Automated method and system for improved computerized detection and classification of massess in mammograms

• US 5,832,103 A
• Filed: 08/16/1995
• Issued: 11/03/1998
• Est. Priority Date: 11/29/1993
• Status: Expired due to Term

• Current Assignee: ARCH Development Corporation (University of Chicago)
• Original Assignee: ARCH Development Corporation (University of Chicago)
• Inventors: Lu, Ping, Huo, Zhimin, Giger, Maryellen L., Doi, Kunio
• Primary Examiner(s): Boudreau, Leo
• Assistant Examiner(s): KELLEY, CHRISTOPHER S
• Application Number:
  US08/515,798
• Time in Patent Office: 1,175 Days
• Field of Search: 382/128, 382/130, 382/132, 128/620, 128/618
• US Class Current: 382/130
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts

Abstract

A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.

Claims

1. A method for detecting and classifying a mass in a human body, comprising:

obtaining an image of a portion of said human body;

obtaining a runlength image using said image;

performing multi-gray-level thresholding and size analysis on said runlength image;

detecting whether said runlength image contains a location potentially corresponding to said mass based on thresholding performed at plural gray-level threshold levels in said performing step;

classifying said mass; and

determining a likelihood of malignancy of said mass.

2. A method as recited in claim 1, wherein detecting said location comprises:

bilaterally subtracting said image to obtain a subtracted image; and

performing said multi-gray-level thresholding on said subtracted image.

3. A method as recited in claim 1, wherein:

performing multi-gray-level thresholding comprises thresholding said image at a plurality of gray-level threshold values to produce a corresponding plurality of thresholded images; and

detecting whether said image contains a location comprises detecting whether each of said thresholded images contains a location potentially corresponding to said mass.

4. A method as recited in claim 2, wherein:

performing multi-gray-level thresholding comprises thresholding said subtracted image at a plurality of gray-level threshold values to produce a corresponding plurality of thresholded images;

said method further comprising performing a size analysis on each of said threshold images.

5. A method as recited in claim 1, comprising:

performing said multi-gray-level thresholding on said image at a plurality of gray-level threshold values to produce a corresponding plurality of thresholded images, at least one of said thresholded images containing said region suspected of being said mass.

6. A method as recited in claim 1, comprising:

classifying said mass;

wherein performing said size analysis comprises;

determining at least one of a circularity, an area, and a contrast of said region; and

wherein determining said contrast comprises;

selecting a first portion of said region;

determining a first average gray-level value of said first portion;

selecting a second portion of said at least one thresholded image adjacent to said region;

determining a second average gray-level value of said second portion; and

determining said contrast using said first and second average gray-level values.

7. A method as recited in claim 6, wherein determining said circularity comprises:

determining an area of said region;

determining a centroid of said region;

placing a circle having said area on said region centered on said centroid;

determining a portion of said region within said circle; and

determining a ratio of said portion to said area as said circularity.

8. A method as recited in claim 6, wherein selecting said second portion comprises:

placing a plurality of blocks having a predetermined size near a plurality of predetermined positions of said region; and

determining said second average gray-level value using said plurality of blocks.

9. A method as recited in claim 6, wherein determining said contrast comprises determining a normalized difference between said first and second average gray-level values.

10. A method as recited in claim 6, comprising:

determining at least one of said circularity, area and contrast of said region using a cumulative histogram.

11. A method as recited in claim 10, comprising:

determining a cutoff value for at least one of said circularity, area and contrast of said region using said cumulative histogram.

12. A method as recited in claim 10, wherein said cumulative histogram is given as C(p) and determined by:

##EQU2## where;

p is a value of one of said circularity, area and contrast,

p_min is a minimum value of at least one of said circularity, area and contrast,

p_max is a the maximum value of at least one of said circularity, area and contrast, and

F(p'"'"') is a frequency of occurrence of at least one of said circularity, area and contrast at p.

13. A method as recited in claim 1, comprising:

processing said image to produce a processed image;

selecting a second region in said processed image encompassing said first region;

identifying a suspicious region in said second region corresponding to said mass;

wherein identifying said suspicious region comprises;

determining a maximum gray-level value in said second region; and

region growing using said maximum gray-level value to produce a third region.

