Method and system for the assessment of tumor extent in magnetic resonance images

• US 6,112,112 A
• Filed: 09/18/1998
• Issued: 08/29/2000
• Est. Priority Date: 09/18/1998
• Status: Expired due to Term

• Current Assignee: ARCH Development Corporation (University of Chicago)
• Original Assignee: ARCH Development Corporation (University of Chicago)
• Inventors: Giger, Maryellen L., Bick, Ulrich, Gilhuijs, Kenneth
• Primary Examiner(s): Lateef, Marvin M.
• Assistant Examiner(s): MANTIS MERCADER, ELENI M
• Application Number:
  US09/156,413
• Time in Patent Office: 711 Days
• Field of Search: 324/309, 324/307, 378/37, 378/62, 600/410, 600/425, 600/420, 382/128, 382/131, 382/132, 382/224, 382/225, 382/226, 382/227
• US Class Current: 600/425
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30068 Mammography; Breast
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 7/11 Region-based segmentation

Abstract

A method, computer program product, and system for assessing tumor extent in medical temporally acquired images. The method, on which the computer program product and system is based, includes obtaining image data corresponding to temporally acquired images including a tumor and surrounding anatomy and performing variance processing on the obtained image data to derive variance image data defining a variance image indicative of variation of voxels in the temporally acquired medical images. Techniques include novel developments and implementations of breast volume segmentation, breast border removal, lesion enhancement, determination of the bounding sphere, computation of a 3-D search volume, suppression of surrounding structures, and volume growing. Output from the methods yields an estimate of the extent of the tumor (lesion) in the breast.

Claims

1. A method for assessing tumor extent in medical temporally acquired images, comprising:

obtaining image data corresponding to temporally acquired images including a tumor and surrounding anatomy; and

performing variance processing with respect to time on the obtained image data to derive variance image data defining a variance image indicative of variation of voxels in said temporally acquired medical images over time.

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

obtaining magnetic resonance (MR) image data representing temporal progression of a contrast agent in said tumor and surrounding anatomy.

3. The method of claim 1, further comprising:

performing extent processing on the variance image data to determine the extent of the tumor.

4. The method of claim 3, wherein:

said variance processing step comprises weighting each voxel as a function of temporal change in value of the voxel due to contrast uptake to obtain said variance image data; and

said extent processing step comprises,

determining a mean value function based on the mean value of the values of voxels of the variance image data that intersect the surface of a sphere which has a center defined by a seed point, as a function of radius of the sphere, and

determining a radius which closely encompasses an uptake region of said tumor based on the mean value function.

5. The method of claim 4, further comprising:

performing thresholding on the variance image data to detect said seed point.

6. The method of claim 5, wherein said step of performing thresholding comprises:

identifying voxels with a higher value than that of other voxels and using at least one of the identified voxels as said seed point.

7. The method of claim 6, wherein said step of performing thresholding comprises:

detecting plural clusters of voxels with a higher value than that of other voxels, and using a voxel at a center of said plural clusters of voxels as said seed point.

8. The method of claim 4, wherein said extent processing step comprises:

low pass filtering said mean value function to produce an expansion function.

9. The method of claim 8, where said extent processing step comprises determining said radius which closely encompasses said uptake region of said tumor based on the expansion function.

10. The method of claim 9, wherein said extent processing step comprises:

determining said radius which closely encompasses said uptake region of said tumor as that radius corresponding to the first point in said expansion function after a global maximum at which the derivative of the expansion function reaches zero.

11. The method of claim 4, wherein said extent processing comprises:

producing a binary image representative of a bounding sphere defined by said radius which closely encompasses said uptake region of said tumor; and

multiplying said variance image with data derived from said binary image to suppress data outside said bounding sphere and to identify a volume growing target region.

12. The method of claim 11, wherein said extent processing comprises:

determining a threshold to terminate volume growing in said volume growing target region;

starting from a predetermined voxel having a value exceeding said threshold, performing volume growing processsing by comparing neighboring voxels to said threshold, and adding to a region growing volume those neighboring voxels bearing a predetermined relationship to said threshold in dependence on the result of the comparison; and

performing further iterative comparing, in which for each voxel included in said region growing volume, voxels neighboring thereto are compared to said threshold and included in said region growing volume in dependence on the result of the comparison.

