System and method for multi-label image segmentation

• US 20050163375A1
• Filed: 01/05/2005
• Published: 07/28/2005
• Est. Priority Date: 01/23/2004
• Status: Active Grant

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Grady, Leo
• Granted Patent: US 7,460,709 B2
• Time in Patent Office: Days
• US Class Current: 382/180
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20072 Graph-based image processing
• CPC Class Codes: G06T 2207/20101 Interactive definition of point of interest, landmark or seed
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/162 involving graph-based methods

Abstract

A system and method for multi-label image segmentation is provided. The method comprises the steps of: receiving image data including a set of labeled image elements; mapping a change in intensities of the image data to edge weights; determining potentials for each image element in the image data; and assigning a label, based upon the determined potentials, to each image element in the image data.

Claims

1. A method for multi-label image segmentation, comprising:

receiving image data including a set of labeled image elements;

mapping a change in intensities of the image data to edge weights;

determining potentials for each image element in the image data; and

assigning a label, based upon the determined potentials, to each image element in the image data.

2. The method of claim 1, wherein the image data is marked by one of a user and a computer.

3. The method of claim 1, wherein the image elements are one of a pixel and a voxel.

4. The method of claim 1, wherein the image data includes unlabeled image elements.

5. The method of claim 1, wherein the change in intensities of the image data to edge weights is mapped by a Gaussian weighting function.

6. The method of claim 5, wherein the Gaussian weighting function is given by

w_ij=exp(−

β

(g_i−

g_j)²),

where g_i indicates image intensity at image element i, g_j indicates image intensity at image element j, and β

is a free parameter.

7. The method of claim 1, wherein the change in intensities of the image data to edge weights is mapped to represent the image data with random walker biases.

8. The method of claim 1, wherein the potentials for each image element in the image data are determined by

9. The method of claim 1, wherein the determined potentials represent the probability that a random walker starting at an image element in the image data first reaches a seed point in the image data when set to unity.

10. The method of claim 1, wherein the label assigned to each image element corresponds to

max_s(x_i^s)

where x_i^s is the potential at an image element i corresponding to a label s.

11. The method of claim 1, wherein the labels assigned to each image element are represented by a color.

12. The method of claim 1, further comprising:

outputting the assigned label.

13. The method of claim 1, further comprising:

acquiring the image data.

14. The method of claim 13, wherein the image data is acquired by one of a computed tomographic (CT), helical CT, x-ray, positron emission tomographic (PET), fluoroscopic, ultrasound, and magnetic resonance (MR) imaging technique.

15. The method of claim 13, wherein the image data is acquired by one of a digital camera and a flatbed scanner.

16. A system for multi-label image segmentation, comprising:

a memory device for storing a program;

a processor in communication with the memory device, the processor operative with the program to;

receive image data including a set of labeled image elements;

map a change in intensities of the image data to edge weights;

determine potentials for each image element in the image data; and

assign a label, based upon the determined potentials, to each image element in the image data.

17. The system of claim 16, wherein the image data is marked by one of a user and a computer.

18. The system of claim 16, wherein the image elements are one of a pixel and a voxel.

19. The system of claim 16, wherein the image data includes unlabeled image elements.

20. The system of claim 16, wherein the change in intensities of the image data to edge weights are mapped by a Gaussian weighting function to represent the image data with random walker biases.

21. The system of claim 20, wherein the Gaussian weighting function is given by

w_ij=exp(g_i−

g_j)²),

where g_i indicates image intensity at image element i, g_j indicates image intensity at image element j, and β

is a free parameter.

22. The system of claim 20, wherein the random walker is biased to avoid crossing object boundaries.

23. The system of claim 16, wherein the potentials for each image element in the image data are determined by

24. The system of claim 16, wherein the determined potentials represent the probability that a random walker starting at an image element in the image data first reaches a seed point in the image data when set to unity.

25. The system of claim 16, wherein the label assigned to each image element corresponds to

max_s(x_i^s),

where x_i^s is the potential at an image element i corresponding to a label s.

26. The system of claim 16, wherein the labels assigned to each image element are represented by a color.

27. The system of claim 16, wherein the processor is further operative with the program code to:

output the assigned label.

28. The system of claim 16, wherein the processor is further operative with the program code to:

acquire the image data.

29. The system of claim 28, wherein the image data is acquired by one of a computed tomographic (CT), helical CT, x-ray, positron emission tomographic (PET), fluoroscopic, ultrasound, and magnetic resonance (MR) imaging technique.

30. The system of claim 28, wherein the image data is acquired by one of a digital camera and a flatbed scanner.

31. A computer program product comprising a computer useable medium having computer program logic recorded thereon for multi-label image segmentation, the computer program logic comprising:

program code for receiving image data including a set of labeled image elements;

program code for mapping a change in intensities of the image data to edge weights;

program code for determining potentials for each image element in the image data; and

program code for assigning a label, based upon the determined potentials, to each image element in the image data.

32. A system for multi-label image segmentation, comprising:

means for receiving image data including a set of labeled image elements;

means for mapping a change in intensities of the image data to edge weights;

means for determining potentials for each image element in the image data; and

means for assigning a label, based upon the determined potentials, to each image element in the image data.

33. A method for semi-automatic multi-label image segmentation, comprising:

acquiring image data;

marking the image data to include a set of labeled image elements;

mapping a change in intensities of the image data to edge weights to represent the image data with random walker biases;

determining potentials for each image element in the image data, wherein the determined potentials represent the probability that a random walker starting at an image element in the image data first reaches a seed point in the image data set to unity;

assigning a label, based upon the determined potentials, to each image element in the image data; and

outputting a segmentation of the image data including the assigned labels.