Methods for microcalcification detection of breast cancer on digital tomosynthesis mammograms
First Claim
1. A computer-implemented method for detecting a microcalcification cluster in reconstructed volumes of a three-dimensional digital breast tomosynthesis image, comprising:
- receiving, at a computing device, a three-dimensional digital breast tomosynthesis image;
reconstructing, at the computing device, the three-dimensional digital breast tomosynthesis image to create a reconstructed three-dimensional digital breast tomosynthesis image;
enhancing, at the computing device, a microcalcification candidate in each slice of the reconstructed three-dimensional digital breast tomosynthesis image to obtain an enhanced three-dimensional digital breast tomosynthesis image by simultaneously;
1) determining a multi-scale calcification response for each voxel (x,y,z) of the microcalcification candidate, and2) one of;
a) enhancing a signal-to-noise ratio of a microcalcification in the reconstructed three-dimensional digital breast tomosynthesis image, orb) enhancing a signal intensity of the microcalcification in the reconstructed three-dimensional digital breast tomosynthesis image;
wherein the multi-scale calcification response includes;
a) filtering the reconstructed three-dimensional digital breast tomosynthesis image with a plurality of smoothing filters where an amount of smoothing for each smoothing filter is determined by a scale, and the plurality of filters includes a set of scales;
b) finding a calcification response at each voxel of the reconstructed three-dimensional digital breast tomosynthesis image for each scale of the set of scales, where, among the set of scales, the calcification response at a central voxel of a three-dimensional microcalcification is higher when the scale is such that a shape of the smoothing filter approximately matches a shape of the three-dimensional microcalcification; and
c) combining, among the set of scales, each calcification response using a non-linear operator at each voxel of the reconstructed three-dimensional digital breast tomosynthesis image; and
detecting, at the computing device, potential individual microcalcifications that form a microcalcification cluster within the enhanced three-dimensional digital breast tomosynthesis image.
3 Assignments
0 Petitions
Accused Products
Abstract
A computer-aided detection system to detect clustered microcalcifications in digital breast tomosynthesis (DBT) is disclosed. The system performs detection in 2D images and a reconstructed 3D volume. The system may include an initial prescreening of potential microcalcifications by using one or more 3D calcification response function (CRF) values modulated by an enhancement method to identify high response locations in the DBT volume as potential signals. Microcalcifications may be enhanced using a Multi-Channel Enhancement method. Locations detected using these methods can be identified and the potential microcalcifications may be extracted. The system may include object segmentation that uses region growing guided by the enhancement-modulated CRF values, gray level voxel values relative to a local background level, or the original DBT voxel values. False positives may be reduced by descriptors of characteristics of microcalcifications. Detected locations of clusters and a cluster significance rating of each cluster may be output and displayed.
-
Citations
19 Claims
-
1. A computer-implemented method for detecting a microcalcification cluster in reconstructed volumes of a three-dimensional digital breast tomosynthesis image, comprising:
-
receiving, at a computing device, a three-dimensional digital breast tomosynthesis image; reconstructing, at the computing device, the three-dimensional digital breast tomosynthesis image to create a reconstructed three-dimensional digital breast tomosynthesis image; enhancing, at the computing device, a microcalcification candidate in each slice of the reconstructed three-dimensional digital breast tomosynthesis image to obtain an enhanced three-dimensional digital breast tomosynthesis image by simultaneously; 1) determining a multi-scale calcification response for each voxel (x,y,z) of the microcalcification candidate, and 2) one of; a) enhancing a signal-to-noise ratio of a microcalcification in the reconstructed three-dimensional digital breast tomosynthesis image, or b) enhancing a signal intensity of the microcalcification in the reconstructed three-dimensional digital breast tomosynthesis image; wherein the multi-scale calcification response includes; a) filtering the reconstructed three-dimensional digital breast tomosynthesis image with a plurality of smoothing filters where an amount of smoothing for each smoothing filter is determined by a scale, and the plurality of filters includes a set of scales; b) finding a calcification response at each voxel of the reconstructed three-dimensional digital breast tomosynthesis image for each scale of the set of scales, where, among the set of scales, the calcification response at a central voxel of a three-dimensional microcalcification is higher when the scale is such that a shape of the smoothing filter approximately matches a shape of the three-dimensional microcalcification; and c) combining, among the set of scales, each calcification response using a non-linear operator at each voxel of the reconstructed three-dimensional digital breast tomosynthesis image; and detecting, at the computing device, potential individual microcalcifications that form a microcalcification cluster within the enhanced three-dimensional digital breast tomosynthesis image. - View Dependent Claims (2, 3, 4, 5, 6)
-
-
