Segmentation in medical images

• US 20030099385A1
• Filed: 09/30/2002
• Published: 05/29/2003
• Est. Priority Date: 11/23/2001
• Status: Active Grant

• Current Assignee: MeVis Medical Solutions AG (Varex Imaging Corporation)
• Original Assignee: MeVis Medical Solutions, Inc. (Varex Imaging Corporation)
• Inventors: Zeng, Xiaolan, Zhang, Wei, Schneider, Alexander C.
• Granted Patent: US 7,336,809 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/20156 Automatic seed setting
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 2207/30064 Lung nodule
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models
• CPC Class Codes: G06T 7/155 involving morphological operators
• CPC Class Codes: G06T 7/187 involving region growing; involving region merging; involving connected component labelling
• CPC Class Codes: Y10S 128/922 including image analysis

Abstract

A method and system of volume segmentation is disclosed. To address throughput speed and accuracy requirements, the segmentation is divided into two stages: presegmentation and detailed segmentation. In presegmentation, a digital image volume is processed to identify anatomical structures from image backgrounds in a gross manner possibly at reduced image resolution. Detailed segmentation then involves refining and segmenting further the fine details within the anatomical structures identified by presegmentation. One result of the overall volume segmentation algorithm is a volume in which segmented regions of interest, such as nodules, are identified.

Claims

1. A method of segmenting an anatomical image comprising:

presegmenting the image to identify at least one region from an image background, thereby forming a presegmented image;

transferring the presegmented image to an image having a first resolution; and

segmenting further the identified region in the image having a first resolution into finer anatomical structures.

2. The method of claim 1 wherein the presegmenting is performed at a reduced image resolution.

3. The method of claim 1 wherein presegmenting the image identifies the body and lungs.

4. The method of claim 1 further comprising refining a boundary of at least one region identified during presegmenting.

5. The method of claim 4 wherein the boundary is a pleural boundary.

6. The method of claim 1 further comprising processing the anatomical structures to identify one or more nodules.

7. The method of claim 4 wherein the one or more nodules includes at least one pleural nodule.

8. The method of claim 1 further including processing the anatomical structures to identify a boundary.

9. The method of claim 8 wherein the boundary is a pleural boundary.

10. The method of claim 8 further including applying morphological closing to the boundary to form a smooth boundary.

11. The method of claim 1 wherein segmenting further the identified region includes identifying a diaphragm and a mediastinum.

12. The method of claim 1 wherein segmenting further the identified region includes identifying left and right lungs.

13. The method of claim 1 further comprising recovering thin anterior or posterior junctions.

14. The method of claim 1 wherein segmenting further the identified region includes identifying different zones of the lungs.

15. The method of claim 1 wherein segmenting further the identified region is performed on the basis of known characteristics of anatomy corresponding to anatomical information in the digital images.

16. The method of claim 1 further comprising processing the anatomical structures to identify bone structures.

17. The method of claim 1 wherein segmenting further the identified region includes identifying a costal peripheral zone.

18. The method of claim 1 further including smoothing pleura.

19. The method of claim 1 wherein presegmenting the image further comprises:

processing the image to create a reduced resolution image volume;

identifying in the reduced resolution image a seed point having a gray level intensity exceeding a first predetermined threshold;

growing a region from the seed point; and

performing feature analysis to select a region of interest from all grown regions.

20. The method of claim 19 wherein the region of interest is a body region.

21. The method of claim 20 further comprising selecting one or two largest remaining ungrown regions as lungs.

22. The method of claim 21 further comprising applying morphological closing to the lungs.

23. The method of claim 1 wherein presegmenting the image includes identifying a background region.

24. The method of claim 1 wherein presegmenting the image further comprises growing the background region inwards from a periphery of the image to a region identified as a body region.

25. The method of claim 24 wherein presegmenting the image further comprises identifying a body region and a lung field in the larger region.

