US5832103A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Automated method and system for improved computerized detection and classification of massess in mammograms
Assignee: Arch Development Corp
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Filing: 1995-08-16
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Granted: 1998-11-03
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Expires: 2015-08-16
Abstract
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.
Inventors
- 👤 Maryellen L. Giger
- 👤 Kunio Doi
- 👤 Ping Lu
- 👤 Zhimin Huo
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-24 16:18
CPC Classifications
G; G06; G06T; G06T7/00; G06T7/0002; G06T7/0012; G06V; G06V20/00; G06V20/60; G06V20/69
Claims Analysis
Total Claims: 75
Claim 1
Independent Claim
78 words
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.
Claim 2
Independent Claim
78 words
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.
Claim 3
Dependent Claim
30 words
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.
Claim 4
Dependent Claim
54 words
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.
Claim 5
Dependent Claim
45 words
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.
Claim 6
Dependent Claim
45 words
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.
Claim 7
Dependent Claim
92 words
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.
Claim 8
Dependent Claim
59 words
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.
Claim 9
Dependent Claim
43 words
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.
Claim 10
Dependent Claim
24 words
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.
Claim 11
Dependent Claim
25 words
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.
Claim 12
Dependent Claim
29 words
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.
Claim 13
Dependent Claim
84 words
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.
Claim 14
Dependent Claim
68 words
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.
Claim 15
Dependent Claim
25 words
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.
Claim 16
Dependent Claim
63 words
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.
Claim 17
Dependent Claim
66 words
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.
Claim 18
Dependent Claim
24 words
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.
Claim 19
Dependent Claim
25 words
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.
Claim 20
Dependent Claim
29 words
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.
Claim 21
Dependent Claim
84 words
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.
Claim 22
Independent Claim
121 words
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.
Claim 23
Independent Claim
121 words
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.
Claim 24
Dependent Claim
20 words
A method as recited in claim 21, comprising: determining a contour of said third region based upon said transition point.
Claim 25
Dependent Claim
98 words
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.
Claim 26
Dependent Claim
64 words
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.
Claim 27
Dependent Claim
51 words
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.
Claim 28
Dependent Claim
164 words
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.
Claim 29
Dependent Claim
26 words
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.
Claim 30
Dependent Claim
28 words
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.
Claim 31
Dependent Claim
25 words
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.
Claim 32
Dependent Claim
30 words
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.
Claim 33
Dependent Claim
101 words
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.
Claim 34
Dependent Claim
47 words
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.
Claim 35
Dependent Claim
39 words
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.
Claim 36
Dependent Claim
20 words
A method as recited in claim 33, comprising: determining a contour of said grown region based upon said transition point.
Claim 37
Independent Claim
204 words
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.
Claim 38
Independent Claim
204 words
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.
Claim 39
Dependent Claim
67 words
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.
Claim 40
Dependent Claim
51 words
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.
Claim 41
Dependent Claim
25 words
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.
Claim 42
Independent Claim
97 words
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.
Claim 43
Independent Claim
97 words
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.
Claim 44
Dependent Claim
70 words
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.
Claim 45
Dependent Claim
51 words
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.
Claim 46
Dependent Claim
14 words
A method as recited in claim 40, comprising: oval correcting said cumulative edge-gradient histogram.
Claim 47
Dependent Claim
39 words
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.
Claim 48
Dependent Claim
30 words
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.
Claim 49
Dependent Claim
81 words
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.
Claim 50
Dependent Claim
38 words
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.
Claim 51
Dependent Claim
37 words
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.
Claim 52
Dependent Claim
34 words
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.
Claim 53
Independent Claim
107 words
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.
Claim 54
Independent Claim
107 words
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.
Claim 55
Dependent Claim
34 words
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.
Claim 56
Dependent Claim
67 words
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.
Claim 57
Dependent Claim
85 words
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.
Claim 58
Dependent Claim
21 words
A method as recited in claim 52, comprising: determining at least one of circularity, area and contrast of said grown region.
Claim 59
Dependent Claim
122 words
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.
Claim 60
Independent Claim
83 words
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.
Claim 61
Independent Claim
83 words
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.
Claim 62
Dependent Claim
34 words
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.
Claim 63
Dependent Claim
25 words
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.
Claim 64
Dependent Claim
50 words
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.
Claim 65
Independent Claim
187 words
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.
Claim 66
Independent Claim
187 words
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.
Claim 67
Independent Claim
150 words
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.
Claim 68
Independent Claim
150 words
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.
Claim 69
Independent Claim
119 words
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.
Claim 70
Independent Claim
119 words
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.
Claim 71
Dependent Claim
93 words
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.
Claim 72
Independent Claim
219 words
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.
Claim 73
Independent Claim
219 words
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.
Claim 74
Independent Claim
89 words
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.
Claim 75
Independent Claim
89 words
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.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
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| 3.5 Years (Year 4) | 2001-11-02 | 2002-05-04 | 2002-11-03 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-11-02 | 2006-05-04 | 2006-11-03 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-11-02 | 2010-05-04 | 2010-11-03 | $7700.00 | Window Closed / Lapsed |