14. A method as recited in claim 13, further comprising:

determining at least two of a circularity, an area, and a contrast of said third region.

15. A method as recited in claim 14, wherein determining said circularity comprises:

determining an area of said third region;

determining a centroid of said third region;

placing a circle having said area on said third region centered on said centroid;

determining a portion of said third region within said circle; and

determining a ratio of said portion to said area as said circularity.

16. A method as recited in claim 14, wherein determining said contrast comprises:

selecting a first portion of said region;

determining a first average gray-level value of said first portion;

selecting a second portion of said at least one thresholded image adjacent to said region;

determining a second average gray-level value of said second portion; and

determining said contrast using said first and second average gray-level values.

17. A method as recited in claim 16, wherein determining said contrast comprises determining a normalized difference between said first and second average gray-level values.

18. A method as recited in claim 14, comprising:

determining at least one of said circularity, area and contrast of said region using a cumulative histogram.

19. A method as recited in claim 18, comprising:

determining a cutoff value for at least one of said circularity, area and contrast of said region using said cumulative histogram.

20. A method as recited in claim 18, wherein said cumulative histogram is given as C(p) and determined by:

##EQU3## where;

p is a value of one of said circularity, area and contrast,

p_min is a minimum value of at least one of said circularity, area and contrast,

p_max is a the maximum value of at least one of said circularity, area and contrast, and

F(p'"'"') is a frequency of occurrence of at least one of said circularity, area and contrast at p.

26. A method as recited in claim 1, comprising:

classifying said mass; and

determining one of a likelihood of malignancy of said mass and whether said mass is a false-positive;

wherein;

classifying said mass comprises

determining a plurality of features of said mass and discriminating between said mass and a nonmass using said features;

determining said plurality of said features comprises

determining at least one of a geometric feature, an intensity feature and a gradient feature;

determining said geometric feature comprises

determining at least one of size, circularity, margin irregularity and compactness of said mass;

determining said intensity feature comprises determining at least one of a contrast of said mass, an average gray-level value of said mass, a first standard deviation of said average gray-level value and a ratio of said average pixel value to said first standard deviation; and

determining said gradient feature comprises determining at least one of an average gradient of said mass and a second standard deviation of said average gradient.

27. A method as recited in claim 20, comprising:

inputting said features to a one of a neural network and a rule-based system trained to detect masses.

28. A method as recited in claim 13, comprising:

determining a plurality of features of said third region; and

discriminating between said mass and a nonmass using said features.

29. A method as recited in claim 28, comprising:

inputting said features to one of a neural network and a rule-based system trained to detect masses.

30. A method as recited in claim 28, wherein determining said plurality of said features comprises:

determining at least one of a geometric feature, an intensity feature and a gradient feature.

31. A method as recited in claim 30, wherein:

determining said geometric feature comprises determining at least one of size, circularity, margin irregularity and compactness of said third region;

determining said intensity feature comprises determining at least one of a contrast of said third region, an average gray-level value of said third region, a first standard deviation of said average gray-level value and a ratio of said average pixel value to said first standard deviation; and

determining said gradient feature comprises determining at least one of an average gradient of said third region and a second standard deviation of said average gradient.

32. A method as recited in claim 1, comprising:

extracting a suspect mass from said image;

determining an approximate center of said suspect mass;

processing said suspect mass to produce a processed suspect mass; and

region growing based upon said processed suspect mass to produce a grown region.

33. A method as recited in claim 32, comprising:

analyzing at least one of a region area, region circularity and region margin irregularity of said grown region; and

determining a transition point at which growing of said grown region terminates.

34. A method as recited in claim 33, comprising:

determining a contour of said grown region based upon said transition point.

21. A method for detecting and classifying a mass in a human body, comprising:

obtaining an image of a portion of said human body;

detecting whether said image contains a first region potentially being said mass;

processing said image to produce a processed image;

selecting a second region in said processed image encompassing said first region;

identifying a suspicious region in said second region corresponding to said mass;

wherein identifying said suspicious region comprises;

determining a maximum gray-level value to produce a third region;

wherein region growing comprises;

analyzing at least one of a region area, region circularity and region margin irregularity of said third region as it grows; and

determining a transition point at which growing of said third region terminates.

