Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images

• US 6,694,046 B2
• Filed: 03/28/2001
• Issued: 02/17/2004
• Est. Priority Date: 03/28/2001
• Status: Expired due to Term

• Current Assignee: ARCH Development Corporation (University of Chicago)
• Original Assignee: ARCH Development Corporation (University of Chicago)
• Inventors: Aoyama, Masahito, Doi, Kunio
• Primary Examiner(s): Patel, Jayanti K.
• Application Number:
  US09/818,831
• Publication Number:
  US 20020172403A1
• Time in Patent Office: 1,056 Days
• Field of Search: 382/100, 382/128, 382/129-134, 382/173, 382/224, 382/227, 382/242, 382/266, 378/18, 378/37, 378/62, 600/425, 128/897
• US Class Current: 382/130
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

An automated method for analyzing a nodule and a computer storage medium storing computer instructions by which the method can be implemented when the instructions are loaded into a computer to program the computer. The method includes obtaining a digital image including the nodule; segmenting the nodule to obtain an outline of the nodule, including generating a difference image from chest image, identifying image intensity contour lines representative of respective image intensities in a region of interest including the nodule, and obtaining an outline of the nodule based on the image intensity contours; extracting features of the nodule based on the outline; applying features including the extracted features to at least one image classifier; and determining a likelihood of malignancy of the nodule based on the output of the at least one classifier. In one embodiment, extracted features are applied to a linear discriminant analyzer and/or an artificial neural network analyzer, the outputs of which are thresholded and the nodule determined to be non-malignant if each classifier output is below the threshold. In another embodiment, a common nodule appearing in an x-ray chest image and a CT image is segmented in each image, features extracted based on the outlines of each segmented nodule in the respective x-ray chest and CT images, and the extracted features from the x-ray chest image and CT images merged as inputs to a common classifier, with the output of the common classifier indicating the likelihood of malignancy.

Claims

1. A method for analyzing a pulmonary nodule, comprising:

obtaining a digital image including the nodule;

segmenting the nodule to obtain an outline of the nodule, comprising,

generating a difference image from the digital image,

identifying image intensity contour lines representative of respective image intensities in a region of interest including the nodule, and

obtaining an outline of the nodule based on the image intensity contour lines.

2. The method of claim 1, wherein the obtaining an outline step comprises:

determining a band of contours including at least three adjacent contours which are separated from each other by less than a predetermined distance, and identifying the nodule outline as being within said band of contours.

3. The method of claim 2, wherein the obtaining an outline step comprises:

determining at least a portion of said outline to be at an upper location from the bottom to the top of the band.

4. The method of claim 3, wherein the obtaining an outline step comprises:

determining at least a portion of said outline to be at an upper 70% location from the bottom to the top of the band.

5. The method of claim 1, further comprising:

extracting features of the nodule based on the outline;

applying features including the extracted features to at least one image classifier; and

determining a likelihood of malignancy of the nodule based on an output of the at least one image classifier.

6. The method of claim 1, further comprising:

extracting features of the nodule based on the outline;

applying features including the extracted features to an artificial neural network classifier; and

determining a likelihood a malignancy of the nodule based on an output of the artificial neural network classifier.

7. The method of claim 6, wherein said determining a likelihood of malignancy step comprises:

comparing the output of the artificial neural network classifier with a predetermined threshold; and

comparing the output of the artificial neural network classifier with a predetermined threshold; and

determining the nodule to be non-malignant when the output of the artificial neural network classifier is less than said predetermined threshold.

8. The method of claim 1, further comprising:

extracting features of the nodule based on the outline;

applying features including the extracted features to a linear discriminant analysis classifier; and

determining a likelihood of malignancy of the nodule based on an output of the linear discriminant analysis classifier.

9. The method of claim 8, wherein said determining a likelihood of malignancy step comprises:

comparing the output of the linear discriminant analysis classifier with a predetermined threshold; and

determining the nodule to be non-malignant when the output of the linear discriminant analysis classifier is less than said predetermined threshold.

10. The method of claim 1, further comprising:

extracting features of the nodule based on the outline;

applying features including the extracted features to an artificial neural network classifier and to a linear discriminant analysis classifier; and

determining a likelihood of malignancy of the nodule based on outputs of the artificial neural network classifier and the linear discriminant analysis classifier.

