US6389157B2
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Joint optimization of parameters for the detection of clustered microcalcifications in digital mammograms
Assignee: iCad Inc
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Filing: 2001-01-11
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Granted: 2002-05-14
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Expires: 2021-01-11
Abstract
Abstract A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm. The results of the system are optimally combined with a radiologist's observation of the original mammogram by combining the observations with the results, after the radiologist has first accepted or rejected individual detections reported by the system.
Inventors
- 👤 Steven K. Rogers
- 👤 Randy P. Broussard
- 👤 Edward M. Ochoa
- 👤 Thomas F. Rathbun
- 👤 John E. Rosenstengel
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 22:56
CPC Classifications
B; B25; B25J; B25J15/00; B25J15/04; G; G06; G06F; G06F18/00; G06F18/40; G06F18/41; G06T; G06T5/00; G06T5/20; G06T7/00; G06T7/0002; G06T7/0012; G06V; G06V10/00; G06V10/20; G06V10/28; G06V10/40; G06V10/44; G06V10/443; A; A61; A61B; A61B6/00; A61B6/50; A61B6/502; B29; B29C; B29C2791/00; B29C2791/004; B29C2791/006; B29C2791/007; B29L; B29L2023/00; B29L2023/18; G06V2201/00; G06V2201/03; Y; Y10; Y10S; Y10S128/00; Y10S128/92; Y10S128/922
Claims Analysis
Total Claims: 29
Claim 1
Independent Claim
168 words
A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of: a) optimizing a value for a predetermined filtering algorithm parameter; b) filtering a digital mammogram image to produce a filtered image comprising essentially microcalcifications; c) thresholding the filtered image from step b) to produce a thresholded image; d) forming clusters of microcalcifications in the thresholded image from step c); and e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from nonsuspicious clusters of microcalcifications; wherein said step a) comprises: i) providing an optimizing algorithm; ii) providing a database of training images; and iii) inputting the database of training images into said optimizing algorithm to determine an optimum value for said predetermined filtering algorithm parameter; and wherein said step b) comprises: i) providing a filtering algorithm incorporating the optimized predetermined filtering algorithm parameter; and ii) applying the filtering algorithm to the digital mammogram image to produce a filtered image comprising essentially microcalcifications.
Claim 2
Independent Claim
168 words
A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of: a) optimizing a value for a predetermined filtering algorithm parameter; b) filtering a digital mammogram image to produce a filtered image comprising essentially microcalcifications; c) thresholding the filtered image from step b) to produce a thresholded image; d) forming clusters of microcalcifications in the thresholded image from step c); and e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from nonsuspicious clusters of microcalcifications; wherein said step a) comprises: i) providing an optimizing algorithm; ii) providing a database of training images; and iii) inputting the database of training images into said optimizing algorithm to determine an optimum value for said predetermined filtering algorithm parameter; and wherein said step b) comprises: i) providing a filtering algorithm incorporating the optimized predetermined filtering algorithm parameter; and ii) applying the filtering algorithm to the digital mammogram image to produce a filtered image comprising essentially microcalcifications.
Claim 3
Dependent Claim
24 words
The method of claim 1 wherein said step a) comprises optimizing a parameter corresponding to the size of microcalcifications in the digital mammogram image.
Claim 4
Independent Claim
161 words
A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of: a) optimizing at least one value for a predetermined threshold parameter; b) filtering a digital mammogram image to produce a filtered image comprising essentially microcalcifications; c) thresholding the filtered image from step b) to produce a threshold image; d) forming clusters of microcalcifications in the filtered image from step c); and e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from nonsuspicious clusters of microcalcifications; wherein said step a) comprises: i) providing an optimizing algorithm; ii) providing a database of training images; and iii) inputting the database of training images into said optimizing algorithm to determine optimum values for said predetermined threshold parameters; and wherein said step c) comprises: i) providing a thresholding algorithm incorporating the optimized predetermined threshold parameter; and ii) applying the thresholding algorithm to the filtered image to identify potential microcalcifications.
Claim 5
Independent Claim
161 words
A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of: a) optimizing at least one value for a predetermined threshold parameter; b) filtering a digital mammogram image to produce a filtered image comprising essentially microcalcifications; c) thresholding the filtered image from step b) to produce a threshold image; d) forming clusters of microcalcifications in the filtered image from step c); and e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from nonsuspicious clusters of microcalcifications; wherein said step a) comprises: i) providing an optimizing algorithm; ii) providing a database of training images; and iii) inputting the database of training images into said optimizing algorithm to determine optimum values for said predetermined threshold parameters; and wherein said step c) comprises: i) providing a thresholding algorithm incorporating the optimized predetermined threshold parameter; and ii) applying the thresholding algorithm to the filtered image to identify potential microcalcifications.