22. A method as recited in claim 21, comprising:

determining a contour of said third region based upon said transition point.

23. A method as recited in claim 21, comprising:

analyzing said region area, said region circularity and said region margin irregularity of said third region as it grows;

determining transition points at which growing of said third region terminates based upon analyzing said region area, said region circularity and said region margin irregularity, respectively;

considering said region circularity and said region margin irregularity as a function of contrast of said third region as it is grown; and

determining a contour based upon a determined transition point based upon said steps of determining said transition points and considering said region.

24. A method as recited in claim 21, wherein determining said region circularity comprises:

determining an area of said third region;

determining a centroid of said third region;

placing a circle having said area on said third region centered on said centroid;

determining a portion of said third region within said circle; and

determining a ratio of said portion to said area as said circularity.

25. A method as recited in claim 21, wherein determining said region margin irregularity comprises:

determining an area of said third region;

determining a first perimeter of said third region;

determining a second perimeter of a circle having said area; and

determining a ratio of said second perimeter to said first perimeter.

35. A method for classifying a mass in a human body, comprising:

obtaining an image of a portion of said human body;

classifying said mass;

determining one of a likelihood of malignancy of said mass and whether said mass is a false-positive;

extracting a suspect mass from said image;

determining an approximate center of said suspect mass;

processing said suspect mass to produce a processed suspect mass;

region growing based upon said processed suspect mass to produce a grown region;

analyzing at least one of a region area, region circularity and region margin irregularity of said grown region;

determining a transition point at which growing of said grown region terminates;

analyzing said region area, said region circularity and said region margin irregularity of said grown region as it grows;

determining transition points at which growing of said grown region terminates based upon analyzing said region area, said region circularity and said region margin irregularity, respectively; and

considering said region circularity and said region margin irregularity as a function of contrast of said grown region as it is grown; and

determining a contour of said grown region based upon a determined transition point based upon said steps of determining said transition points and considering said region.

36. A method as recited in claim 35, wherein determining said region circularity comprises:

determining an area of said grown region;

determining a centroid of said grown region;

placing a circle having said area on said grown region centered on said centroid;

determining a portion of said area of said grown region within said circle; and

determining a ratio of said portion to said area as said circularity.

37. A method as recited in claim 35, wherein determining said region margin irregularity comprises:

determining an area of said grown region;

determining a first perimeter of said grown region;

determining a second perimeter of a circle having said area; and

determining a ratio of said second perimeter to said first perimeter.

38. A method as recited in claim 35, wherein said step of processing comprises at least one of background-trend correcting and histogram equalizing said suspect mass.

39. A method for classifying a mass in a human body, comprising:

obtaining an image of a portion of said human body;

classifying said mass;

determining one of a likelihood of malignancy of said mass and whether said mass is a false-positive;

selecting a region of interest containing said mass;

performing cumulative edge-gradient histogram analysis on said region of interest;

determining a geometric feature of said suspected mass;

determining a gradient feature of said suspected mass; and

determining whether said suspected mass is malignant based upon said cumulative edge-gradient histogram analysis, said geometric feature and said gradient feature.

40. A method as recited in claim 39, wherein:

said region of interest contains a plurality of pixels; and

performing cumulative edge-gradient histogram analysis comprises;

determining a maximum gradient of each pixel in said region of interest;

determining an angle of said maximum gradient for each said pixel to at least of a radial direction and an x-axis direction;

calculating a cumulative edge-gradient histogram using said maximum gradients and said angles.

41. A method as recited in claim 40, comprising:

determining FWHM of said cumulative edge-gradient histogram;

determining a standard deviation of said cumulative edge-gradient histogram;

determining at least one of a minimum and a maximum of said cumulative edge-gradient histogram; and

determining an average radial edge gradient of said cumulative edge-gradient histogram.

42. A method as recited in claim 40, comprising:

oval correcting said cumulative edge-gradient histogram.

43. A method as recited in claim 41, comprising:

discriminating between a benign mass and a malignant mass based upon said FWHM, said standard deviation, said at least one of a minimum and a maximum, and average radial edge gradient.

