SEGMENTATION OF TISSUE IMAGES USING COLOR AND TEXTURE

• US 20090003691A1
• Filed: 06/28/2007
• Published: 01/01/2009
• Est. Priority Date: 06/28/2007
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Datar, Manasi, Padfield, Dirk Ryan, Cline, Harvey
• Granted Patent: US 7,949,181 B2
• Time in Patent Office: Days
• US Class Current: 382/164
• CPC Class Codes: G06V 20/695 Preprocessing, e.g. image segmentation

Abstract

The present techniques provide for the processing of color tissue images based on image segmentation. In an exemplary embodiment, the color and texture features of pixels in a tissue image are used to generate a matrix of feature vectors. A subset of feature vectors is selected from the matrix of feature vectors and a set of colors and textures are derived using the tissue image and the subset of feature vectors. An initial segmented tissue image is then generated from this set of colors and textures.

Claims

1. A method for processing a color tissue image comprising:

generating a matrix of feature vectors, wherein each feature vector corresponds to a respective pixel of a tissue image and describes color and texture features of the respective pixel;

selecting a subset of feature vectors from the matrix of feature vectors;

deriving a set of colors and textures using the tissue image and the subset; and

generating an initial segmented tissue image using the set of colors and textures and the tissue image.

2. The method as recited in claim 1, comprising extracting the two or more features for each pixel of interest in the tissue image.

3. The method as recited in claim 1, wherein the color and texture features comprise three color features and four texture features.

4. The method as recited in claim 1, wherein deriving the set of colors and textures comprises performing a two-dimensional self organized mapping using the subset of feature vectors and the tissue image.

5. The method as recited in claim 1, wherein generating the initial segmented tissue image comprises performing a one-dimensional self organized mapping using the set of colors and textures and the tissue image.

6. The method as recited in claim 1, comprising merging regions within the initial segmented image to generate a segmented image having fewer segments than the initial segmented image.

7. The method as recited in claim 6, comprising assigning colors or labels to the segments of the segmented image to generate a pseudo-colored or labeled image.

8. The method as recited in claim 6, wherein merging regions is iteratively performed until a merge threshold criteria is no longer satisfied and/or until a target number of regions remain.

9. The method as recited in claim 1, comprising performing an automatic pathology assessment of the initial segmented tissue image or an image derived from the initial segmented tissue image.

10. The method as recited in claim 1, comprising establishing a training database using the initial segmented tissue image or an image derived from the initial segmented tissue image.

11. The method as recited in claim 1, comprising training a pathologist or a CAD algorithm using the initial segmented tissue image or an image derived from the initial segmented tissue image.

12. One or more computer-readable media having a computer program comprising:

a routine configured to generate a matrix of feature vectors, wherein each feature vector corresponds to a respective pixel of a tissue image and describes color and texture features of the respective pixel;

a routine configured to select a subset of feature vectors from the matrix of feature vectors;

a routine configured to derive a set of colors and textures using the tissue image and the subset; and

a routine configured to generate an initial segmented tissue image using the set of colors and textures and the tissue image.

13. The one or more computer-readable media as recited in claim 12, wherein the computer program comprises a routine configured to extract the two or more features for each pixel of interest in the tissue image.

14. The one or more computer-readable media as recited in claim 12, wherein the routine configured to derive the set of colors and textures performs a two-dimensional self organized mapping using the subset of feature vectors and the tissue image.

15. The one or more computer-readable media as recited in claim 12, wherein the routine configured to generate an initial segmented tissue image performs a one-dimensional self organized mapping using the set of colors and textures and the tissue image.

16. The one or more computer-readable media as recited in claim 12, wherein the computer program comprises a routine configured to merge regions within the initial segmented image to generate a segmented image having fewer segments than the initial segmented image.

17. The one or more computer-readable media as recited in claim 16, wherein the computer program comprises a routine configured to assign colors or labels to the segments of the segmented image to generate a pseudo-colored or labeled image.

18. The one or more computer-readable media as recited in claim 16, wherein the routine configured to merge regions iteratively merges regions until a merge threshold criteria is no longer satisfied and/or until a target number of regions remain.

19. An image analysis system, comprising:

an imager configured to optically analyze a tissue sample;

data acquisition circuitry configured to acquire a tissue image of the tissue sample via the imager; and

data processing circuitry configured to generate a matrix of feature vectors, wherein each feature vector corresponds to a respective pixel of the tissue image and describes color and texture features of the respective pixel, to select a subset of feature vectors from the matrix of feature vectors, to derive a set of colors and textures using the tissue image and the subset, and to generate an initial segmented tissue image using the set of colors and textures and the tissue image.

20. The image analysis system of claim 19, wherein the data processing circuitry is further configured to extract the two or more features for each pixel of interest in the tissue image.

21. The image analysis system of claim 19, wherein the data processing circuitry is configured to derive the set of colors and textures by performing a two-dimensional self organized mapping using the subset of feature vectors and the tissue image.

22. The image analysis system of claim 19, wherein the data processing circuitry is configured to generate the initial segmented tissue image by performing a one-dimensional self organized mapping using the set of colors and textures and the tissue image.

23. The image analysis system of claim 19, wherein the data processing circuitry is further configured to merge regions within the initial segmented image to generate a segmented image having fewer segments than the initial segmented image.

24. The image analysis system of claim 23, wherein the data processing circuitry is configured to assign colors or labels to the segments of the segmented image to generate a pseudo-colored or labeled image.

25. The image analysis system of claim 23, wherein the data processing circuitry is configured to merge regions iteratively until a merge threshold criteria is no longer satisfied and/or until a target number of regions remain.