11. The method of claim 10, wherein said determining a likelihood of malignancy step comprises:

comparing the outputs of the artificial neural network classifier and the linear discriminant analysis classifier with respective predetermined thresholds; and

determining the nodule to be non-malignant when both the outputs of the artificial neural network classifier and the linear discriminant analysis classifier are less than said respective predetermined thresholds.

12. The method of claims 6, 7, 8, 9, 10, or 11, wherein:

the step of obtaining a digital image comprises obtaining a digital chest radiographic image including the nodule; and

the applying step comprises applying plural of the following features;

(1) age, (2) root-mean square variation of the power spectrum of the nodule contour, (3) overlap measure in the background trend and the density-corrected image derived from the original image, (4) FWHM for the outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (5) degree of irregularity of the nodule outline, (6) FWHM for the inside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (7) FWHM for the inside region of the segmented nodule on the original image, (8) contrast of the segmented nodule on the background trend and the density-corrected image derived from the original image, (9) contrast of the segmented nodule on the original image, (10) degree of circularity of the nodule outline, (11) relative standard deviation for outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, and (12) mean pixel value for inside region of the segmented nodule on the background trend and the density-corrected image derived from the original image.

13. The method of claim 12, wherein:

the applying step comprises applying each of the following features;

(1) age, (2) root-mean square variation of the power spectrum of the nodule contour, (3) overlap measure in the background trend and the density-corrected image derived from the original image, (4) FWHM for the outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (5) degree of irregularity of the nodule outline, (6) FWHM for the inside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, and (7) FWHM for the inside region of the segmented nodule on the original image.

14. The method of claim 12, wherein:

the applying step comprises applying each of the following features;

(1) age, (2) root-mean square variation of the power spectrum of the nodule contour, (3) overlap measure in the background trend and the density-corrected image derived from the original image, (4) FWHM for the outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (5) degree of irregularity of the nodule outline, (7) FWHM for the inside region of the segmented nodule on the original image, (8) contrast of the segmented nodule on the background trend and the density-corrected image derived from the original image.

15. The method of claim 12, wherein:

the applying step comprises applying each of the following features;

(1) age, (2) root-mean square variation of the power spectrum of the nodule contour, (3) overlap measure in the background trend and the density-corrected image derived from the original image, (4) FWHM for the outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (5) degree of irregularity of the nodule outline, (8) contrast of the segmented nodule on the background trend and the density-corrected image derived from the original image, and (9) contrast of the segmented nodule on the original image.

16. The method of claim 12, wherein:

the applying step comprises applying each of the following features;

(1) age, (2) root-mean square variation of the power spectrum of the nodule contour, (3) overlap measure in the background trend and the density-corrected image derived from the original image, (4) FWHM for the outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (6) FWHM for the inside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (7) FWHM for the inside region of the segmented nodule on the original image, and (10) degree of circularity of the nodule outline.

17. The method of claim 8, wherein:

the applying step comprises applying the following features;

(1) age, (2) root-mean square variation of the power spectrum of the nodule contour, (3) overlap measure in the background trend and the density-corrected image derived from the original image, (4) FWHM for the outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (5) degree of irregularity of the nodule outline, (8) contrast of the segmented nodule on the background trend and the density-corrected image derived from the original image, and (11) relative standard deviation for outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image.

18. The method of claim 12, wherein:

the applying step comprises applying the following features;

(1) age, (2) root-mean square variation of the power spectrum of the nodule contour, (3) overlap measure in the background trend and the density-corrected image derived from the original image, (4) FWHM for the outside region of the segmented nodule on the background trend and the density-corrected image derived from the original image, (5) degree of irregularity of the nodule outline, (7) FWHM for the inside region of the segmented nodule on the original image, and (12) mean pixel value for inside region of the segmented nodule on the background trend and the density-corrected image derived from the original image.

19. The method of claim 8, wherein the applying stepfurther comprises:

applying to the at least one image classifier at least one clinical parameter corresponding to the nodule.

20. The method of claim 19, wherein the step of applying the at least one clinical parameter comprises:

selecting the at least one clinical parameter from the group consisting essentially of age and gender.