44. A method as recited in claim 43, wherein said discriminating comprises discriminating between said benign mass and said maglinant mass using one of a neural network and a rule-based scheme.

45. A method as recited in claim 41, comprising:

determining at least one of a geometric feature, an intensity feature and a gradient feature;

discriminating at least one of between said mass and a nonmass and between a benign mass and a maglinant mass based on said at least one of a geometric feature, an intensity feature and a gradient feature and said FWHM, said standard deviation, said at least one of a minimum and a maximum, and average radial edge gradient.

46. A method as recited in claim 45, comprising discriminating at least one of between said mass and said nonmass and between said benign mass and said malignant mass using one of a neural network and a rule-based system.

47. A method as recited in claim 39, comprising:

determining a plurality of features of said mass;

merging said features; and

discriminating said mass as one of a benign mass and a malignant mass based upon said features.

48. A method as recited in claim 47, wherein said discriminating comprises discriminating said mass as one of said benign mass and said malignant mass using one of a neural network and a rule-based scheme.

49. A method of detecting a mass in a mammogram, comprising:

obtaining a digital mammogram;

bilaterally subtracting said digital mammogram to obtain a runlength image;

performing multi-gray-level threshold analysis on said runlength image;

detecting a region suspected of being said mass;

selecting a region of interest containing said region suspected of being said mass;

performing cumulative edge-gradient histogram analysis on said region of interest;

determining a geometric feature of said region;

determining a gradient feature of said region; and

determining one of whether said mass is malignant and whether said mass is a false-positive based upon said cumulative edge-gradient histogram analysis, said geometric feature and said gradient feature.

50. A method as recited in claim 49, comprising:

obtaining a pair of digital mammograms;

bilaterally subtracting said pair of digital mammograms to produce said runlength image; and

performing a border-distance test on said region.

51. A method as recited in claim 50, comprising:

detecting said region having a plurality of pixels;

wherein performing said border-distance test comprises;

determining respective distances of each of said plurality of pixels of said region to a closest border point of a breast in said image;

finding an average distance of said distances; and

disregarding said region if said average distance is less than a predetermined value.

52. A method as recited in claim 49, comprising:

processing said digital mammogram to produce a processed mammogram;

detecting said suspect region in said processed mammogram;

selecting a region of interest in said digital mammogram containing a first region having a plurality of pixels and corresponding to said suspect region;

region growing using one of said plurality of pixels having a maximum gray-level value to produce a grown region; and

terminating said region growing when a gray-level of said grown region reaches a predetermined gray-level value.

53. A method as recited in claim 52, comprising:

determining at least one of circularity, area and contrast of said grown region.

54. A method as recited in claim 53, wherein:

determining said circularity comprises;

determining an area of said grown region;

determining a centroid of said grown region;

placing a circle having said area on said grown region centered on said centroid;

determining a portion of said grown region within said circle; and

determining a ratio of said portion to said area as said circularity; and

wherein determining said contrast comprises;

selecting a first portion of said grown region;

determining a first average gray-level value of said first portion;

selecting a second portion of said digital mammogram adjacent to said grown region;

determining a second average gray-level value of said second portion; and

determining said contrast using said first and second average gray-level values.

55. A system for detecting a mass in an image, comprising:

an image acquisition device;

a segmenting circuit connected to said image acquisition device and producing a run-length image;

a multi-gray level thresholding circuit connected to said segmenting circuit;

a size analysis circuit connected to said multi-gray level thresholding circuit

a mass detection circuit connected to said size analysis circuit;

a neural network circuit trained to analyze a mass connected to said mass detection circuit; and

a display connected to said neural network circuit.

56. A system as recited in claim 55, wherein said multi-gray level thresholding circuit comprises means for thresholding said image at a plurality of gray-level threshold values to produce a corresponding plurality of thresholded images.

57. A system as recited in claim 55, further comprising a bilateral subtraction circuit connected to said image acquisition device and said multi-gray level thresholding circuit.

58. A system as recited in claim 55, further comprising:

a location circuit;

a mass extraction circuit connected to said location circuit;

a feature extraction circuit connected to said mass extraction circuit; and

a second neural network trained to analyze masses based upon input features connected to said feature extraction circuit.

