METHOD OF FILTERING AN IMAGE DATASET

• US 20110262023A1
• Filed: 10/08/2008
• Published: 10/27/2011
• Est. Priority Date: 10/08/2008
• Status: Active Grant

• Current Assignee: TomTec Imaging Systems GmbH (Koninklijke Philips N.V.)
• Original Assignee: TomTec Imaging Systems GmbH (Koninklijke Philips N.V.)
• Inventors: Brabec, Stefan
• Granted Patent: US 8,634,615 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 5/002 Denoising; Smoothing noise processing or correction adapted to be used in an image pickup device containing and electronic image sensor H04N23/81, H04N25/60 - H04N25/67
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/70 Denoising; Smoothing

Abstract

A method of filtering an image dataset and an apparatus, a data carrier, and a computer program product, the method having the following steps: providing an N-dimensional input image dataset; applying a filtering algorithm to the input image dataset, wherein the filtering algorithm is essentially unvarying over the dataset, to thereby obtain a filtered image dataset; providing a spatially varying weighting function between the input and the filtered image datasets; computing a weighted sum of the input image dataset and the filtered image dataset, thereby using the spatially varying weighting function, in order to obtain an output image dataset.

Claims

1. A method of filtering an image dataset, having the following steps:

providing an N-dimensional input image dataset;

applying a filtering algorithm to the input image dataset, wherein the filtering algorithm is essentially unvarying over the dataset, to thereby obtain a filtered image dataset;

wherein this step may be repeated with different filtering algorithms to thereby obtain further filtered image datasets;

providing a pre-determined spatially varying weighting function between the input and the at least one filtered image dataset;

computing a weighted sum of the input image dataset and the at least one filtered image dataset, thereby using the spatially varying weighting function, in order to obtain an output image dataset.

2. The method of claim 1, wherein the filtering algorithm is a noise reduction algorithm, and the filtered image dataset is a noise reduced image dataset.

3. The method of claim 1, wherein the input image dataset is an image dataset having a spatially varying pixel or voxel size.

4. The method of claim 1, wherein the input image dataset is an image dataset having a spatially varying noise distribution.

5. The method of claim 1, wherein the input image dataset is a raw image dataset obtained by medical ultrasound imaging.

6. The method of claim 1, wherein the input image dataset is a dataset in acoustic coordinates.

7. The method of claim 1, wherein the input image dataset is a dataset obtained by rotational ultrasound.

8. The method of claim 1, wherein the filtering algorithm comprises a kernel filtering algorithm using a kernel size which does not vary over the dataset.

9. The method of claim 1, wherein the spatially varying weighting function is varying linearly, quadratically or cubically in at least one coordinate direction (r, X) of the dataset.

10. The method according to claim 1, wherein the output image dataset is a three-dimensional dataset, and wherein the method further comprises

generating a two-dimensional perspective image from the output image dataset by means of rendering.

11. The method according to claim 1, wherein at least two filtered image datasets are computed with different filtering algorithms, and wherein the output image dataset is a linear combination of the at least two filtered image datasets with or without the input image dataset, the weights of the linear combination being described by at least one spatially varying weighting function.

16. Data carrier containing electronically readable signals, which may interact with a programmable computer system to thereby execute the method according to claim 1.

17. Computer program product comprising program code for executing the method according to claim 1 whenever the program is executed on a computer.

18. The method of claim 1, wherein the spatially varying weighting function is an algebraic function or a pre-determined scalar dataset.

12. A method of filtering an image dataset, having the following steps:

providing an N-dimensional input image dataset;

applying a first filtering algorithm to the input image dataset, wherein the filtering algorithm is essentially unvarying over the dataset, to thereby obtain a first filtered image dataset;

applying a second filtering algorithm to the input image dataset, wherein the filtering algorithm is essentially unvarying over the dataset, to thereby obtain a second filtered image dataset;

wherein this step may be repeated with different filtering algorithms to thereby obtain further filtered image datasets;

providing a pre-determined spatially varying weighting function between the at least two filtered image dataset and optionally the input image dataset;

computing a weighted sum of the at least two filtered image datasets, or a weighted sum of the at least two filtered image datasets and the input image dataset, thereby using the spatially varying weighting function, in order to obtain an output image dataset.

13. An apparatus for processing an image dataset, wherein the apparatus comprises:

a data storage in which an N-dimensional input image dataset is stored, and which is capable of storing further image datasets as well as a pre-determined spatially varying weighting function;

a computing unit, which is adapted to apply a filtering algorithm to the input image dataset, wherein the filtering algorithm is essentially unvarying over the dataset, to thereby obtain a filtered image dataset, and which is adapted to compute a weighted sum of the input image dataset and the filtered image dataset, thereby using the spatially varying weighting function, in order to obtain an output image dataset.

14. The apparatus according to claim 13, further comprising a screen adapted for displaying the output image dataset.

15. The apparatus according to claim 13, which is capable of carrying out the method comprising:

providing an N-dimensional input image dataset;

applying a filtering algorithm to the input image dataset, wherein the filtering algorithm is essentially unvarying over the dataset, to thereby obtain a filtered image dataset;

wherein this step may be repeated with different filtering algorithms to thereby obtain further filtered image datasets;

providing a pre-determined spatially varying weighting function between the input and the at least one filtered image dataset;

computing a weighted sum of the input image dataset and the at least one filtered image dataset, thereby using the spatially varying weighting function, in order to obtain an output image dataset.