US6185320B1
Expired / Lapsed
US
⏱️ 0.0 years remaining
Method and system for detection of lesions in medical images
Assignee: Arch Development Corp
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Filing: 1997-12-01
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Granted: 2001-02-06
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Expires: 2017-12-01
Abstract
Abstract A method and system for the automated detection of lesions in medical images. Medical images, such as mammograms are segmented and optionally processing with peripheral enhancement and/or modified median filtering. A modified morphological open operation and filtering with a modified mass filter are performed for the initial detection of circumscribed lesions. Then, the lesions are matched using a deformable shape template with Fourier descriptors. Characterization of the match is done using simulated annealing, and measuring the circularity and density characteristics of the suspected lesion. The procedure is performed iteratively at different spatial resolution in which at each resolution step a specific lesion size is detected. The detection of the lesion leads to a localization of a suspicious region and thus the likelihood of cancer.
Inventors
- 👤 Ulrich Bick
- 👤 Maryellen L. Giger
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 16:46
CPC Classifications
G; G06; G06T; G06T7/00; G06T7/0002; G06T7/0012; G06T7/10; G06T7/11; G06T7/12; G06T7/149; G06T7/155; G06T7/187; G06V; G06V10/00; G06V10/40; G06V10/46; G06V10/478; G06T2207/00; G06T2207/20; G06T2207/20048; G06T2207/20056; G06T2207/20112; G06T2207/20156; G06T2207/30; G06T2207/30004; G06T2207/30068; G06T2207/30096
Claims Analysis
Total Claims: 58
Claim 1
Independent Claim
108 words
A method for the automated detection of a mass lesion in mammographic images, comprising: generating a mammogram; segmenting said mammogram to produce a segmented mammogram; performing a modified morphological operation on said segmented mammogram; and detecting whether said lesion is present in said mammogram; wherein said modified morphological operation comprises: determining a first gray-level value of a pixel in said segmented mammogram using morphological processing; determining a difference between a second gray level value of said pixel before performing said morphological operation and said first value; and substituting said first value for said second value in said segmented mammogram if said difference is greater than a threshold value.
Claim 2
Independent Claim
108 words
A method for the automated detection of a mass lesion in mammographic images, comprising: generating a mammogram; segmenting said mammogram to produce a segmented mammogram; performing a modified morphological operation on said segmented mammogram; and detecting whether said lesion is present in said mammogram; wherein said modified morphological operation comprises: determining a first gray-level value of a pixel in said segmented mammogram using morphological processing; determining a difference between a second gray level value of said pixel before performing said morphological operation and said first value; and substituting said first value for said second value in said segmented mammogram if said difference is greater than a threshold value.
Claim 3
Dependent Claim
20 words
A method as recited in claim 1 , comprising performing said modified morphological operation using a plurality of filter sizes.
Claim 4
Dependent Claim
20 words
A method as recited in claim 1 , wherein segmenting said mammogram comprises segmenting a breast border from said mammogram.
Claim 5
Dependent Claim
51 words
A method as recited in claim 1 , wherein segmenting said mammogram comprises: performing noise analysis on said mammogram; applying a gray value range operator; performing global gray-level histogram analysis to provide a thresholded image; region growing using said thresholded image; performing a morphological erosion operation; and determining a distance map.
Claim 6
Dependent Claim
29 words
A method as recited in claim 1 , comprising: peripherally enhancing said segmented image to produce an enhanced image; and performing said modified morphological operation using said enhanced image.
Claim 7
Dependent Claim
76 words
A method as recited in claim 5 , wherein said peripherally enhancing said segmented image comprises: determining average gray-level values of pixels as a function of distance from a border of a breast in said mammogram; fitting an enhancement curve to said gray-level values; reversing a fit of said enhancement curve to obtain a second enhancement curve; and adding said second enhancement curve to a curve of said average gray-level values as a function of distance.
Claim 8
Dependent Claim
28 words
A method as recited in claim 1 , comprising: processing said segmented image to produce a processed image; and performing said modified morphological operation using said processed image.
Claim 9
Dependent Claim
97 words
A method as recited in claim 7 , wherein processing said segmented image comprises: obtaining a gray-level value in said segmented image at a first pixel; determining a local gray-level minimum in said segmented image in a neighborhood of pixels disposed around said first pixel; substituting said local gray-level minimum for said gray-level value of said first pixel when a first difference between said minimum and said gray-level value is greater than a first gray-level threshold value; and repeating said obtaining, determining and substituting steps for selected pixels of said segmented image to obtain a processed image.
Claim 10
Dependent Claim
31 words
A method as recited in claim 8 , comprising: determining whether a pixel in said processed image is a seed pixel; and performing said modified morphological operation using said seed pixel.
