. The method claimed in claim 1, wherein the step of forming the examination bundlette, includes the steps of:

a1) forming an image packet of the real-time captured in vivo images of the patient;

a2) forming patient metadata; and

a3) combining the image packet and the patient metadata into the examination bundlette.

3. The method claimed in claim 1, wherein the step of processing the examination bundlette, includes the steps of:

b1) separating the in vivo images from the examination bundlette; and

b2) processing the in vivo images according to selected image processing methods.

4. The method claimed in claim 3, wherein the selected image processing methods include color space conversion and/or noise filtering.

5. The method claimed in claim 4, wherein the color space conversion converts the in vivo images from RGB space to generalized RGB space.

6. The method claimed in claim 1, wherein the step of automatically detecting the one or more abnormalities in the examination bundlette includes the steps of:

c1) detecting parameters that exceed a given threshold of physical data as identified in the in vivo images.

7. The method claimed in claim 1, wherein the step of automatically detecting the one or more abnormalities includes the steps of:

c1) detecting parameters that are substantially different from a given geometric template of physical data as identified in the in vivo images.

8. The method claimed in claim 6, wherein the given threshold is based on statistical data according to the predetermined criteria.

9. The method claimed in claim 7, wherein the geometric template is formed by training a template according to the predetermined criteria.

10. The method claimed in claim 1, wherein the step of signaling the alarm includes the steps of:

d1) providing a communication channel to a remote site; and

d2) sending the alarm to the remote site.

11. The method claimed in claim 1, wherein the step of signaling the alarm includes the steps of:

d1) providing a communication channel to a local site; and

d2) sending the alarm to the local site.

12. A digital image processing system for real-time automatic abnormality detection of in vivo images, comprising:

a) means for forming an examination bundlette of a patient that includes real-time captured in vivo images;

b) means for processing the examination bundlette;

c) means for automatically detecting one or more abnormalities in the examination bundlette based on predetermined criteria for the patient; and

d) means for signaling an alarm provided that the one or more abnormalities in the examination bundlette have been detected.

13. The system claimed in claim 12, wherein the means for forming the examination bundlette, further comprises:

a1) means for forming an image packet of the real-time captured in vivo images of the patient;

a2) means for forming patient metadata; and

a3) means for combining the image packet and the patient metadata into the examination bundlette.

14. The system claimed in claim 12, wherein the means for processing the examination bundlette, further comprises:

b1) means for separating the in vivo images from the examination bundlette; and

b2) means for processing the in vivo images according to selected image processing methods.

15. The system claimed in claim 14, wherein the selected image processing methods include color space conversion and/or noise filtering.

16. The system claimed in claim 15, wherein the color space conversion converts the in vivo images from RGB space to generalized RGB space.

17. The system claimed in claim 12, wherein the means for automatically detecting abnormalities further comprises:

c1) means for detecting parameters that exceed a given threshold of physical data as identified in the in vivo images.

18. The system claimed in claim 12, wherein the means for automatically detecting abnormalities further comprises:

c1) means for detecting parameters that are substantially different from a given geometric template of physical data as identified in the in vivo images.

19. The system claimed in claim 17, wherein the given threshold is based on statistical data according to the predetermined criteria.

20. The system claimed in claim 18, wherein the geometric template is formed by training a template according to the predetermined criteria.

21. The system claimed in claim 12, wherein the means for signaling the alarm further comprises:

d1) means for providing a communication channel to a remote site; and

d2) means for sending the alarm to the remote site.

22. The system claimed in claim 12, wherein the means for signaling the alarm further comprises:

d1) means for providing a communication channel to a local site; and

d2) means for sending the alarm to the local site.

23. An in vivo camera for employing real-time automatic abnormality detection of in vivo images, comprising:

a) means for forming an examination bundlette of a patient that includes real-time captured in vivo images;

b) means for processing the examination bundlette;

c) means for automatically detecting one or more abnormalities in the examination bundlette based on predetermined criteria for the patient; and

d) means for signaling an alarm provided that the one or more abnormalities in the examination bundlette have been detected.