Claim 6
Dependent Claim
29 words
The method of claim 3 wherein said step a) comprises computing the histogram of the filtered image and optimizing a parameter corresponding to an upper fraction of the histogram.
Claim 7
Dependent Claim
29 words
The method of claim 4 wherein said parameter corresponding to an upper fraction of the histogram comprises a parameter related to a maximum gray level in the filtered image.
Claim 8
Dependent Claim
40 words
The method of claim 3 wherein said step a) comprises optimizing a local threshold parameter set for determining a local threshold and optimizing a local neighborhood parameter for determining a local neighborhood size within which the local threshold is applied.
Claim 9
Dependent Claim
131 words
The method of claim 3 wherein: said step a) further comprises; A) computing the histogram of the filtered image and optimizing a parameter, f, corresponding to an upper fraction of the histogram; B) optimizing a local threshold parameter set for determining a local threshold; and C) optimizing a local neighborhood parameter for determining a local neighborhood size within which the local threshold is applied; and said step c) comprises: A) producing a globally thresholded image by retaining an upper fraction of the pixels in the filtered image; and B) producing a locally thresholded image by locally thresholding pixels in a window of the filtered image, determined by the local neighborhood parameter, and retaining the pixels in the window whose gray levels satisfy a relation determined by the local threshold parameter set.
Claim 10
Dependent Claim
20 words
The method of claim 7 including the step of logically ANDing the globally thresholded image and the locally thresholded image.
Claim 11
Independent Claim
166 words
A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of: a) optimizing a value for at least one predetermined clustering algorithm parameter; b) filtering a digital mammogram image to produce a filtered image comprising essentially microcalcifications; c) thresholding the filtered image from step b) to produce a thresholded image; d) forming clusters of microcalcifications in the thresholded image from step c); and e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from nonsuspicious clusters of microcalcifications; wherein said step a) comprises: i) providing an optimizing algorithm; ii) providing a database of training images; and iii) inputting the database of training images into said optimizing algorithm to determine optimum values for said predetermined clustering algorithm parameters; and wherein said step d) comprises: i) providing a clustering algorithm incorporating the optimized predetermined clustering algorithm parameter; and ii) applying the clustering algorithm to the thresholded image to identify potential clusters of microcalcifications.
Claim 12
Independent Claim
166 words
A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of: a) optimizing a value for at least one predetermined clustering algorithm parameter; b) filtering a digital mammogram image to produce a filtered image comprising essentially microcalcifications; c) thresholding the filtered image from step b) to produce a thresholded image; d) forming clusters of microcalcifications in the thresholded image from step c); and e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from nonsuspicious clusters of microcalcifications; wherein said step a) comprises: i) providing an optimizing algorithm; ii) providing a database of training images; and iii) inputting the database of training images into said optimizing algorithm to determine optimum values for said predetermined clustering algorithm parameters; and wherein said step d) comprises: i) providing a clustering algorithm incorporating the optimized predetermined clustering algorithm parameter; and ii) applying the clustering algorithm to the thresholded image to identify potential clusters of microcalcifications.
Claim 13
Dependent Claim
23 words
The method of claim 9 wherein said step a) comprises optimizing a parameter, d nn , corresponding to a maximum distance between microcalcifications.
Claim 14
Dependent Claim
27 words
The method of claim 9 wherein said step a) comprises optimizing a parameter, μCs min , corresponding to a minimum number of microcalcifications for forming a cluster.
Claim 15
Dependent Claim
46 words
The method of claim 9 wherein said step a) comprises: A) optimizing a parameter, d nn , corresponding to a maximum distance between microcalcifications in a cluster; and B) optimizing a parameter, μCs min , corresponding to a minimum number of microcalcifications for forming a cluster.
Claim 16
Dependent Claim
35 words
The method of claim 12 wherein said step d) comprises identifying groups of microcalcifications in which at least μCs min microcalcifications are within distance d nn of each other to define the clusters of microcalcifications.