13. The method of claim 12, further comprising:

identifying said voxels included in said region growing volume as being tumor voxels.

14. The method of claim 7, further comprising:

performing thresholding on the variance image data to detect said seed point.

15. The method of claim 14, wherein said step of starting from a predetermined voxel having a value exceeding said threshold comprises starting from that voxel closest to said seed point and having a value exceeding said threshold to terminate volume growing in said volume growing target region.

16. The method of claim 14, wherein said step of performing thresholding comprises:

identifying voxels with a higher value than that of other voxels and using at least one of the identified voxels as said seed point.

17. The method of claim 16, wherein said step of starting from a predetermined voxel having a value exceeding said threshold comprises starting from said seed point when said seed point has a value equal to or exceeding the threshold to terminate volume growing in said volume growing target region.

18. The method of claim 16, wherein said step of performing thresholding comprises:

detecting plural clusters of voxels with a higher value than that of other voxels, and using a voxel at a center of said plural clusters of voxels as said seed point.

19. The method of claim 18, wherein said step of starting from a predetermined voxel having a value exceeding said threshold comprises starting from said seed point when said seed point has a value equal to or exceeding the threshold to terminate volume growing in said volume growing target region.

20. The method of claim 4, wherein said extent processing comprises:

producing a binary image representative of a bounding sphere defined by said radius which closely encompasses said uptake region of said tumor;

smoothing said binary image to obtain a smoothed binary image; and

multiplying said variance image with data derived from said smoothed binary image.

21. The method of claim 1, wherein said step of obtaining image data comprises:

obtaining image data including a breast of a subject; and

segmenting the breast volume from the rest of the obtained image data.

22. The method of claim 21, wherein said step of segmenting comprises:

performing 3-dimensional morphological processing to remove small gaps and spikes from the obtained image data;

determining a global histogram of voxel values from the image data after performing 3-dimensional morphological processing;

determining a threshold from the global histogram by maximization of the inter-class variance between peaks in said global histogram; and

defining the breast volume based on those voxels having a value exceeding the threshold determined from the global histogram.

23. The method of claim 22, wherein said step of defining a breast volume comprises:

performing a sequence of contour tracing and region filling steps to remove gaps in the voxels having a value exceeding the threshold determined from the global histogram.

24. The method of claim 23, further comprising:

removing a border of the segmented breast volume by morphological erosion.

25. The method of claim 21, further comprising:

removing a border of the segmented breast volume by morphological erosion.

26. The method of claim 1, further comprising:

performing thresholding on the variance image data to detect potential tumor sites.

27. The method of claim 13, further comprising:

performing thresholding on the variance image data to detect said seed point.

28. The method of claim 27, wherein said step of performing thresholding comprises:

identifying voxels with a higher value than that of other voxels and using at least one of the identified voxels as said seed point.

29. The method of claim 28, wherein said step of performing thresholding comprises:

detecting plural clusters of voxels with a higher value than that of other voxels, and using a voxel at a center of said plural clusters of voxels as said seed point.

30. A computer readable medium storing computer instructions for assessing tumor extent in medical temporally acquired images, by performing the steps of:

obtaining image data corresponding to temporally acquired images including a tumor and surrounding anatomy; and

performing variance processing with respect to time on the obtained image data to derive variance image data defining a variance image indicative of variation of voxels in said temporally acquired medical images over time.

31. The computer readable medium of claim 30, wherein the obtaining step comprises:

obtaining magnetic resonance (MR) image data representing temporal progression of a contrast agent in said tumor and surrounding anatomy.

32. The computer readable medium of claim 30, further comprising:

performing extent processing on the variance image data to determine the extent of the tumor.

