Ultrasonic diagnosis apparatus, image processing apparatus, control method for ultrasonic diagnosis apparatus, and image processing method
View Patent ↗A transmission/reception unit transmits an ultrasonic wave to a subject via the ultrasonic probe, receives an ultrasonic wave reflected by the subject, and outputs an echo signal corresponding to the received ultrasonic wave. A first volume data generating unit generates first volume data based on the echo signal. A second volume data generating unit generates second volume data by applying, to each voxel contained in the first volume data, three-dimensional filtering with a filtering characteristic corresponding to three-dimensional directivity of the voxel. An image data generating unit generates data of a two-dimensional ultrasonic image based on the second volume data.
1. An ultrasonic diagnosis apparatus comprising:
an ultrasonic probe;
a transmission/reception circuit configured to transmit an ultrasonic wave to a subject via the ultrasonic probe and receive an echo signal corresponding to an ultrasonic wave reflected by the subject; and
processing circuit configured to
generate first volume data based on the received echo signal;
generate low-frequency volume data and high-frequency volume data on spatial frequencies based on the generated first volume data;
calculate three eigenvalues, identify three-dimensional structure patterns of three-dimensional structures indicating a degree of anisotropic structure components and isotropic structure components contained in the generated low-frequency volume data using the three eigenvalues, calculate edge information in accordance with the identified structure patterns using two eigenvalues of the three eigenvalues, and apply a nonlinear anisotropic diffusion filter having a filter characteristic corresponding to the calculated edge information to the low-frequency volume data;
perform edge enhancement processing for the high-frequency volume data using the generated edge information; and
generate second volume data with reduced speckle and reduced noise based on the low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied and the high-frequency volume data for which edge enhancement processing is performed.
2. An ultrasonic diagnosis apparatus comprising:
an ultrasonic probe;
a transmission/reception circuit configured to transmit an ultrasonic wave to a subject via the ultrasonic probe, receive an ultrasonic wave reflected by the subject, and output an echo signal corresponding to the received ultrasonic wave; and
a processing circuit configured to
generate first volume data based on the echo signal;
generate second volume data by applying, to each voxel contained in the first volume data, three-dimensional filtering with a filtering characteristic derived from three-dimensional directivity of the voxel; and
generate data of a two-dimensional ultrasonic image based on the second volume data.
3. The apparatus according to claim 2 , wherein the processing circuit calculates an intensity and direction of three-dimensional diffusion of the voxel based on a value of the voxel and values of a plurality of neighboring voxels located near the voxel, and determines the filtering characteristic in accordance with the calculated intensity and direction of three-dimensional diffusion.
4. The apparatus according to claim 2 , wherein the processing circuit is further configured to
generate low-frequency volume data and high-frequency volume data on spatial frequencies based on the generated first volume data,
calculate three eigenvalues for classifying three-dimensional structure patterns of three-dimensional structures indicating a degree of anisotropic structure components and isotropic structure components contained in the generated low-frequency volume data using the three eigenvalues, calculate edge information in accordance with the identified structure patterns based on two eigenvalues of the three eigenvalues, and apply a nonlinear anisotropic diffusion filter having a filter characteristic corresponding to the calculated edge information to the low-frequency volume data;
perform edge enhancement processing for the high-frequency volume data based on the generated edge information; and
generate second volume data with reduced speckle and reduced noise based on the low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied by the filter unit and the high-frequency volume data for which edge enhancement processing is performed.
5. The apparatus according to claim 4 , wherein the processing circuit generates the low-frequency volume data and the high-frequency volume data by performing multiresolution analysis of the generated first volume data, and
generates the second volume data by performing multiresolution synthesis of low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied unit and high-frequency volume data for which edge enhancement processing is performed.
6. The apparatus according to claim 5 , wherein the multiresolution analysis/synthesis includes wavelet transform/inverse transform, a Laplacian pyramid method, and Gabor transform/inverse transform.
7. The apparatus according to claim 2 , further comprising a display configured to display the ultrasonic image.
8. An ultrasonic diagnosis apparatus, comprising:
an ultrasonic probe;
a transmission/reception circuit configured to transmit an ultrasonic wave to a subject via the ultrasonic probe, receive an ultrasonic wave reflected by the subject, and output an echo signal corresponding to the received ultrasonic wave; and
processing circuit configured to
generate first volume data based on the echo signal;
generate second volume data from the first volume data by identifying a three-dimensional isotropic structure component and a three dimensional anisotropic structure component, reducing the three-dimensional isotropic structure component, and enhancing the three-dimensional anisotropic structure component, the three-dimensional isotropic structure component and the three-dimensional anisotropic structure component being contained in the first volume data; and
generate data of a two-dimensional ultrasonic image by performing three-dimensional image processing for the second volume data.
9. The apparatus according to claim 8 , wherein the processing circuit is further configured to
perform multiresolution analysis of the first volume data to generate first low-frequency volume data and first high-frequency volume data,
calculate an eigenvalue and eigenvector of a three-dimensional structure tensor based on the first low-frequency volume data,
apply a filter to the first low-frequency volume data along a direction corresponding to the eigenvector to generate second low-frequency volume data, the filter having an intensity of diffusion corresponding to the eigenvalue,
control a value of a voxel contained in the first high-frequency volume data based on the eigenvalue to generate second high-frequency volume data, and
perform multiresolution synthesis of the second low-frequency volume data and the second high-frequency volume data to generate the second volume data.
