Patent US5832103A successfully refreshed from Google Patents.
US5832103A
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US
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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 17:32
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.
References Cited (Backward Citations)
US5245539A
Cited
US5079698A
Cited
EP0405455A2
Cited
EP0405456A2
Cited
US5133020A
Cited
EP0424912A2
Cited
US5212637A
Cited
US5297036A
Cited
US5331550A
Cited
US5260871A
Cited
US5289374A
Cited
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 |