7. A non-transitory computer-readable medium having instructions stored thereon, the instructions when executed by a processor detect a microcalcification cluster in reconstructed volumes of a three-dimensional digital breast tomosynthesis image, causing the processor to:
-
receive a three-dimensional digital breast tomosynthesis image; reconstruct the three-dimensional digital breast tomosynthesis image to create a reconstructed three-dimensional digital breast tomosynthesis image; enhance a microcalcification candidate in each slice of the reconstructed three-dimensional digital breast tomosynthesis image to obtain a three-dimensional enhancement-modulated calcification response image; detect a seed object in the three-dimensional enhancement-modulated calcification response image, wherein the seed object includes a plurality of voxels; and detect a plurality of microcalcification candidates based on criteria including one or more of a proximity of each microcalcification candidate to the seed object and a signal-to-noise ratio value of each microcalcification candidate exceeding a threshold; wherein the instruction to enhance the microcalcification candidate in each slice of the three-dimensional digital breast tomosynthesis image to obtain the three-dimensional enhancement-modulated calcification response image includes a multi-scale calcification response function to; construct a Hessian matrix for each voxel of the reconstructed three-dimensional digital breast tomosynthesis image; select a voxel of the reconstructed three-dimensional digital breast tomosynthesis image wherein all eigenvalues of the Hessian matrix corresponding to the selected voxel are negative; determine a ratio between a square of a smallest magnitude eigenvalue and a negative value of a largest magnitude eigenvalue, wherein the ratio includes a calcification response; create an enhanced three-dimensional digital breast tomosynthesis image by enhancing, for each voxel of the three-dimensional digital breast tomosynthesis image, either;
a) a signal to noise ratio to obtain the enhanced three-dimensional digital breast tomosynthesis image or b) a signal intensity value to obtain the enhanced three-dimensional digital breast tomosynthesis image; andweight each voxel of a multi-scale calcification response volume by a value of either;
a) the signal-to-noise ratio enhanced three-dimensional digital breast tomosynthesis image or b) the signal intensity value enhanced three-dimensional digital breast tomosynthesis image to obtain the three-dimensional enhancement-modulated calcification response image from the three-dimensional digital breast tomosynthesis image.
-
-
8. A computer system for detecting a microcalcification cluster in reconstructed volumes of a three-dimensional digital breast tomosynthesis image, the system comprising:
-
a processor; a memory; a reconstruction module stored in the memory and executed by the processor to reconstruct the three-dimensional digital breast tomosynthesis image to create a reconstructed three-dimensional digital breast tomosynthesis image; an enhancement module stored in the memory and executed by the processor to enhance a microcalcification candidate in each slice of the reconstructed three-dimensional digital breast tomosynthesis image to obtain a three-dimensional enhancement-modulated calcification response image; a seed detection module stored in the memory and executed by the processor to detect a seed object in the three-dimensional enhancement-modulated calcification response image, wherein the seed object includes a plurality of voxels; a microcalcification candidate detection module stored in the memory and executed by the processor to detect a plurality of microcalcification candidates based on criteria including one or more of a proximity of each microcalcification candidate to the seed object and a signal-to-noise ratio value of each microcalcification candidate exceeding a threshold; a multi-scale calcification response module stored in the memory and executed by the processor to obtain a multi-scale calcification response volume from a reconstructed volume of the three-dimensional digital breast tomosynthesis image by applying a multi-scale calcification response function to each voxel of the reconstructed volume, wherein the multi-scale calcification response function is configured to; smooth the enhanced reconstructed three-dimensional digital breast tomosynthesis image; construct a Hessian matrix for each voxel of the enhanced three-dimensional digital breast tomosynthesis image; select a voxel of the enhanced three-dimensional digital breast tomosynthesis image wherein all eigenvalues of the Hessian matrix corresponding to the selected voxel are negative; and determine a ratio between a square of a smallest magnitude eigenvalue and a negative value of a largest magnitude eigenvalue, wherein the ratio includes a calcification response; create an enhanced three-dimensional digital breast tomosynthesis image by enhancing, for each voxel of the three-dimensional digital breast tomosynthesis image, either;
a) a signal to noise ratio to obtain the enhanced three-dimensional digital breast tomosynthesis image or b) a signal intensity value to obtain the enhanced three-dimensional digital breast tomosynthesis image; anda weighting module stored in the memory and executed by the processor to weight each voxel of the three-dimensional multi-scale calcification response volume by a value of either;
a) the signal-to-noise ratio enhanced three-dimensional digital breast tomosynthesis image or b) the signal intensity value enhanced three-dimensional digital breast tomosynthesis image to obtain the three-dimensional enhancement-modulated calcification response image from the three-dimensional digital breast tomosynthesis image. - View Dependent Claims (9)