33. The computer readable medium of claim 32, wherein:

said variance processing step comprises weighting each voxel as a function of temporal change in value of the voxel due to contrast uptake to obtain said variance image data; and

said extent processing step comprises,

determining a mean value function based on the mean value of the values of voxels of the variance image data that intersect the surface of a sphere which has a center defined by a seed point, as a function of radius of the sphere, and

determining a radius which closely encompasses an uptake region of said tumor based on the mean value function.

34. The computer readable medium of claim 33, further comprising:

performing thresholding on the variance image data to detect said seed point.

35. The computer readable medium of claim 34, wherein said step of performing thresholding comprises:

identifying voxels with a higher value than that of other voxels, and using at least one of the identified voxels as said seed point.

36. The computer readable medium of claim 35, wherein said step of performing thresholding comprises:

detecting plural clusters of voxels with a higher value than that of other voxels, and using a voxel at a center of said plural clusters of voxels as said seed point.

37. The computer readable medium of claim 33, wherein said extent processing step comprises:

low pass filtering said mean value function to produce an expansion function.

38. The computer readable medium of claim 37, where said extent processing step comprises determining said radius which closely encompasses said uptake region of said tumor based on the expansion function.

39. The computer readable medium of claim 38, wherein said extent processing step comprises:

determining said radius which closely encompasses said uptake region of said tumor as that radius corresponding to the first point in said expansion function after a global maximum at which the derivative of the expansion function reaches zero.

40. The computer readable medium of claim 33, wherein said extent processing comprises:

producing a binary image representative of a bounding sphere defined by said radius which closely encompasses said uptake region of said tumor; and

multiplying said variance image with data derived from said binary image to suppress data outside said bounding sphere and to identify a volume growing target region.

41. The computer readable medium of claim 40, wherein said extent processing comprises:

determining a threshold to terminate volume growing in said volume growing target region;

starting from a predetermined voxel having a value exceeding said threshold, performing volume growing processsing by comparing neighboring voxels to said threshold, and adding to a region growing volume those neighboring voxels bearing a predetermined relationship to said threshold in dependence on the result of the comparison; and

performing further iterative comparing, in which for each voxel included in said region growing volume, voxels neighboring thereto are compared to said threshold and included in said region growing volume in dependence on the result of the comparison.

42. The computer readable medium of claim 41, further comprising:

identifying said voxels included in said region growing volume as being tumor voxels.

43. The computer readable medium of claim 42, further comprising:

performing thresholding on the variance image data to detect said seed point.

44. The computer readable medium of claim 43, wherein said step of performing thresholding comprises:

identifying voxels with a higher value than that of other voxels and using at least one of the identified voxels as said seed point.

45. The computer readable medium of claim 44, wherein said step of performing thresholding comprises:

detecting plural clusters of voxels with a higher value than that of other voxels, and using a voxel at a center of said plural clusters of voxels as said seed point.

46. The computer readable medium of claim 44, further comprising:

performing thresholding on the variance image data to detect said seed point.

47. The computer readable medium of claim 46, wherein said step of starting from a predetermined voxel having a value exceeding said threshold comprises starting from that voxel closest to said seed point and having a value exceeding said threshold to terminate volume growing in said volume growing target region.

48. The computer readable medium of claim 46, wherein said step of performing thresholding comprises:

identifying voxels with a higher value than that of other voxels, and using at least one of the identified voxels as said seed point.

49. The computer readable medium of claim 48, wherein said step of starting from a predetermined voxel having a value exceeding said threshold comprises starting from said seed point when said seed point has a value equal to or exceeding the threshold to terminate volume growing in said volume growing target region.

50. The computer readable medium of claim 48, wherein said step of performing thresholding comprises:

detecting plural clusters of voxels with a higher value than that of other voxels, and using a voxel at a center of said plural clusters of voxels as said seed point.

51. The computer readable medium of claim 50, wherein said step of starting from a predetermined voxel having a value exceeding said threshold comprises starting from said seed point when said seed point has a value equal to or exceeding the threshold to terminate volume growing in said volume growing target region.

52. The computer readable medium of claim 30, wherein said step of obtaining image data comprises:

obtaining image data including a breast of a subject; and

segmenting the breast volume from the rest of the obtained image data.