10. The apparatus according to claim 9 , wherein the processing circuit calculates three eigenvalues of the three-dimensional structure tensor, and
calculates edge information associated with a magnitude of an edge component contained in the first low-frequency volume data based on a maximum eigenvalue and minimum eigenvalue of the three eigenvalues, and applies a nonlinear anisotropic diffusion filter having the intensity of diffusion corresponding to the magnitude of the edge information to the first low-frequency volume data.
11. The apparatus according to claim 9 , wherein the multiresolution analysis/synthesis includes wavelet transform/inverse transform, a Laplacian pyramid method, and Gabor transform/inverse transform.
12. The apparatus according to claim 8 , further comprising a display configured to display the ultrasonic image.
13. An image processing apparatus, comprising:
a memory configured to store first volume data associated with a subject; and
a processing circuit configured to
generate second volume data by applying, to each voxel contained in the first volume data, three-dimensional filtering with a filtering characteristic derived from three-dimensional directivity of the voxel; and
generate data of a two-dimensional ultrasonic image based on the second volume data.
14. The apparatus according to claim 13 , wherein the processing circuit calculates an intensity and direction of three-dimensional diffusion of the voxel based on a value of the voxel and values of a plurality of neighboring voxels located near the voxel, and determines the filtering characteristic in accordance with the calculated intensity and direction of three-dimensional diffusion.
15. The apparatus according to claim 13 , wherein the processing circuit is further configured to
generate low frequency volume data and high-frequency volume data on spatial frequencies based on the generated first volume data,
calculate three eigenvalues for classifying three-dimensional structure patterns of three-dimensional structures indicating a degree of anisotropic structure components and isotropic structure components contained in the generated low-frequency volume data using the three eigenvalues, calculate edge information in accordance with the identified structure patterns based on two eigenvalues of the three eigenvalues, and apply a nonlinear anisotropic diffusion filter having a filter characteristic corresponding to the calculated edge information to the low-frequency volume data;
perform edge enhancement processing for the high-frequency volume data based on the generated edge information; and
generate second volume data with reduced speckle and reduced noise based on the low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied and the high-frequency volume data for which edge enhancement processing is performed.
16. The apparatus according to claim 15 , wherein the processing circuit generates the low-frequency volume data and the high-frequency volume data by performing multiresolution analysis of the generated first volume data, and
generates the second volume data by performing multiresolution synthesis of low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied and high-frequency volume data for which edge enhancement processing is performed.
17. The apparatus according to claim 16 , wherein the multiresolution analysis/synthesis includes wavelet transform/inverse transform, a Laplacian pyramid method, and Gabor transform/inverse transform.
18. The apparatus according to claim 13 , further comprising a display configured to display the ultrasonic image.
19. An image processing apparatus, comprising:
a memory configured to store first volume data associated with a subject;
a processing circuit configured to
generate second volume data from the first volume data by identifying a three-dimensional isotropic structure component and a three dimensional anisotropic structure component, reducing the three-dimensional isotropic structure component, and enhancing the three-dimensional anisotropic structure component, the three-dimensional isotropic structure component and the three-dimensional anisotropic structure component being contained in the first volume data; and
generate data of a two-dimensional image by performing three-dimensional image processing for the second volume data.
20. The apparatus according to claim 19 , wherein the processing circuit is further configured to
perform multiresolution analysis of the first volume data to generate first low-frequency volume data and first high-frequency volume data,
calculate an eigenvalue and eigenvector of a three-dimensional structure tensor based on the first low-frequency volume data,
apply a filter to the first low-frequency volume data along a direction corresponding to the eigenvector to generate second low-frequency volume data, the filter having an intensity of diffusion corresponding to the eigenvalue,
control a value of a voxel contained in the first high-frequency volume data based on the eigenvalue to generate second high-frequency volume data, and perform multiresolution synthesis of the second low-frequency volume data and the second high-frequency volume data to generate the second volume data.
21. The apparatus according to claim 20 , wherein the processing circuit calculates three eigenvalues of the three-dimensional structure tensor, and calculates edge information associated with a magnitude of an edge component contained in the first low-frequency volume data based on a maximum eigenvalue and minimum eigenvalue of the three eigenvalues, and applies a nonlinear anisotropic diffusion filter having the intensity of diffusion corresponding to the magnitude of the edge information to the first low-frequency volume data.
22. The apparatus according to claim 20 , wherein the multiresolution analysis/synthesis includes wavelet transform/inverse transform, a Laplacian pyramid method, and Gabor transform/inverse transform.
