IP Library Granted Patent US 10,127,642
Granted Patent B2
US 10,127,642 · App. 15/533,677 · Granted Nov 13, 2018

Method for correcting defective pixel artifacts in a direct radiography image

Inventors: Marc Cresens (Mortsel, BE); Herman Van Goubergen (Mortsel, BE)
Assignee: AGFA NV
G06T5/005G06T2207/10116G06T2207/20012G06T2207/20028G06T2207/20032
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 10,127,642
App. No.
15/533,677
Granted
Nov 13, 2018
Kind
B2
Abstract

A method for reducing image disturbances caused by reconstructed defective pixel clusters located in signal-gradient affected diagnostic image regions. An individually adapted central symmetrical pair reconstruction (CSP) kernel is composed for a defective image pixel based on a kernel-pair candidate order encoded in a model thereby using the pixel's validity state. The image impacted by defective pixels is corrected in real-time by statistical filtering or spatial convolution of the kernel-associated image data accessible via a predetermined CSP kernels image-offsets structure.

Claims (16)

1. A method for correcting defective pixel artifacts in a direct radiography image comprising executing, for a defective pixel, the steps of:

gathering information on a defective state of image pixels surrounding the defective pixel;

composing an adaptive reconstruction kernel by combining a set of pair-wise arranged kernel locations which correspond to non-defective pixels in a vicinity of the defective pixel;

obtaining image data associated with the set of pair-wise arranged kernel locations in the adaptive reconstruction kernel;

processing the associated image data along with kernel data using a reconstruction algorithm to calculate a replacement value for image data of the defective pixel; and

replacing the defective pixel image data with the replacement value; wherein the adaptive reconstruction kernel includes an even number of non-defective, center symmetrical kernel-pair locations relative to a center of the adaptive reconstruction kernel and is added to a center symmetrical pair kernels structure.

2. The method according to claim 1 , wherein the reconstruction algorithm performs a statistical filtering process on the image data spatially associated with the set of pair-wise arranged kernel locations in the adaptive reconstruction kernel.

3. The method according to claim 1 , wherein the reconstruction algorithm performs a spatial convolution process on the image data spatially associated with the set of pair-wise arranged kernel locations in the adaptive reconstruction kernel using associated kernel weights.

4. The method according to claim 3 , wherein the spatial convolution process includes calculating a weighted average value of the image data associated with the adaptive reconstruction kernel using corresponding kernel weights.

5. The method according to claim 4 , wherein weights of the non-defective, center symmetrical kernel-pair locations relative to the center of the adaptive reconstruction kernel in the adaptive reconstruction kernel are determined based on a relative distance model wherein a kernel location weight decreases with an increasing distance between the kernel location and the center of the adaptive reconstruction kernel.

6. The method according to claim 1 , wherein the center symmetrical kernel-pair locations are composed by order-consecutively selecting a predetermined number of kernel-pair candidates from a center symmetrical pair model based on the defective state of kernel-pair associated sensor pixels.

7. The method according to claim 6 , wherein the center symmetrical pair model is arranged as an order-sorted set of the kernel pair candidates geometrically defined by eccentricities and angular directions relative to the center of the adaptive reconstruction kernel.

8. The method according to claim 7 , wherein the center symmetrical pair eccentricity is defined such that a distance from the center of the adaptive reconstruction kernel does not decrease with sorting order.

9. The method according to claim 7 , wherein the center symmetrical pair angular directions are mutually perpendicular per set of two order-sorted kernel pairs candidates.

10. The method according to claim 1 , wherein information on the defective state of the image pixels surrounding the defective pixel is derived from a defective pixel locations structure and is merged with image position information derived from the center symmetrical pair kernels structure to create an image-offsets structure that is used during the reconstruction.

11. A non-transitory computer readable medium comprising computer executable program code adapted to carry out, when the computer executable program code is executed on a computer, the steps of claim 1 .

Assignments (2)
MERGER Recorded Aug 24, 2018
From: AGFA HEALTHCARE NV
To: AGFA NV
Reel/Frame 046940/0786 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 7, 2017
From: CRESENS, MARC; VAN GOUBERGEN, HERMAN
To: AGFA HEALTHCARE NV
Reel/Frame 042629/0802 →
Priority Claims (1)
EP 14197652 · Dec 12, 2014 · regional
Continuity (1)
Related Publication 20170345134A1 · Nov 30, 2017
Cited By (1)
US 12,621,584