Texture analysis map for image data

Patent metadata

Field	Value
Publication number	US-10074190-B2
Application number	US-201515521722-A
Country	US
Kind code	B2
Filing date	Oct 30, 2015
Priority date	Oct 30, 2014
Publication date	Sep 11, 2018
Grant date	Sep 11, 2018

Abstract

A method includes obtaining at least a first energy dependent spectral image volume and a second different energy dependent spectral image volume from reconstructed spectral image data. The method further includes generating a multi-dimensional spectral diagram that maps, for each voxel, a value of the first energy dependent spectral image volume to a corresponding value of the second energy dependent spectral image volume. The method further includes generating a set of spectral texture analysis weights from the multi-dimensional spectral diagram. The method further includes retrieving a set of texture analysis functions, which are generated as a function of voxel intensity and voxel gradient value from a co-occurrence matrix histogram. The method further includes generating a texture analysis map through a texture analysis of the reconstructed spectral image data with the set of texture analysis functions and the set of spectral texture analysis weights and visually presenting the texture analysis map.

First claim

The invention claimed is:

1. A method for generating a texture analysis map from spectral image data, comprising:
obtaining at least a first energy dependent spectral image volume and a second energy dependent spectral image volume from reconstructed spectral image data, wherein the first and second energies are different;
generating a multi-dimensional spectral diagram that maps, for each voxel of the reconstructed spectral image data, a value of the first energy dependent spectral image volume to a corresponding value of the second energy dependent spectral image volume;
generating a set of spectral texture analysis weights from the multi-dimensional spectral diagram;
retrieving a set of texture analysis functions, which are generated as a function of voxel intensity and voxel gradient value from a co-occurrence matrix histogram;
generating the texture analysis map through a texture analysis of the reconstructed spectral image data with the set of texture analysis functions and the set of spectral texture analysis weights; and
visually presenting the texture analysis map.

2. The method of claim 1 , further comprising:
generating the set of spectral texture analysis weights from a location of each voxel in the multi-dimensional spectral diagram.

3. The method of claim 2 , further comprising:
generating the set of spectral texture analysis weights from voxel gradients corresponding to a set of voxels neighboring each voxel in the multi-dimensional spectral diagram.

4. The method of claim 1 , further comprising:
visually presenting the set of spectral texture analysis weights along with the texture analysis map.

5. The method of claim 1 , further comprising:
receiving an input indicative of a change in at least one spectral texture analysis weight of the set of spectral texture analysis weights;
changing the set of spectral texture analysis weights based on the input and generating an updated set of spectral texture analysis weights; and
generating the texture analysis map through the texture analysis of the reconstructed spectral image data with the set of texture analysis functions and the updated set of spectral texture analysis weights.

6. The method of claim 1 , further comprising:
fusing the reconstructed spectral image data and the texture analysis map to create a single fused image; and
visually presenting the single fused image.

7. The method of claim 1 , further comprising:
visually presenting the reconstructed spectral image data and the texture analysis map in separate viewing areas of a viewing region of a display.

8. The method of claim 1 , further comprising:
using a dependency on the gradient direction to mix a material density effect and a material spectral separation in the texture analysis map.

9. The method of claim 1 , wherein generating the texture analysis map comprises retrieving, for a voxel, a voxel intensity as a function of a corresponding vector length in the spectral diagram and a voxel gradient as a function of a difference-vector between two vectors in the spectral diagram, corresponding to the set of voxels neighboring the voxel.

10. The method of claim 9 , further comprising:
weighting the voxel gradient with the set of spectral texture analysis weights.

11. The method of claim 9 , further comprising:
mapping a gradient weight for a voxel into the co-occurrence matrix histogram.

12. The method of claim 11 , wherein the gradient weight is a mean weight value corresponding to the set of voxels neighboring the voxel.

13. The method of claim 1 , further comprising:
calculating an initial distribution of spatial weights for the co-occurrence matrix histogram prior to generating the texture analysis map,
wherein the texture analysis map is an intermediate texture analysis map;
calculating an updated distribution of spatial weights for the co-occurrence matrix histogram based on a local difference between voxel values in intermediate texture analysis map;
generating an updated set of texture analysis functions using the updated distribution of spatial weights for the co-occurrence matrix histogram; and
generating a refined texture analysis map with the updated set of texture analysis functions.

14. The method of claim 1 , further comprising:
reconstructing, using a spectral basis decomposition algorithm, the spectral image data to generate the at least the first energy dependent spectral image volume and the second energy dependent spectral image volume.

15. An imaging system, comprising:
a reconstruction processor configured to reconstruct, using a spectral basis decomposition algorithm, spectral imaging data to generate at least a first energy dependent spectral image volume and a second energy dependent spectral image volume; and
a spectral data texture processor that includes a processor configured to:
generate a multi-dimensional spectral diagram that maps, for each voxel, a value of the first energy dependent spectral image volume to a corresponding value of the second energy dependent spectral image volume;
generate a set of spectral texture analysis weights from the multi-dimensional spectral diagram;
generate a set of texture analysis functions as a function of voxel intensity and voxel gradient value from a co-occurrence matrix histogram;
generate the texture analysis map through a texture analysis of the reconstructed spectral image data with the set of texture analysis functions and the set of spectral texture analysis weights; and
visually present the texture analysis map.

16. The imaging system of claim 15 , where the processor is further configured to: generate the set of spectral texture analysis weights from a location of each voxel in the multi-dimensional spectral diagram and voxel gradients corresponding to a set of voxels neighboring each voxel in the multi-dimensional spectral diagram.

17. The imaging system of claim 15 , where the processor is further configured to: change a weight of the set of spectral texture analysis weights in response to receiving an input indicative of the change; and generate the texture analysis map using the set of spectral texture analysis weights with the changed weight.

18. The imaging system of claim 15 , where the processor is further configured to: visually present the set of spectral texture analysis weights along with the texture analysis map.

19. The imaging system of claim 15 , where the processor is further configured to: visually present the reconstructed spectral imaging data along with the set of spectral texture analysis weights along with the texture analysis map.

20. The imaging system of claim 15 , where the processor is further configured to: fuse the reconstructed spectral imaging data and the texture analysis map to create a single fused image; and visually present the single fused image.

21. The imaging system of claim 15 , where the processor uses a dependency on the gradient direction to mix a material density effect and material spectral separation in the texture analysis map.

22. The imaging system of claim 15 , where the processor is further configured to: create the texture analysis map by retrieving, for each voxel, a voxel intensity as a function of a corresponding vector length in the spectral diagram and a voxel gradient as a function of a difference-vector between two vectors in the spectral diagram, corresponding to the set of voxels neighboring the voxel.

23. The imaging system of claim 15 , wherein

Assignees

• Koninklijke Philips Nv

Inventors

• Carmi Raz

Classifications

• G06T12/30

  Image post-processing, e.g. metal artefact correction · CPC title

• G06T2207/10081

  Computed x-ray tomography [CT] · CPC title

• G06T7/45 Primary

  using co-occurrence matrix computation · CPC title

• G06K9/4642

  Physics · mapped topic

• G06T11/008

  Physics · mapped topic