Claim 11
Dependent Claim
82 words
A method as recited in claim 9 , wherein determining whether said pixel is said seed pixel comprises: determining a local gray-level maximum of said neighborhood; a first step of determining whether a first difference between said gray-level value and said local gray level minimum is less than a second difference between said local gray-level maximum and said gray-level value; and a second step determining whether said pixel is within a predetermined distance of a pixel having said local minimum gray-level value.
Claim 12
Dependent Claim
89 words
A method as recited in claim 9 , comprising: performing said modified morphological operation at a selected seed pixel in said segmented image; determining whether a second difference of a first gray-level value of said selected seed pixel before performing said modified morphological operation and a second gray-level value of said selected seed pixel after performing said morphological operation is less than a second gray-level threshold value; replacing said first gray-level value with said second gray-level value when said second difference is greater than said second gray-level threshold value.
Claim 13
Dependent Claim
36 words
A method as recited in claim 1 , wherein said detecting step comprises: extracting features using a result of said morphological operation, said mass filtering and said template matching; integrating said features; and classifying said features.
Claim 14
Dependent Claim
47 words
A method as recited in claim 1 , comprising: performing mass filtering after performing said modified morphological operation to produce a filtered image performing template matching using said filtered image; performing thresholding using said filtered image; and detecting said lesions using said template matching and said thresholding.
Claim 15
Dependent Claim
16 words
A method as recited in claim 1 , comprising: performing mass filtering; and performing template matching.
Claim 16
Dependent Claim
43 words
A method as recited in claim 14 , comprising: performing said modified morphological operation, said mass filtering and said template matching serially at a plurality of morphological resolutions; wherein said modified morphological operations performed at said plurality of resolutions are conducted in parallel.
Claim 17
Dependent Claim
42 words
A method as recited in claim 14 , comprising: performing said modified morphological operation, said mass filtering and said template matching serially at a plurality of morphological resolutions; wherein said modified morphological operations performed at said plurality of resolutions are conducted iteratively.
Claim 18
Dependent Claim
20 words
A method as recited in claim 16 , further comprising thresholding a mass filtered image obtained from said mass filtering.
Claim 19
Dependent Claim
33 words
A method as recited in claim 14 , further comprising: thresholding a mass filtered image obtained from said mass filtering to obtained a threshold image; using said threshold image in detecting said lesions.
Claim 20
Dependent Claim
26 words
A method as recited in claim 18 , wherein: said detecting step comprises performing feature extraction; and said threshold image is used in said feature extraction.
Claim 21
Dependent Claim
31 words
A method as recited in claim 14 , wherein performing said mass filtering comprises: using a ring-shaped kernel; and determining a filter value based upon a second derivative of edge orientation.
Claim 22
Dependent Claim
30 words
A method as recited in claim 14 , wherein said template matching comprises: determining a deformable shape template; and fitting said shape template to a shape of a suspected lesion.
Claim 23
Dependent Claim
34 words
A method as recited in claim 21 , comprising: determining said deformable shape template using Fourier descriptors; and varying said descriptors to dynamically fit said shape template to said shape of said suspected lesion.
Claim 24
Dependent Claim
48 words
A method as recited in claim 21 , comprising: determining said shape template as an inverse Fourier transform having a plurality of Fourier terms; varying said Fourier terms to fit said shape template to said shape of said suspected lesion; and outputting a contour of said suspected lesion.
Claim 25
Dependent Claim
21 words
A method as recited in claim 23 , wherein varying said Fourier terms comprises minimizing a cost function using simulated annealing.
Claim 26
Dependent Claim
37 words
A method as recited in claim 23 , comprising: varying a center location, a size, an orientation, a ratio of a long axis to a short axis, and a degree of asymmetry of said inverse Fourier transform.
Claim 27
Dependent Claim
52 words
A method as recited in claim 21 , comprising: determining a center of a suspected lesion as a maximum mass filter value; determining edges of said suspected lesion as one of a derivative and a second derivative of said image after mass-filtering; and fitting said template using said center and said edges.
Claim 28
Dependent Claim
59 words
A method as recited in claim 14 , wherein performing said mass filtering comprises: selecting an initial kernel size of a mass filter; performing mass filtering using said initial kernel size to produce a mass-filtered image; varying said kernel size up to a maximum kernel size; and performing mass filtering at each kernel size and producing corresponding mass-filter images.
Claim 29
Dependent Claim
31 words
A method as recited in claim 27 , comprising: performing said template matching on each of said mass-filter images; detecting suspected lesions in said mass-filtered images; and integrating said suspected lesions.
