Automatic detection of lung nodules from high resolution CT images

• US 20020028008A1
• Filed: 04/23/2001
• Published: 03/07/2002
• Est. Priority Date: 09/07/2000
• Status: Abandoned Application

• Current Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Novak, Carol L., Qian, Jainzhong, Fan, Li, Wei, Guo-Qing
• Application Number:
  US09/840,266
• Publication Number:
  US 20020028008A1
• 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 2207/20132 Image cropping
• 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

Abstract

A method for automatically detecting lung nodules from MSHR CT images includes defining a volume of interest (VOI) for a lung volume in an MSHR CT image. The lung volume is examined using the VOI, including, determining a local histogram of intensity and adaptive threshold values for segmenting the VOI to obtain seeds. Each seed is examined to detect lung nodules therefrom, including segmenting anatomical structures represented by the seed by applying a segmentation method that adaptively adjusts a segmentation threshold value based on histogram analysis of the seed to extract the structures based on three-dimensional connectivity and histogram intensity information, and classifying each structure as a lung nodule or a non-nodule based on a priori knowledge corresponding to lung nodules and related structures. The lung nodules are displayed. The lung nodules are analyzed, including automatically quantifying lung nodule features to provide an automatic detection decision.

Claims

1. A method for automatically detecting lung nodules from Multi-Slice High Resolution Computed Tomography (MSHR CT) images, comprising the steps of:

defining a volume of interest (VOI) for moving through a lung volume in an MSHR CT image, based on MSHR CT image data;

examining the lung volume using the VOI, including,

determining a local histogram of intensity inside the VOI; and

determining adaptive threshold values for segmenting the VOI to obtain seeds;

examining each of the seeds to detect the lung nodules therefrom, including,

segmenting anatomical structures represented by the seeds by applying a segmentation method to the seeds that adaptively adjusts a segmentation threshold value based on a local histogram analysis of the seeds to extract the anatomical structures based on three-dimensional connectivity and intensity information corresponding to the local histogram; and

classifying each of the segmented, anatomical structures as one of a lung nodule or a non-nodule, based on a priori knowledge corresponding to the lung nodules and related, pre-defined anatomical structures;

displaying the lung nodules; and

analyzing the lung nodules, including,

automatically quantifying features of the lung nodules to provide an automatic detection decision for each of the lung nodules.

2. The method according to claim 1, wherein said defining step comprises the step of locating a lung region in the MSHR CT image, the lung volume being disposed within the lung region.

3. The method according to claim 2, wherein said locating step comprises the step of locating a chest wall and excluding an entire region behind the chest wall.

4. The method according to claim 1, wherein said defining step comprises the step of defining a shape and a size of the VOI.

5. The method according to claim 1, wherein said step of examining the lung volume comprises the step of determining a curvature of a one-dimensional histogram curve corresponding to the local histogram.

6. The method according to claim 5, wherein said step of examining the lung volume comprises the step of determining positive and negative curvature extrema of the curvature of the one-dimensional histogram curve.

7. The method according to claim 6, wherein said step of examining the lung volume comprises the step of determining the adaptive segmentation threshold value based upon an analysis of positive and negative curvature extrema of the curvature of the one-dimensional histogram curve.

8. The method according to claim 1, wherein said step of examining each of the seeds comprises the step of computing intensity and geometric features of the segmented, anatomical structures.

9. The method according to claim 8, wherein the intensity and geometric features are computed from at least some cutting cross sections produced by a 360-degree-spin-plane method applied in the VOI, and the intensity and geometric features comprise at least some of a position, a volume, a circularity, a sphericity, and a mean and standard deviation of intensity.

10. The method according to claim 1, wherein the a priori knowledge comprises at least some of an intensity, a volume, and a shape of the lung nodules and the related, predefined anatomical structures.

11. The method according to claim 1, wherein said classifying step comprises the step of automatically recording a segmented, anatomical structure for further evaluation, when the segmented, anatomical structure is classified as the lung nodule.