-
-
10. A computer-implemented method for determining a strength of a microcalcification candidate in an image for differentiation of true and false microcalcifications comprising:
-
receiving an image including one of a two-dimensional projection view or a three-dimensional digital breast tomosynthesis slice image; identifying a region of interest within the image including a plurality of microcalcification candidates; characterizing the region of interest as a vector g including a set of channel response {g1, ... , gN} given a multi-channel set; differentiating each of the plurality of microcalcification candidates as one of a true microcalcification or a false microcalcification according to a linear classification model;
D(g)=(m2 −
m1 )TΣ
−
1gwherein mk includes a mean vector for class k where k=1,2, Σ
includes a covariance matrix estimated from training samples of true microcalcifications and false microcalcifications, and D(g) includes a multi-channel enhancement (MCE) response representing the strength of each of the plurality of microcalcification candidates. - View Dependent Claims (11, 12)
-
-
13. A non-transitory computer-readable medium having instructions stored thereon, the instructions when executed by a processor determine a strength of a microcalcification candidate in an image for differentiation of true and false microcalcifications, causing the processor to:
-
receive an image including one of a two-dimensional projection view or a three-dimensional digital breast tomosynthesis slice image; identify a region of interest within the image including a plurality of microcalcification candidates; characterize the region of interest as a vector g including a set of channel response {g1, ... , gN} given a multi-channel set; differentiate each microcalcification candidate as one of a true microcalcification or a false microcalcification according to a linear classification model;
D(g)=(m2 −
m1 )TΣ
−
1gwherein mk includes a mean vector for class k where k=1,2, Σ
includes a covariance matrix estimated from training samples of true microcalcifications and false microcalcifications, and D(g) includes a multi-channel enhancement (MCE) response representing the strength of each microcalcification candidate. - View Dependent Claims (14)
-
-
15. A computer system for determining a strength of a microcalcification candidate in an image for differentiation of true and false microcalcifications, the system comprising:
-
a processor; a memory; a receiving module stored in the memory and executed by the processor to receive an image including one of a two-dimensional projection view or a three-dimensional digital breast tomosynthesis slice image; a prescreening module stored in the memory and executed by the processor to identify a region of interest within the image including a plurality of microcalcification candidates; a vector module stored in the memory and executed by the processor to characterize the region of interest as a vector g including a set of channel response {g1, ... , gN} given a multi-channel set; a differentiation module stored in the memory and executed by the processor to differentiate each microcalcification candidate as one of a true microcalcification or a false microcalcification according to a linear classification model;
D(g) =(m2 −
m1 )TΣ
−
1gwherein mk includes a mean vector for class k where k=1,2, Σ
includes a covariance matrix estimated from training samples of true microcalcifications and false microcalcifications, and D(g) includes a multi-channel enhancement (MCE) response representing the strength of each microcalcification candidate. - View Dependent Claims (16)
-
-
17. A computer-implemented method for detecting a microcalcification cluster in an image, comprising:
-
enhancing, at a computing device, a received image, the image including one of a two-dimensional projection view or a three-dimensional digital tomosynthesis slice image and a plurality of microcalcification candidates, the enhancing including differentiating each of the plurality of microcalcification candidates as one of a true microcalcification or a false microcalcification according to a linear classification model;
D(g)=(m2 −
m1 )TΣ
−
1gwherein mk includes a mean vector for class k where k=1,2, Σ
includes a covariance matrix estimated from training samples of true microcalcifications and false microcalcifications, and D(g) includes a multi-channel enhancement (MCE) response representing a strength of each of the plurality of microcalcification candidates;back-projecting, at the computing device, an MCE-response for each microcalcification candidate on the two-dimensional projection view to the three-dimensional tomosynthesis image determining, at the computing device, a multi-scale calcification response function for the three-dimensional tomosynthesis image by; smoothing the enhanced reconstructed three-dimensional digital breast tomosynthesis image; constructing a Hessian matrix for each voxel of the enhanced reconstructed three-dimensional digital breast tomosynthesis image; selecting a voxel of the enhanced reconstructed three-dimensional digital breast tomosynthesis image wherein all eigenvalues of the Hessian matrix corresponding to the selected voxel are negative; and determining a ratio between a square of a smallest magnitude eigenvalue and a negative value of a largest magnitude eigenvalue, wherein the ratio includes a calcification response; weighting, at the computing device, the multi-scale calcification response volume by the back-projected MCE-response; detecting, at the computing device, a seed object in the weighted multi-scale calcification response volume.
