IMAGE PROCESSING DEVICE AND METHOD

• US 20150302602A1
• Filed: 12/02/2013
• Published: 10/22/2015
• Est. Priority Date: 12/03/2012
• Status: Active Grant

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips N.V.
• Inventors: WIEMKER, Rafael, KLINDER, Tobias
• Granted Patent: US 9,536,318 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 7/12 Edge-based segmentation

Abstract

The present invention relates to an image processing device for detecting line structures in an image data set. The device comprises a model definition unit (12) for defining a line model of a line structure to be detected, said line model comprising a number of voxels, a calculation unit (14) for calculating, per voxel of interest of said image data set, several correlation values of a correlation between said line model and an image area around said voxel of interest, said image area comprising a corresponding number of voxels as said line model, wherein for each of a number of different relative orientations of said line model with respect to said image area a respective correlation value is calculated, and a determining unit (16) for determining, per voxel of interest, the maximum correlation value from said calculated correlation values and the corresponding optimal orientation at which said maximum correlation value is obtained.

Claims

1. An image processing device for detecting line structures in an image data set, comprising:

a model definition unit for defining a line model of a line structure to be detected, said line model comprising a number of voxels; and

said line model configured to define a two-dimensional rectangular line model having a predetermined length and width,

a calculation unit for calculating, per voxel of interest of said image data set, several correlation values of a correlation between said line model and an image area around said voxel of interest, said image area comprising a corresponding number of voxels as said line model, wherein for each of a number of different relative orientations of said line model with respect to said image area a respective correlation value is calculated, and wherein said calculation unit is configured to calculate, per pixel of interest of two-dimensional image data set, several correlation values of a correlation between said two-dimensional rectangular line model and a two-dimensional image area around said pixel of interest, and

a determining unit for determining, per voxel of interest, the maximum correlation value from said calculated correlation values and the corresponding optimal orientation at which said maximum correlation value is obtained.

2. The image processing device as claimed in claim 1,

wherein said calculation unit is configured to calculate, per voxel of interest of said image data set, several correlation values of a linear correlation between said line model and an image area around said voxel of interest.

3. The image processing device as claimed in claim 2,

wherein said calculation unit is configured to calculate, per voxel of interest of said image data set, several correlation values of said linear correlation between said line model and an image area around said voxel Of interest by calculating the ratio of the covariance of intensity values of said image area and said line model over the variance of the intensity values of said image area and variance of the intensity values of said line model.

5. The image processing device as claimed in claim 1,

wherein said model definition unit is configured to define a two-dimensional rectangular line model having a lamer extension in a direction along the line structure to be detected than in a direction perpendicular to the line structure to be detected.

7. The image processing device as claimed in claim 1,

wherein said model definition unit is configured to define a three-dimensional cuboidal line model having a predetermined length, width and height.

8. The image processing device as claimed in claim 1,

wherein said model definition unit is configured to define a map of voxels or an analytical function as line model.

9. The image processing device as claimed in claim 1,

wherein said model definition unit is configured to initially define a general line model of the line stricture to be detected and to use the maximum correlation values and the corresponding optimal orientations obtained by use of said general line model to define a refined line model of the line structure to be detected, and

wherein said calculation unit and said determining unit are configured to repeat the steps of calculating and determining by use of said refined line model.

10. The image processing device as claimed in claim 1,

wherein said calculation unit is configured to calculate said correlation values only for voxels belonging to a predetermined region of interest and to determine if a voxel belongs to said predetermined region of interest based on a mask image indicating if a voxel belongs to said predetermined region of interest or a probability value of the probability that a voxel belongs to said predetermined region of interest.

11. The image processing device as claimed in claim 1,

wherein said calculation unit is configured to calculate, per voxel of interest of said image data set, several correlation values of a correlation between said line model and an image area around said voxel of interest for different relative orientations of said line model with respect to said image area by rotating said line model and/or said image area around said voxel of interest to change the relative orientation.

12. The image processing device as claimed in claim 1, further comprising a post-processing unit for identifying within the image data set areas of voxels for which substantially the same optimal orientation has been found.

13. The image processing device as claimed in claim 12,

wherein said post-processing unit is configured to determine, per voxel of interest, a local orientedness value and to multiply, per voxel of interest, said local orientedness value with the obtained maximum correlation value to obtain an amplified maximum correlation value.

4. (canceled)

6. The image processing device as claimed 1,

wherein said calculation unit and said determining unit are configured to repeat the steps of calculating and determining for different two-dimensional image data sets, in particular different image slices of a three-dimensional image data set.

14. An image processing method for detecting line structures in image data set, comprising:

defining a line model of a line structure to be detected, said line model comprising a number of voxels; and

said line model defining a two-dimensional rectangular line model having a predetermined length and width,

calculating, per voxel of interest of said image data set, several correlation values of a correlation between said Jibe model and an image area around said voxel of interest, said image area comprising a corresponding number of voxels as said line model, wherein for each of a number of different relative orientations of said line model with respect to said image area a respective correlation value is calculated, and wherein per pixel of interest of two-dimensional image data set, several correlation values of a correlation between said two-dimensional rectangular line model and a two-dimensional image area around said pixel of interest are calculated, and

determining, per voxel of interest, the maximum correlation value from said calculated correlation values and the corresponding optimal orientation at which said maximum correlation value is obtained.

15. A computer program comprising program code means for causing a computer to carry out the steps of the method as claimed in claim 14 when said computer program is carried out on the computer.