12. The method according to claim 1, wherein said classifying step comprises the step of excluding non-nodule structures from further evaluation.

13. The method according to claim 12, wherein said excluding step comprises the step of applying a depth-first search to the seeds in a direction of a Z-axis of the VOI, to exclude any of the seeds representing the non-nodules structures.

14. The method according to claim 1, wherein said displaying step comprises the step of defining a bounding box for a current lung nodule to be displayed, the bounding box including the current lung nodule and any pre-specified background structures.

15. The method according to claim 1, wherein said displaying step comprises the step of refining a segmentation of the lung nodules to enhance detailed surface features of the lung nodules.

16. The method according to claim 1, wherein said displaying step comprises the step of rendering surfaces of the lung nodules to provide three-dimensional free rotation of the lung nodules.

17. The method according to claim 1, wherein said analyzing step comprises the step of receiving, from a user, a final detection decision for each of the lung nodules, the final detection decision overriding the automatic detection decision.

18. The method according to claim 1, further comprising the step of storing the automatic detection decision.

19. The method according to claim 17, further comprising the step of storing the final detection decision.

29. The system according to claim 1, wherein said displaying device renders surfaces of the lung nodules to provide three-dimensional free rotation of the lung nodules.

30. The system according to claim 1, wherein said detection device receives, from a user, a final detection decision for each of the lung nodules, the final detection decision overriding the automatic detection decision.

32. The system according to claim 30, further comprising a storage device for storing the final detection decision.

20. A system for automatically detecting lung nodules from Multi-Slice High Resolution Computed Tomography (MSHR CT) images, comprising the steps of:

a volume of interest selector for defining a volume of interest (VOI) based on MSHR CT image data corresponding to an MSHR CT image, the VOI for moving through a lung volume in the MSHR CT image;

a lung volume examination device for determining a local histogram of intensity inside the VOI, and for determining adaptive threshold values for segmenting the VOI to obtain seeds;

a seed examination device for examining each of the seeds to detect the lung nodules therefrom, including,

a segmentation device for segmenting anatomical structures represented by the seeds by applying a segmentation method to the seeds that adaptively adjusts a segmentation threshold value based on a local histogram analysis to extract the anatomical structures based on three-dimensional connectivity and intensity information corresponding to the local histogram; and

a classifier for classifying each of the segmented, anatomical structures as one of a lung nodule or a non-nodule, based on a priori knowledge corresponding to the lung nodules and related, pre-defined anatomical structures;

a display device for displaying the lung nodules; and

a detection device for automatically quantifying features of the lung nodules to provide an automatic detection decision for each of the lung nodules.

21. The system according to claim 20, wherein the lung volume examination device determines a curvature of a one-dimensional histogram curve corresponding to the local histogram.

22. The system according to claim 21, wherein the lung volume examination device determines positive and negative curvature extrema of the curvature of the one-dimensional histogram curve.

23. The system according to claim 22, wherein said lung volume examination device determines the adaptive segmentation threshold value based upon an analysis of positive and negative curvature extrema of the curvature of the one-dimensional histogram curve.

24. The system according to claim 20, wherein said seed examination device comprises a feature computation device for computing intensity and geometric features of the segmented, anatomical structures.

25. The system according to claim 24, wherein the intensity and geometric features are computed from at least some cutting cross-sections produced by a 360-degree-spin-plane method applied in the VOI, and the intensity and geometric features comprise at least some of a position, a volume, a circularity, a sphericity, and a mean and standard deviation of intensity.

26. The system according to claim 20, wherein the a priori knowledge comprises at least some of an intensity, a volume, and a shape of the lung nodules and the related, predefined anatomical structures.

27. The system according to claim 20, wherein said classifier excludes non-nodule structures from further evaluation.

28. The system according to claim 27, wherein said classifier applies a depth-first search to the seeds in a direction of a Z-axis of the VOI, to exclude any of the seeds representing the non-nodules structures.

31. The system according to claim 20, further comprising a storage device for storing the automatic detection decision.