-
-
18. A non-transitory computer-readable medium having non-transitory instructions stored thereon, the instructions when executed by a processor detect a microcalcification cluster in an image, causing the processor to:
-
enhance a received image, the image including one of a two-dimensional projection view or a two-dimensional digital tomosynthesis slice image and a plurality of microcalcification candidates, the enhance instruction further causing the processor to differentiate each of the plurality of microcalcification candidates as one of a true microcalcification or a false microcalcification according to a linear classification model;
D(g)=(m2 −
m1 )TΣ
−
gwherein includes a mean vector for class k where k=1,2, includes a covariance matrix estimated from training samples of true microcalcifications and false microcalcifications, and D(g) includes a multi-channel enhancement (MCE) response representing a strength of each of the plurality of microcalcification candidates; back-project an MCE-response for each microcalcification candidate on the two-dimensional projection view to the three-dimensional tomosynthesis image determine a multi-scale calcification response function for the three-dimensional tomosynthesis image by; smoothing the enhanced reconstructed three-dimensional digital breast tomosynthesis image; constructing a Hessian matrix for each voxel of the enhanced reconstructed three-dimensional digital breast tomosynthesis image; selecting a voxel of the enhanced reconstructed three-dimensional digital breast tomosynthesis image wherein all eigenvalues of the Hessian matrix corresponding to the selected voxel are negative; and determining a ratio between a square of a smallest magnitude eigenvalue and a negative value of a largest magnitude eigenvalue, wherein the ratio includes a calcification response; weight the multi-scale calcification response volume by the back-projected MCE-response; detect a seed object in the weighted multi-scale calcification response volume, wherein the seed object includes a plurality of voxels.
-
-
19. A computer system for detecting a microcalcification cluster in an image, the system comprising:
-
a processor; a memory; a two-dimensional image enhancement module stored in the memory and executed by the processor to enhance a received image, the image including one of a two-dimensional projection view or a two-dimensional digital tomosynthesis slice image and a plurality of microcalcification candidates, the two-dimensional image enhancement module further differentiating each of the plurality of microcalcification candidates as one of a true microcalcification or a false microcalcification according to a linear classification model;
D(g)=(m2 −
m1 )TΣ
−
1gwherein mk includes a mean vector for class k where k=1,2, Σ
includes a covariance matrix estimated from training samples of true microcalcifications and false microcalcifications, and D(g) includes a multi-channel enhancement (MCE) response representing a strength of each of the plurality of microcalcification candidates;a seed object detection module stored in the memory and executed by the processor to; back-project an MCE-response for each microcalcification candidate on the two-dimensional projection view to the three-dimensional tomosynthesis image determine a multi-scale calcification response function for the three-dimensional tomosynthesis image by; smoothing the enhanced reconstructed three-dimensional digital breast tomosynthesis image; constructing a Hessian matrix for each voxel of the enhanced reconstructed three-dimensional digital breast tomosynthesis image; selecting a voxel of the enhanced reconstructed three-dimensional digital breast tomosynthesis image wherein all eigenvalues of the Hessian matrix corresponding to the selected voxel are negative; and determining a ratio between a square of a smallest magnitude eigenvalue and a negative value of a largest magnitude eigenvalue, wherein the ratio includes a calcification response; weight the multi-scale calcification response volume by the back-projected MCE-response; and detect a seed object in the weighted multi-scale calcification response volume a clustering module stored in the memory and executed by the processor to detect a microcalcification cluster based on a location of the seed object in the weighted multi-scale calcification response volume, the microcalcification cluster having a plurality of microcalcification candidates with a calcification response above a threshold.
-
Specification