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US 11,699,214 B2Granted
Computer aided image denoising method for clinical analysis of PET images
AssigneeImam Abdulrahman Bin Faisal University
InventorMahbubunnabi Tamal
Life of this patent
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Abstract
Aspects of the disclosure provide a method for denoising an image. The method can include receiving an acquired image from an image acquisition system, and processing the acquired image with a nonlinear diffusion coefficient based filter having a diffusion coefficient that is calculated using gradient vector orientation information in the acquired image.
Claims4
A computer aided image denoising method for clinical analysis using positron emission tomography (PET), comprising:
- acquiring a PET clinical medical image by imaging a torso of a human body with a PET image acquisition system,
- receiving the PET clinical medical image on a computing device having a memory and a processor from the PET image acquisition system;
- and
- processing, with the processor of the computing device, the PET clinical medical image with a nonlinear diffusion coefficient based filter having a diffusion coefficient that is calculated using gradient vector orientation information in the PET clinical medical image to provide a denoised image,
- displaying the denoised image on a display device,
- wherein the nonlinear diffusion coefficient based filter is defined by following partial-differential-equation,
- ∂
- I
-
- (
- x
- ,
- y
- ,
- t
- )
- ∂
- t
- =
- ∇
- ·
- [
- c
-
- (
- x
- ,
- y
- ,
- t
- )
-
- ∇
- I
-
- (
- x
- ,
- y
- ,
- t
- )
- ]
- ,
- I
-
- (
- t
- =
- 0
- )
- =
- I
- 0
- ,
- where I(x,y,t) represents an image, (x,y) represents a position of pixel in the image, t is a parameter indicating a scale level in a scale space of the image I(x,y,t), I0 represents an initial image with t=0, ∇. and ∇ are divergence and gradient operators, respectively, and c (x, y, t) is the diffusion coefficient at a pixel located at position (x, y), and
- wherein the diffusion coefficient is defined as,
- c
-
- (
- x
- ,
- y
- ,
- t
- )
- =
- exp
- [
- -
- (
-
- ∇
- I
- σ
- (
- x
- ,
- y
- ,
- t
- )
-
- ×
- α
- σ
- (
- x
- ,
- y
- ,
- t
- )
- k
- )
- 2
- ]
- ,
- where Iσ(x,y,t) represents a smoothed image that is the initial image I0 smoothed with a Gaussian kernel having a width of σ, k is a noise threshold, ασ(x, y, t) is a term carrying gradient vector orientation information.
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Dependencies are inferred from the claim text.