26. The method of claim 25 further including selecting largest regions from all the grown regions.

27. The method of claim 26 wherein the largest region is a lung field.

28. The method of claim 25 further comprising applying morphological closing to a grown region.

29. The method of claim 1 further including the step of reducing noise in the image.

30. The method of claim 29 wherein the step of reducing noise is performed by a Gaussian smoothing operation.

31. The method of claim 29 wherein the step of reducing noise is performed on an anatomical structure.

32. The method of claim 1 further including recovering anatomical details.

33. The method of claim 32 wherein the recovered anatomical details are anterior or posterior junction tissue.

34. The method of claim 1 further comprising creating a mask.

35. The method of claim 1 wherein the anatomical structure is a heart, brain, spine, colon, liver or kidney.

36. The method of claim 1 wherein the image is a series of sectional digital images of the body that constitute an image volume.

37. The method of claim 36 wherein presegmenting the image volume further comprises:

processing the image volume to create one or more reduced resolution volumes;

identifying in the one or more reduced resolution volumes one or more seed points at image voxels having gray level intensities exceeding a first predetermined threshold;

growing a volume from the one or more seed points; and

performing feature analysis to select a volume of interest from all grown volumes.

38. The method of claim 36 wherein the series of sectional digital images depicts a thoracic region.

39. The method of claim 36 wherein the sectional digital images are formed by computer tomography, magnetic resonance imaging, positron emission tomography, or single photon emission computer tomography.

40. The method of claim 1 wherein the first resolution is full resolution.

41. The method of claim 1 further comprising the steps of processing the anatomical structure to identify one or more lesions and reporting the lesion as located in one of said finer anatomical structures.

42. In a storage medium, software for segmenting anatomical information in an image of a portion of a body comprising:

logic code for presegmenting the image to identify at least one region from an image background, thereby forming a presegmented image;

logic code for transferring the presegmented image to an image having a first resolution; and

logic code for segmenting the identified region into finer anatomical structures.

43. The software of claim 42 wherein one identified region is a lung further including logic code for processing an anatomical structure to identify one or more nodules.

44. The software of claim 43 further comprising logic code for processing an anatomical structure to identify the diaphragm and the mediastinum.

45. The software of claim 43 further comprising logic code for processing an anatomical structure to identify the costal peripheral zone.

46. The software of claim 43 wherein the image is a series of sectional digital images of the body that constitute an image volume and the logic code for presegmenting the image comprises:

logic code for identifying seed points at image voxels having gray level intensities exceeding a first predetermined threshold;

logic code for growing volumes from the seed points to include voxels having gray level intensities exceeding a second predetermined threshold;

logic code for identifying a body region; and

logic code for growing a background region inwards from a periphery of the reduced resolution image to a volume identified as the body region.

47. The software of claim 46 further comprising logic code for growing remaining unidentified voxels and selecting a largest or first two largest volumes as a lung field.

48. A method of segmenting information to identify organ nodules in a series of digital images comprising:

forming from the digital images a series of reduced resolution images;

processing the reduced resolution images to identify a reduced resolution body region and a reduced resolution background region;

using the identification of the reduced resolution body region and the reduced resolution background region to identify a body region, and a background region in the digital images;

processing the digital images to identify a boundary of an organ; and

processing the digital images to identify a region of interest in the organ.

49. The method of claim 48 wherein the organ boundary is a pleural boundary.

50. The method of claim 49 wherein the region of interest is a pleural nodule.

51. The method of claim 48 wherein processing the reduced resolution images to identify a body region and a background region comprises:

identifying in the reduced resolution images seed points at image voxels having gray level intensities exceeding a first predetermined threshold;

growing volumes from the seed points to include voxels having gray level intensities exceeding a second predetermined threshold;

identifying the body region as the largest volume grown; and

growing the background region inwards from the periphery of the reduced resolution image to the volume identified as the body region.

52. The method of claim 51 further comprising growing remaining unidentified voxels and selecting a largest or first two largest volumes as a lung field.