53. The computer readable medium of claim 52, wherein said step of segmenting comprises:

performing 3-dimensional morphological processing to remove small gaps and spikes from the obtained image data;

determining a global histogram of voxel values from the image data after performing 3-dimensional morphological processing;

determining a threshold from the global histogram by maximization of the inter-class variance between peaks in said global histogram; and

defining the breast volume based on those voxels having a value exceeding the threshold determined from the global histogram.

54. The computer readable medium of claim 53, wherein said step of defining a breast volume comprises:

performing a sequence of contour tracing and region filling steps to remove gaps in the voxels having a value exceeding the threshold determined from the global histogram.

55. The computer readable medium of claim 54, further comprising:

removing a border of the segmented breast volume by morphological erosion.

56. The computer readable medium of claim 52, further comprising:

removing a border of the segmented breast volume by morphological erosion.

57. The computer readable medium of claim 33, wherein said extent processing comprises:

producing a binary image representative of a bounding sphere defined by said radius which closely encompasses said uptake region of said tumor;

smoothing said binary image to obtain a smoothed binary image; and

multiplying said variance image with data derived from said smoothed binary image.

58. The computer readable medium of claim 30, further comprising:

performing thresholding on the variance image data to detect potential tumor sites.

59. A system for assessing tumor extent in medical temporally acquired images, comprising:

a mechanism configured to obtain image data corresponding to temporally acquired images including a tumor and surrounding anatomy; and

a mechanism configured to perform variance processing with respect to time on the obtained image data to derive variance image data defining a variance image indicative of variation of voxels in said temporally acquired medical images over time.

60. The system of claim 59, wherein the mechanism configured to obtain image data comprises:

a mechanism configured to obtain magnetic resonance (MR) image data representing temporal progression of a contrast agent in said tumor and surrounding anatomy.

61. The system of claim 59, further comprising:

a mechanism configured to perform extent processing on the variance image data to determine the extent of the tumor.

62. The system of claim 61, wherein:

said mechanism configured to perform said variance processing comprises weighting each voxel as a function of temporal change in value of the voxel due to contrast uptake to obtain said variance image data; and

said mechanism configured to perform extent processing comprises,

a mechanism configured to determine a mean value function based on the mean value of the values of voxels of the variance image data that intersect the surface of a sphere which has a center defined by a seed point, as a function of radius of the sphere, and

a mechanism configured to determine a radius which closely encompasses an uptake region of said tumor based on the mean value function.

63. The system of claim 62, wherein said mechanism configured to perform extent processing comprises:

a mechanism configured to produce a binary image representative of a bounding sphere defined by said radius which closely encompasses said uptake region of said tumor;

a mechanism configured to smooth said binary image to obtain a smoothed binary image; and

a mechanism configured to multiply said variance image with data derived from said smoothed binary image.

64. The system of claim 62, wherein said mechanism configured to perform extent processing comprises:

a mechanism configured to low pass filter said mean value function to produce an expansion function.

65. The system of claim 64, where said mechanism configured to perform extent processing comprises:

a mechanism configured to determine said radius which closely encompasses said uptake region of said tumor based on the expansion function.

66. The system of claim 65, wherein said mechanism configured to perform extent processing comprises:

a mechanism configured to determine said radius which closely encompasses said uptake region of said tumor as that radius corresponding to the first point in said expansion function after a global maximum at which the derivative of the expansion function reaches zero.

67. The system of claim 62, wherein said mechanism configured to perform extent processing comprises:

a mechanism configured to produce a binary image representative of a bounding sphere defined by said radius which closely encompasses said uptake region of said tumor; and

a mechanism configured to multiply said variance image with data derived from said binary image to suppress data outside said bounding sphere and to identify a volume growing target region.