62. A system as recited in claim 55, wherein said size analysis circuit comprises:

means for determining a contrast of a region suspected of being said mass, said means comprising;

means for selecting a first portion of said region

means for determining a first average gray-level value of said first portion,

means for selecting a second portion of said at least one thresholded image adjacent to said region,

means for determining a second average gray-level value of said second portion, and

means for determining said contrast using said first and second average gray-level values.

59. A system for detecting a mass in an image, comprising:

an image acquisition device;

a runlength image circuit connected to said image acquisition device;

a multi-gray level thresholding circuit connected to said runlength image circuit;

a size analysis circuit connected to said multi-gray level thresholding circuit;

a mass detection circuit connected to said size analysis circuit;

a classification circuit connected to said mass detection circuit; and

a display connected to said neural network circuit;

wherein said classification circuit comprises

at least one of;

means for determining a geometric feature of said mass having means for determining at least one of size, circularity, margin irregularity and compactness of said mass;

means for determining an intensity feature having means for determining at least one of a contrast of said mass, an average gray-level value of said mass, a first standard deviation of said average gray-level value and a ratio of said average pixel value to said first standard deviation; and

means for determining a gradient feature having means for determining at least one of an average gradient of said mass and a second standard deviation of said average gradient.

60. A system for detecting and classifying a mass in a human body, comprising:

an image acquisition device;

a segmenting circuit connected to said image acquisition device;

a first selection circuit connected to said circuit for selection a first region in said image suspected of being said mass;

an image processing circuit;

a second selection circuit connected to said image processing circuit for selecting a second region in said processed image encompassing said first region; and

a mass identification circuit connected to said second selection circuit having;

means for determining a maximum gray-level value in said second region; and

means for region growing using said maximum gray-level value to produce a third region, comprising,

means for analyzing at least one of a region area, a region circularity and a region margin irregularity of said third region, and

means for determining a transition point at which growing of said third region terminates.

61. A system for detecting and classifying a mass in a human body, comprising:

an image acquisition device;

a segmenting circuit connected to said image acquisition device;

a mass detection circuit connected to said segmenting circuit;

a mass classification circuit connected to said mass detection circuit;

means for determining a likelihood of malignancy of said mass;

means for selecting a region of interest containing said mass;

a cumulative edge-gradient histogram circuit connected to said means for selecting;

a geometric feature circuit connected to said histogram circuit;

a gradient feature circuit connected to said histogram circuit; and

means for determining whether said suspected mass is malignant using said cumulative edge-gradient histogram circuit, said geometric feature circuit and said gradient feature circuit.

63. A system for classifying a mass in a human body, comprising:

an image acquisition circuit; and

a classification circuit;

wherein said classification circuit comprises;

means for determining one of a likelihood of malignancy of said mass and whether said mass is a false-positive;

means for extracting a suspect mass from said image;

means for determining an approximate center of said suspect mass;

means for processing said suspect mass to produce a processed suspect mass;

a region growing circuit to produce a grown region;

means for analyzing at least one of a region area, region circularity and region margin irregularity of said grown region;

means for determining a transition point at which growing of said grown region terminates;

means for analyzing said region area, said region circularity and said region margin irregularity of said grown region as it grows;

means for determining transition points at which growing of said grown region terminates based upon analyzing said region area, said region circularity and said region margin irregularity, respectively;

means for considering said region circularity and said region margin irregularity as a function of contrast of said grown region as it is grown; and

means for determining a contour of said grown region based upon a determined transition point based upon said steps of determining said transition points and considering said region.

64. A system for classifying a mass in a human body, comprising:

an image acquisition circuit; and

a classification circuit;

wherein said classification circuit comprises;

means for determining one of a likelihood of malignancy of said mass and whether said mass is a false-positive;

a region of interest selection circuit;

a cumulative edge-gradient histogram circuit;

a geometric feature circuit;

a gradient feature circuit; and

means for determining whether said suspected mass is malignant based upon an output of said cumulative edge-gradient histogram circuit, said geometric feature and said gradient feature.