Claim 30
Dependent Claim
32 words
A method as recited in claim 28 , wherein integrating said suspected images comprises: determining whether suspected lesions in said mass-filtered images spatially overlap; and eliminating selected ones of overlapping suspected lesions.
Claim 31
Dependent Claim
33 words
A method as recited in claim 29 , comprising: determining a radial index for each of said overlapping suspected lesions; and eliminating one of said overlapping suspected lesions having a smaller radial index.
Claim 32
Dependent Claim
26 words
A method as recited in claim 1 , comprising: selecting a region of interest containing said lesion; and performing feature extraction using said region of interest.
Claim 33
Dependent Claim
21 words
A method as recited in claim 31 , comprising: selecting said region of interest containing a lesion identified using template matching.
Claim 34
Dependent Claim
56 words
A method as recited in claim 31 , wherein performing feature extraction comprises: determining a gradient of said region of interest; determining an orientation of said region of interest; determining a gradient index of said region of interest; determining a contrast of said region of interest; and determining an elongation factor of said region of interest.
Claim 35
Dependent Claim
23 words
A method as recited in claim 33 , comprising: thresholding at least one of said gradient, orientation, gradient index, contrast and elongation factor.
Claim 36
Dependent Claim
21 words
A method as recited in claim 34 , comprising: determining a final filter value as a sum of said filter values.
Claim 37
Dependent Claim
32 words
A method as recited in claim 35 , comprising: determining said final filter value using a plurality of said orientation bins excluding a predetermined number of said orientation bins having highest values.
Claim 38
Independent Claim
138 words
A method for the automated detection of mass lesions in mammographic images, comprising: generating a mammogram; segmenting said mammogram to produce a segmented mammogram; performing a morphological operation on said segmented mammogram; performing mass filtering; and detecting whether a lesion is present in said image; wherein performing said mass filtering comprises: determining a filter value for a plurality of edge orientation bins; and wherein determining said filter value comprises: calculating said filter value using: f(B i )=(1/N)Σ P in K {MAX(0, cosφ)*EDGE STRENGTH (at P)} where: f(B i ) is said filter value at an orientation bin; K is a filter kernel; P is a neighbor point in K; N is a number of points in K; and Φ is an angle between a gradient vector and a connection line between said neighbor point and a center point.
Claim 39
Independent Claim
138 words
A method for the automated detection of mass lesions in mammographic images, comprising: generating a mammogram; segmenting said mammogram to produce a segmented mammogram; performing a morphological operation on said segmented mammogram; performing mass filtering; and detecting whether a lesion is present in said image; wherein performing said mass filtering comprises: determining a filter value for a plurality of edge orientation bins; and wherein determining said filter value comprises: calculating said filter value using: f(B i )=(1/N)Σ P in K {MAX(0, cosφ)*EDGE STRENGTH (at P)} where: f(B i ) is said filter value at an orientation bin; K is a filter kernel; P is a neighbor point in K; N is a number of points in K; and Φ is an angle between a gradient vector and a connection line between said neighbor point and a center point.
Claim 40
Independent Claim
131 words
A system for detecting lesions in a medical image, comprising: a data input device; a segmenting circuit connected to said input device; a modified morphological circuit connected to receive an output of said segmenting circuit; and a lesion detecting circuit operably connected to the modified morphological circuits; wherein said modified morphological circuit comprises: a gray level value determining circuit adapted to determine a first gray-level value of a pixel in said segmented mammogram using morphological processing; a difference circuit adapted to obtain a difference between a second gray level value of said pixel before performing said morphological operation and said first value; and a pixel substitution circuit adapted to substitute said first value for said second value in said segmented mammogram if said difference is greater than a gray-level threshold value.
Claim 41
Independent Claim
131 words
A system for detecting lesions in a medical image, comprising: a data input device; a segmenting circuit connected to said input device; a modified morphological circuit connected to receive an output of said segmenting circuit; and a lesion detecting circuit operably connected to the modified morphological circuits; wherein said modified morphological circuit comprises: a gray level value determining circuit adapted to determine a first gray-level value of a pixel in said segmented mammogram using morphological processing; a difference circuit adapted to obtain a difference between a second gray level value of said pixel before performing said morphological operation and said first value; and a pixel substitution circuit adapted to substitute said first value for said second value in said segmented mammogram if said difference is greater than a gray-level threshold value.
Claim 42
Dependent Claim
34 words
A system as recited in claim 38 , comprising: a peripheral enhancement circuit connected to said segmenting circuit; wherein said morphological circuit is further connected to receive an output of said peripheral enhancement circuit.