68. The system of claim 67, wherein said extent processing comprises:

a mechanism configured to determine a threshold to terminate volume growing in said volume growing target region;

a mechanism configured to perform volume growing processsing, starting from a predetermined voxel having a value exceeding said threshold, by comparing neighboring voxels to said threshold, and adding to a region growing volume those neighboring voxels bearing a predetermined relationship to said threshold in dependence on the result of the comparison; and

a mechanism configured to perform further iterative comparing, in which for each voxel included in said region growing volume, voxels neighboring thereto are compared to said threshold and included in said region growing volume in dependence on the result of the comparison.

69. The system of claim 68, further comprising:

a mechanism configured to perform thresholding on the variance image data to detect said seed point.

70. The system of claim 69, wherein said mechanism configured to perform thresholding comprises:

a mechanism configured to identify voxels with a higher value than that of other voxels, and to use at least one of the identified voxels as said seed point.

71. The system of claim 70, wherein said mechanism configured to perform thresholding comprises:

a mechanism configured to detect plural clusters of voxels with a higher value than that of other voxels, and to use a voxel at a center of said plural clusters of voxels as said seed point.

72. The system of claim 71, wherein said mechanism configured to perform volume growing processing is further configured to start said volume growing processing from said seed point when said seed point has a value equal to or exceeding the threshold to terminate volume growing in said volume growing target region.

73. The system of claim 69, wherein said mechanism configured to perform volume growing processing is further configured to start said volume growing processing from that voxel closest to said seed point and having a value exceeding said threshold to terminate volume growing in said volume growing target region.

74. The system of claim 70, wherein said mechanism configured to perform volume growing processing is further configured to start said volume growing processing from said seed point when said seed point has a value equal to or exceeding the threshold to terminate volume growing in said volume growing target region.

75. The system of claim 68, further comprising:

a mechanism configured to identify said voxels included in said region growing volume as being tumor voxels.

76. The system of claim 75, further comprising:

a mechanism configured to perform thresholding on the variance image data to detect said seed point.

77. The system of claim 76, wherein said mechanism configured to perform thresholding comprises:

a mechanism configured to identify voxels with a higher value than that of other voxels, and to use at least one of the identified voxels as said seed point.

78. The system of claim 77, wherein said mechanism configured to perform thresholding comprises:

a mechanism configured to detect plural clusters of voxels with a higher value than that of other voxels, and to use a voxel at a center of said plural clusters of voxels as said seed point.

79. The system of claim 62, further comprising:

a mechanism configured to perform thresholding on the variance image data to detect said seed point.

80. The system of claim 79, wherein said mechanism configured to perform thresholding comprises:

a mechanism configured to identify voxels with a higher value than that of other voxels, and to use at least one of the identified voxels as said seed point.

81. The system of claim 80, wherein said mechanism configured to perform thresholding comprises:

a mechanism configured to detect plural clusters of voxels with a higher value than that of other voxels, and to use a voxel at a center of said plural clusters of voxels as said seed point.

82. The system of claim 59, wherein said mechanism configured to obtain image data comprises:

a mechanism configured to obtain image data including a breast of a subject; and

a mechanism configured to segment the breast volume from the rest of the obtained image data.

83. The system of claim 82, wherein said a mechanism configured to perform segmenting comprises:

a mechanism configured to perform 3-dimensional morphological processing to remove small gaps and spikes from the obtained image data;

a mechanism configured to determine a global histogram of voxel values from image data received from the mechanism configured to perform 3-dimensional morphological processing;

a mechanism configured to determine a threshold from the global histogram by maximization of the inter-class variance between peaks in said global histogram; and

a mechanism configured to define the breast volume based on those voxels having a value exceeding the threshold determined from the global histogram.

84. The system of claim 83, wherein said mechanism configured to define a breast volume comprises:

a mechanism configured to perform a sequence of contour tracing and region filling steps to remove gaps in the voxels having a value exceeding the threshold determined from the global histogram.

85. The system of claim 84, further comprising:

a mechanism configured to remove a border of the segmented breast volume by morphological erosion.

86. The system of claim 82, further comprising:

a mechanism configured to remove a border of the segmented breast volume by morphological erosion.

87. The system of claim 59, further comprising:

a mechanism configured to perform thresholding on the variance image data to detect potential tumor sites.