Claim 17
Independent Claim
138 words
A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of: a) jointly optimizing a filtering algorithm parameter, a thresholding algorithm parameter and a clustering algorithm parameter; b) providing a filtering algorithm incorporating the optimized filtering algorithm parameter and applying the filtering algorithm to the digital mammogram image to produce a filtered image comprising essentially microcalcifications; c) providing a thresholding algorithm incorporating the optimized thresholding algorithm parameter and applying the thresholding algorithm to the filtered image to produce a thresholded image; d) providing a clustering algorithm incorporating the optimized clustering algorithm parameter and applying the clustering algorithm to the thresholded image to identify potential clusters of microcalcifications; and e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from non-suspicious clusters of microcalcifications.
Claim 18
Independent Claim
138 words
A method for detecting clustered microcalcifications in a digital mammogram image by digital processing comprising, in sequence, the steps of: a) jointly optimizing a filtering algorithm parameter, a thresholding algorithm parameter and a clustering algorithm parameter; b) providing a filtering algorithm incorporating the optimized filtering algorithm parameter and applying the filtering algorithm to the digital mammogram image to produce a filtered image comprising essentially microcalcifications; c) providing a thresholding algorithm incorporating the optimized thresholding algorithm parameter and applying the thresholding algorithm to the filtered image to produce a thresholded image; d) providing a clustering algorithm incorporating the optimized clustering algorithm parameter and applying the clustering algorithm to the thresholded image to identify potential clusters of microcalcifications; and e) inputting the identified clusters of microcalcifications to a classifier to distinguish suspicious clusters of microcalcifications from non-suspicious clusters of microcalcifications.
Claim 19
Dependent Claim
45 words
The method of claim 14 wherein said step a) comprises: i) providing an optimizing algorithm; ii) providing a database of training images; and iii) inputting the database of training images into said optimizing algorithm to determine an optimum value for each of said algorithm parameters.
Claim 20
Dependent Claim
29 words
The method of claim 14 wherein said step of optimizing the filtering algorithm parameter comprises optimizing a parameter corresponding to the size of microcalcifications in the digital mammogram image.
Claim 21
Dependent Claim
34 words
The method of claim 14 wherein said step of optimizing the thresholding algorithm parameter comprises computing the histogram of the filtered image and optimizing a parameter corresponding to an upper fraction of the histogram.
Claim 22
Dependent Claim
29 words
The method of claim 17 wherein said parameter corresponding to an upper fraction of the histogram comprises a parameter related to a maximum gray level in the filtered image.
Claim 23
Dependent Claim
45 words
The method of claim 14 wherein said step of optimizing a thresholding algorithm parameter comprises optimizing a local threshold parameter set for determining a local threshold and optimizing a local neighborhood parameter for determining a local neighborhood size within which the local threshold is applied.
Claim 24
Dependent Claim
136 words
The method of claim 14 wherein: said step of optimizing a thresholding algorithm parameter further comprises; i) computing the histogram of the filtered image and optimizing a parameter, f, corresponding to an upper fraction of the histogram; ii) optimizing a local threshold parameter set for determining a local threshold; and iii) optimizing a local neighborhood parameter for determining a local neighborhood size within which the local threshold is applied; and said step c) comprises: (i) producing a globally thresholded image by retaining an upper fraction of the pixels in the filtered image; and (ii) producing a locally thresholded image by locally thresholding pixels in a window of the filtered image, determined by the local neighborhood parameter, and retaining the pixels in the window whose gray levels satisfy a relation determined by the local threshold parameter set.
Claim 25
Dependent Claim
20 words
The method of claim 20 including the step of logically ANDing the globally thresholded image and the locally thresholded image.
Claim 26
Dependent Claim
28 words
The method of claim 14 wherein said step of optimizing the clustering algorithm parameter comprises optimizing a parameter d nn , corresponding to a maximum distance between microcalcifications.
Claim 27
Dependent Claim
32 words
The method of claim 14 wherein said step of optimizing the clustering algorithm parameter comprises optimizing a parameter, μCs min , corresponding to a minimum number of microcalcifications for forming a cluster.
Claim 28
Dependent Claim
51 words
The method of claim 14 wherein said step of optimizing the clustering algorithm parameter comprises: i) optimizing a parameter, d nn , corresponding to a maximum distance between microcalcifications in a cluster; and ii) optimizing a parameter, μCs min , corresponding to a minimum number of microcalcifications for forming a cluster.
Claim 29
Dependent Claim
37 words
The method of claim 24 wherein said step d) comprises identifying groups of microcalcifications in which at least μCs min , microcalcifications are within distance d nn , of each other to define the clusters of microcalcifications.
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| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
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| 3.5 Years (Year 4) | 2005-05-13 | 2005-11-12 | 2006-05-14 | $2000.00 | Window Closed / Lapsed |
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