Technique for manipulating medical images

• US 6,799,066 B2
• Filed: 09/14/2001
• Issued: 09/28/2004
• Est. Priority Date: 09/14/2000
• Status: Expired due to Term

• Current Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford University)
• Original Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford University)
• Inventors: Lang, Philipp, Steines, Daniel
• Primary Examiner(s): Smith, Ruth S.
• Application Number:
  US09/953,531
• Publication Number:
  US 20020147392A1
• Time in Patent Office: 1,110 Days
• Field of Search: 600/407, 600/410, 382/128, 382/190, 382/199
• US Class Current: 600/407
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/4514 Cartilage
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: G01R 33/50 based on the determination of relaxation times , e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences
• CPC Class Codes: G01R 33/56 Image enhancement or correction, e.g. subtraction or averaging techniques , e.g. improvement of signal-to-noise ratio and resolution
• CPC Class Codes: G01R 33/5608 Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels image data processing or generation, in general G06T
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30008 Bone
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation

Abstract

The invention and the embodiments described in this invention provide new techniques for manipulating digital images and is particularly useful for extracting tissues (i.e., assigning tissue boundary locations) from medical images. These techniques can be applied to diagnosing arthritis and for monitoring disease progression or response to therapeutic intervention. The invention provides for means to extract the articular cartilage from medical images for analysis purposes.

Claims

1. A method of assessing a digital image to determine a boundary between first and second image components, which comprises:

(a) selecting a first boundary element between two pixels being evaluated for assignment to the first or second image component;

(b) determining a first value of a first pixel on one side of the first boundary element;

(c) determining a second value of a second pixel on the other side of the first boundary element;

(d) comparing the first and second value to determine magnitude of a gradient across the first boundary element and assigning a gradient boundary value to the first boundary element;

(e) determining relative locations of (1) the first boundary element and (2) a reference element selected from the group consisting of (2a) a reference boundary element previously assigned as separating a pixel assigned to the first image component from a pixel assigned to the second image component and (2b) a reference second-boundary element previously assigned as separating a pixel assigned to one of the first and second image components from a third image component, the second-boundary element having a known, statistically significant distance or angle relationship relative to an actual boundary between real components represented by the first and second image components, and assigning a location boundary value to the first boundary element based on distance or angle of the relative locations from each other;

(f) mathematically combining the gradient boundary value and the location boundary value to provide a first potential boundary value for the first boundary element;

repeating steps (a)-(f) for a second boundary element to obtain a second potential boundary value; and

(g) assigning the first boundary element or the second boundary element as dividing the first image component from the second image component, depending on relative potential boundary values of the first and second boundary elements, wherein said method is used to locate a bone/cartilage interface or an external cartilage surface.

2. The method of claim 1, wherein said image is a magnetic resonance image, an ultrasound image, or an X-ray image.

3. The method of claim 2, wherein said cartilage is diseased or damaged.

4. The method of claim 1, wherein said method assigns said boundary element to said first or second image component using existing knowledge of probable shape of said first or second image component.

5. The method of claim 4, wherein said digital image is of a living subject and said first and second image components comprise first and second tissues of said living subject.

6. The method of claim 5, wherein said knowledge is obtained from (a) information derived from same-type tissue images of other subjects or (b) information derived from a different image obtained from said living subject or from a different region of said tissue image.

7. The method of claim 6, wherein said other subjects are age and sex matched or weight matched with said living subject.

8. The method of claim 6, wherein said information is (a) cartilage thickness, cartilage curvature or cartilage shape in a contralateral joint or in a different region of a first joint whose image is being assessed or (b) curvature or shape of a bone in said first joint.

9. The method of claim 8, wherein said bone is subchondral bone.

10. The method of claim 9, wherein curvature or shape of the subchondral bone is used to estimate curvature or shape of overlying articular cartilage.

11. The method of claim 1, wherein said method is used to assess condition of cartilage in a joint of a human, which method further comprises:

(a) electronically transferring an electronically generated image of a cartilage of the joint from a transferring device to a receiving device located distant from the transferring device;

(b) receiving the transferred image at the distant location;

(c) extracting said cartilage from said transferred image using the method of claim 1;

(d) converting the transferred image to a normal or degeneration pattern of the cartilage; and

(e) transmitting said normal or said degeneration pattern to a site for analysis.

12. The method of claim 11, wherein said normal or said degeneration pattern is stored in a database.

13. The method of claim 1, wherein all steps are carried out at an initial time T1 and are carried out again at a later time T2.

14. The method of claim 13, wherein the assessment includes an analysis of the degree of degeneration of the cartilage between T1 and T2.

15. The method of claim 1, wherein said digital image of a cartilage is obtained by a magnetic resonance imaging (MRI) technique.