Lung field segmentation from CT thoracic images

• US 20030099390A1
• Filed: 11/23/2001
• Published: 05/29/2003
• Est. Priority Date: 11/23/2001
• Status: Abandoned Application

• Current Assignee: R2 Technology, Inc. (Hologic Incorporated)
• Original Assignee: R2 Technology, Inc. (Hologic Incorporated)
• Inventors: Zeng, Xiaolan, Zhang, Wei, Schneider, Alexander C.
• Application Number:
  US09/993,793
• Publication Number:
  US 20030099390A1
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/20156 Automatic seed setting
• CPC Class Codes: G06T 2207/30064 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

Abstract

A method and system of volume segmentation is disclosed. To address throughput and accuracy issues, the segmentation is divided into two stages: presegmentation and detailed segmentation. In presegmentation, a digital image volume is segmented into different anatomical structures. In the detailed segmentation, additional processing over a limited range is performed. The result of the volume segmentation is a volume in which segmented regions of interest, such as nodules, are labeled or identified.

Claims

1. A method of segmenting a volume from a series of digital images comprising the steps of:

forming an image volume from the series of digital images;

presegmenting the image volume to identify a body region; and

segmenting further the body region into anatomical volumes.

2. The method of claim 1 wherein the body region is a lung region.

3. The method of claim 1 further including the step of processing the anatomical volumes to identify one or more nodules.

4. The method of claim 3 wherein the one or more nodules includes at least one pleural nodule.

5. The method of claim 1 further including the step of processing the anatomical volumes to identify a boundary.

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

7. The method of claim 1 wherein the step of segmenting further the body region includes forming a coronal section image.

8. The method of claim 1 wherein the step of segmenting further the body region includes identifying a diaphragm and a mediastinum.

9. The method of claim 1 wherein the step of further segmenting the body region is performed on the basis of known characteristics of anatomy corresponding to anatomical information in the digital images or anatomical volumes.

10. The method of claim 1 further comprising the step of processing the anatomical volumes to identify bone structures.

11. The method of claim 2 wherein the step further segmenting the lung region includes identifying a costal peripheral zone.

12. The method of claim 1 wherein the body region is a reduced resolution image.

13. The method of claim 1 further including the step of smoothing pleura.

14. The method of claim 1 wherein the step of presegmenting the image further comprises the steps of:

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; and

growing a volume from the one or more seed points.

15. The method of claim 14 wherein the volume includes voxels having gray level intensities exceeding a second predetermined threshold.

16. The method of claim 14 wherein the step of presegmenting the image includes the step of identifying a background region.

17. The method of claim 14 wherein the step of presegmenting the image further comprises the step of growing the background region inwards from a periphery of the reduced resolution image to a volume identified as the body region.

18. The method of claim 17 wherein the step of presegmenting the image further comprises the steps of identifying grown volumes in the body region.

19. The method of claim 18 further including the step of selecting a largest volume from the grown volumes.

20. The method of claim 19 wherein the largest volume is a lung field.

21. The method of claim 18 wherein the two largest volumes grown is a lung field.

22. The method of claim 18 further comprises the step of applying morphological closing to a grown volume.

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

24. The method of claim 23 wherein the step of reducing noise is performed by a Gaussian smoothing operation.

25. The method of claim 23 wherein the step of reducing noise is performed on an anatomical volume.

26. The method of claim 4 further including the step of applying morphological closing to the boundary to form a smooth boundary.

27. The method of claim 1 further including the step of recovering anatomical details.

28. The method of claim 27 wherein the recovered anatomical details is anterior or posterior junction tissue.

29. The method of claim 3 wherein the step of segmenting includes segmenting the lung region into zones.

30. The method of claim 27 further comprising the step of assigning pixels of one in the series of digital images or anatomical volumes to different zones.

31. The method of claim 1 further comprising the step of creating a mask volume.

32. The method of claim 1 wherein the series digital images depicts a thoracic region.

33. The method of claim 1 wherein the anatomical volume includes an organ.

34. The method of claim 33 wherein the organ is a heart, brain, spine, colon, liver or kidney.

40. The computer system of claim 34 wherein 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 the 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.

43. The method of claim 40 wherein the organ nodules are pleural nodules.

35. A computer system including software for segmenting anatomical information in a series of computer digital images of the lung comprising:

logic code for forming an image volume from the series of digital images;

logic code for presegmenting the image volume to identify a body region; and

logic code for segmenting the body region into anatomical volumes.

36. The computer system of claim 35 further including logic code for processing the segmented images to identify one or more nodules.

37. The computer system of claim 35 further comprising logic code for processing the digital images to form a coronal section image.

38. The computer system of claim 37 further comprising logic code for processing the coronal section image to identify the diaphragm and the mediastinum.

39. The computer system of claim 35 further comprising software for processing the digital images to identify the costal peripheral zone.

41. A method of segmenting information to identify organ nodules comprising the steps of:

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 the organ boundary; and

processing the digital images to identify organ nodules.

42. The method of claim 41 wherein the organ boundary is a pleural boundary.

44. The method of claim 41 wherein the step of processing the reduced resolution images to identify a body region and a background region comprises the steps of:

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.