23. The apparatus according to claim 19 , further comprising a display configured to display the two-dimensional image.
24. A control method for an ultrasonic diagnosis apparatus, the ultrasonic diagnosis apparatus which comprises an ultrasonic probe, a transmission/reception circuit configured to transmit and receive ultrasonic waves via the ultrasonic probe, and a processing circuit, the processing circuit being configured to:
cause the transmission/reception circuit to transmit an ultrasonic wave to a subject via the ultrasonic probe, receive an ultrasonic wave reflected by the subject via the ultrasonic probe, and output an echo signal corresponding to the received ultrasonic wave;
generate first volume data based on the received echo signal;
generate low-frequency volume data and high-frequency volume data on spatial frequencies based on the generated first volume data;
calculate three eigenvalues, identify three-dimensional structure patterns of three-dimensional structures indicating a degree of anisotropic structure components and isotropic structure components contained in the generated low-frequency volume data using the three eigenvalues, calculate edge information in accordance with the identified structure patterns based on two eigenvalues of the three eigenvalues, and apply a nonlinear anisotropic diffusion filter having a filter characteristic corresponding to the calculated edge information to the low-frequency volume data;
perform edge enhancement processing for the high-frequency volume data based on the generated edge information; and
generate second volume data with reduced speckle and reduced noise based on the low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied and the high-frequency volume data for which edge enhancement processing is performed.
25. A control method for an ultrasonic diagnosis apparatus, the ultrasonic diagnosis apparatus comprises an ultrasonic probe, a transmission/reception circuit configured to transmit and receive ultrasonic waves via the ultrasonic probe, and a processing circuit, the processing circuit configured to:
cause the transmission/reception circuit to transmit an ultrasonic wave to a subject via the ultrasonic probe, receive an ultrasonic wave reflected by the subject via the ultrasonic probe, and output an echo signal corresponding to the received ultrasonic wave;
generate first volume data based on the output echo signal;
generate second volume data by applying, to each voxel contained in the first volume data, three-dimensional filtering with a filtering characteristic derived from three-dimensional directivity of the voxel; and
generate data of a two-dimensional ultrasonic image based on the second volume data.
26. The method according to claim 25 , wherein the processing circuit is further configured to calculate an intensity and direction of three-dimensional diffusion of the voxel based on a value of the voxel and values of a plurality of neighboring voxels located near the voxel, and determine the filtering characteristic in accordance with the calculated intensity and direction of three-dimensional diffusion.
27. The method according to claim 25 , wherein the processing circuit is further configured to generate low-frequency volume data and high-frequency volume data on spatial frequencies based on the generated first volume data, calculate three eigenvalues for classifying three-dimensional structure patterns of three-dimensional structures contained in the generated low-frequency volume data, calculate edge information based on two eigenvalues of the three eigenvalues, apply a nonlinear anisotropic diffusion filter having a filter characteristic corresponding to the calculated edge information to the low-frequency volume data, perform edge enhancement processing for the high-frequency volume data based on the generated edge information, and generate second volume data with reduced speckle and reduced noise based on the low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied and the high-frequency volume data for which edge enhancement processing is performed.
28. An image processing method comprising:
generating low-frequency volume data and high-frequency volume data on spatial frequencies based on first volume data associated with a subject;
calculating three eigenvalues and identifying three-dimensional structure patterns of three-dimensional structures indicating a degree of anisotropic structure components and isotropic structure components contained in the generated low-frequency volume data using the three eigenvalues;
calculating edge information in accordance with the identified structure pattern based on two eigenvalues of the three eigenvalues;
applying a nonlinear anisotropic diffusion filter having a filter characteristic corresponding to the calculated edge information to the low-frequency volume data;
performing edge enhancement processing for the high-frequency volume data based on the generated edge information; and
generating second volume data with reduced speckle and reduced noise based on the low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied and the high-frequency volume data for which edge enhancement processing is performed.
29. An image processing method comprising:
generating second volume data by applying, to each voxel contained in generated first volume data associated with a subject, three-dimensional filtering with a filtering characteristic derived from three-dimensional directivity of the voxel; and
generating data of a two-dimensional image based on the second volume data.
30. The method according to the claim 29 , wherein generating second volume data calculates an intensity and direction of three-dimensional diffusion of the voxel based on a value of the voxel and values of a plurality of neighboring voxels located near the voxel, and determines the filtering characteristic in accordance with the calculated intensity and direction of three-dimensional diffusion.
31. The method according to claim 29 , wherein generating second volume data generates low-frequency volume data and high-frequency volume data on spatial frequencies based on the generated first volume data, calculates three eigenvalues for classifying three-dimensional structure patterns of three-dimensional structures contained in the generated low-frequency volume data, calculates edge information based on two eigenvalues of the three eigenvalues, applies a nonlinear anisotropic diffusion filter having a filter characteristic corresponding to the calculated edge information to the low-frequency volume data; performs edge enhancement processing for the high-frequency volume data based on the generated edge information; and generates second volume data with reduced speckle and reduced noise based on the low-frequency volume data to which the nonlinear anisotropic diffusion filter is applied and the high-frequency volume data for which edge enhancement processing is performed.
32. The method of claim 28 , wherein the step of identifying the three-dimensional structure patterns comprises:
classifying the three-dimensional structures into three-dimensional isotropic structure components and three-dimensional anisotropic structure components using the calculated three eigenvalues.