21. The method of claims 6, 7, 8, 9, 10 or 11, wherein:

the step of obtaining a digital image comprises obtaining a CT image including the nodule; and

the applying step comprises applying plural of the following features;

(1) effective diameter of the nodule contour;

(2) peak value for inside region of the segmented nodule on the edge gradient image derived from the CT image;

(3) sex of the patient;

(4) relative standard deviation for inside region of the segmented nodule on the CT image;

(5) peak value for inside region of the segmented nodule on the CT image;

(6) difference of the mean pixel values for the inside and the outside regions of the segmented nodule on the edge gradient image derived from the CT image;

(7) line pattern component for the outside region of the segmented nodule;

(8) full width at tenth maximum for inside region of the segmented nodule on the CT image;

(9) tangential gradient index for outside region of the segmented nodule; and

(10) first moment of the power spectrum of the nodule contour.

22. The method of claim 21, wherein:

the applying step comprises applying each of the following features;

(1) effective diameter of the nodule contour;

(2) peak value for inside region of the segmented nodule on the edge gradient image derived from the CT image;

(3) sex of the patient;

(4) relative standard deviation for inside region of the segmented nodule on the CT image; and

(5) peak value for inside region of the segmented nodule on the CT image.

23. The method of claim 21, wherein:

the applying step comprises applying each of the following features;

(1) effective diameter of the nodule contour;

(2) peak value for inside region of the segmented nodule on the edge gradient image derived from the CT image;

(3) sex of the patient;

(4) relative standard deviation for inside region of the segmented nodule on the CT image; and

(6) difference of the mean pixel values for the inside and the outside regions of the segmented nodule on the edge gradient image derived from the CT image.

24. The method of claim 21, wherein:

the applying step comprises applying each of the following features;

(1) effective diameter of the nodule contour;

(2) peak value for inside region of the segmented nodule on the edge gradient image derived from the CT image;

(3) sex of the patient;

(4) relative standard deviation for inside region of the segmented nodule on the CT image; and

(7) line pattern component for the outside region of the segmented nodule.

25. The method of claim 21, wherein:

the applying step comprises applying each of the following features;

(1) effective diameter of the nodule contour;

(2) peak value for inside region of the segmented nodule on the edge gradient image derived from the CT image;

(3) sex of the patient;

(6) difference of the mean pixel values for the inside and the outside regions of the segmented nodule on the edge gradient image derived from the CT image; and

(8) full width at tenth maximum for inside region of the segmented nodule on the CT image.

26. The method of claim 21, wherein:

the applying step comprises applying each of the following features;

(1) effective diameter of the nodule contour;

(2) peak value for inside region of the segmented nodule on the edge gradient image derived from the CT image;

(3) sex of the patient;

(5) peak value for inside region of the segmented nodule on the CT image; and

(9) tangential gradient index for outside region of the segmented nodule.

27. The method of claim 21, wherein:

the applying step comprises applying each of the following features;

(1) effective diameter of the nodule contour;

(2) peak value for inside region of the segmented nodule on the edge gradient image derived from the CT image;

(4) relative standard deviation for inside region of the segmented nodule on the CT image;

(5) peak value for inside region of the segmented nodule on the CT image; and

(10) first moment of the power spectrum of the nodule contour.

28. The method of any one of claims 1-5, comprising:

said step of obtaining a digital image including a nodule comprising obtaining from x-ray imaging and CT imaging modalities respective digital images of a same portion of the anatomy in which a common nodule is identified in each image;

said segmenting step comprising segmenting the nodule identified in each digital image to obtain an outline of the nodule in each respective image;

extracting, for each of said digital images, at least one feature of the nodule in the respective image based on the outline; and

merging plural features including the features extracted from the two digital images derived from x-ray imaging and CT imaging modulaties, as inputs to a common image classifier to characterize said nodule based on the merged plurality of extracted features and determine a likelihood of malignancy of the nodule based on an output of the common image classifier.

29. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of any one of claims 19 and 20.

30. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 12.

31. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 13.

32. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 14.

33. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 15.

34. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 17.

35. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 17.

36. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 18.

37. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 21.

38. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 22.

39. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 23.

40. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 24.

41. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 25.

42. A computer readable medium storing computer program instructions for analyzing a nodule, which when used to program a computer cause the computer to perform the steps of claim 26.

43. A computer readable medium storing computer program instructions for analysing a nodule, which when used to program a computercause the computer to perform thesteps of claim 27.

44. A computer readable medium storing computer program instructions for analysing a nodule, which when used to program a computercause the computer to perform thesteps of claim 28.