Claim 43
Dependent Claim
52 words
A system as recited in claim 39 , comprising: a sampling circuit connected to said segmenting, peripheral enhancement and morphological circuits; wherein said sampling circuit is connected to receive said outputs of said segmenting and peripheral enhancement circuits; and said morphological circuit is connected to receive an output of said sampling circuit.
Claim 44
Dependent Claim
26 words
A system as recited in claim 38 , wherein said morphological circuit comprises: means for performing a modified morphological operation using a plurality of filter sizes.
Claim 45
Dependent Claim
39 words
A system as recited in claim 38 , wherein said morphological circuit comprises: means for determining whether a pixel in said medical image is a seed pixel; and means for performing a modified morphological operation using said seed pixel.
Claim 46
Dependent Claim
93 words
A system as recited in claim 38 , wherein: said gray level value determining circuit comprises means for obtaining a gray-level value in said medical image at a first pixel; said difference circuit comprises means for determining a local gray-level minimum in said segmented image in a neighborhood of pixels disposed around said first pixel; and said pixel substitution circuit comprises means for substituting said local gray-level minimum for said gray-level value of said first pixel when a difference between said minimum and said gray-level value is greater than said gray-level threshold value.
Claim 47
Dependent Claim
76 words
A system as recited in claim 43 , comprising: means for determining a local gray-level maximum of said neighborhood of pixels; first means for determining whether a first difference between said gray-level value and said local gray level minimum is less than a second difference between said local gray-level maximum and said gray-level value; and second means for determining whether said pixel is within a predetermined distance of a pixel having said local minimum gray-level value.
Claim 48
Dependent Claim
52 words
A system as recited in claim 38 , comprising: a mass filter circuit connected to said modified morphological circuit; a template matching circuit connected to said mass filter circuit; and a feature analysis circuit connected to said template matching circuit; wherein said lesion detecting circuit is connected to the feature analysis circuit.
Claim 49
Dependent Claim
41 words
A system as recited in claim 45 , wherein said template matching circuit comprises: a Fourier descriptor generating circuit; an edge generating circuit connected to said Fourier descriptor generating circuit; and a simulated annealing circuit connected to said edge generating circuit.
Claim 50
Dependent Claim
42 words
A system as recited in claim 45 , wherein said mass filter circuit comprises: means for determining a filter value for a plurality of edge orientation bins; and means for determining a final filter value as a sum of said filter values.
Claim 51
Dependent Claim
35 words
A system as recited in claim 45 , wherein said template matching circuit comprises: means for determining a deformable shape template; and means for fitting said shape template to a shape of a suspected lesion.
Claim 52
Dependent Claim
44 words
A system as recited in claim 48 , wherein said template matching circuit further comprises: means for determining said deformable shape template using Fourier descriptors; and means for varying said descriptors to dynamically fit said shape template to said shape of said suspected lesion.
Claim 53
Dependent Claim
65 words
A system as recited in claim 48 , wherein said template matching circuit further comprises: means for determining said shape template as an inverse Fourier transform having a plurality of Fourier terms; means for varying said Fourier terms to fit said shape template to fit said shape template to said shape of said suspected lesion; and means for outputting a contour of said suspected lesion.
Claim 54
Dependent Claim
45 words
A system as recited in claim 50 , wherein said template matching circuit further comprises: means for varying a center location, a size, an orientation, a ratio of a long axis to a short axis, and a degree of asymmetry of said inverse Fourier transform.
Claim 55
Dependent Claim
50 words
A system as recited in claim 45 , wherein said template matching circuit further comprises: means for determining a center of a suspected lesion as a maximum mass filter value; means for determining edges of said suspected lesion; and means for fitting said template using said center and said edges.
Claim 56
Dependent Claim
67 words
A system as recited in claim 45 , wherein said feature extraction circuit comprises: means for determining a gradient of said region of interest; means for determining an orientation of said region of interest; means for determining a gradient index of said region of interest; means for determining a contrast of said region of interest; and means for determining an elongation factor of said region of interest.
Claim 57
Dependent Claim
18 words
A system as recited in claim 45 , wherein said feature extraction circuit further comprises a thresholding circuit.
Claim 58
Dependent Claim
67 words
A system as recited in claim 45 , wherein said mass filter circuit comprises: means for selecting an initial kernel size of a mass filter; means for performing mass filtering using said initial kernel size to produce a mass-filtered image; means for varying said kernel size up to a maximum kernel size; and means for performing mass filtering at each kernel size and producing corresponding mass-filter images.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2004-02-06 | 2004-08-07 | 2005-02-06 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2008-02-06 | 2008-08-07 | 2009-02-06 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2012-02-06 | 2012-08-07 | 2013-02-06 | $7700.00 | Window Closed / Lapsed |