Anisotropy diffusion image noise reduction method based on McIlhagga edge detection operator

An edge detection operator and anisotropic technology, applied in the field of anisotropic diffusion image noise reduction, can solve the problems of poor robustness of strong speckle noise, limit the effect of high noise image denoising, etc., and achieve good robustness effect

Inactive Publication Date: 2013-10-09
SHANGHAI UNIV
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Problems solved by technology

They all use the imaginary part of the image as an edge detection operator, but both have the disadvantage of poor robustness to strong speckle noise
[0006] Zhang et al. proposed Laplacian pyramid-based nonlinear diffusion (LPND) (see F. Zhang, M.Y. Yang, M.K. Liang, Y. Kim. "Nonlinear D

Method used

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  • Anisotropy diffusion image noise reduction method based on McIlhagga edge detection operator
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  • Anisotropy diffusion image noise reduction method based on McIlhagga edge detection operator

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Embodiment 1

[0049] see figure 1 , the specific implementation steps of the anisotropic diffusion image denoising method based on the McIlhagga edge detection operator are as follows:

[0050] Step 1, use the McIlhagga edge detection operator to detect the edge in the image for the ultrasound image containing speckle noise; the specific process is:

[0051] (1) Assume that the noise in the image is the root mean square magnitude of n 0 white noise, its power spectrum is n 0 2 , while assuming that the uninteresting edges in the image are power spectra of C 2 / ω 2 Brownian noise, where C is an amplitude constant, ω Indicates the frequency, then the total noise power spectrum of the image is C 2 / ω 2 + n 0 2 ;

[0052] (2) In the two-dimensional frequency domain, the McIlhagga edge detection operator is expressed as:

[0053]

[0054] in i is the imaginary unit, ω 1 with ω 2 are the row and column frequencies, respectively, is the standard deviation of σ Th...

Embodiment 2

[0080] This embodiment is basically the same as Embodiment 1, and the special feature is: the direction parameter in the step 1 θ take multiple directions θ = [0, π / 12, π / 6..., 2 π ], scale parameter σ multi-scale σ = [0, 1, 2, 3]; in the second step τ is 15; Δ in the step 4 t with n m 0.05 and 20 respectively.

Embodiment 3

[0082] The specific implementation steps of the anisotropic diffusion image denoising method based on the McIlhagga edge detection operator are as follows:

[0083] Step 1. Use the McIlhagga edge detection operator to figure 2 The ultrasonic image shown in (a) is processed to obtain its binary edge map H ( x , y ),Such as figure 2 (b) shown.

[0084] Step 2. Use the distance mapping function to convert the binary edge map into a gradient edge map M ( I ).

[0085] The specific method is:

[0086] (1) Calculate the distance from a point in the image to the nearest edge point in the binary edge map, and generate a distance mapping function:

[0087]

[0088] in yes H ( x , y ) composed of all edge points on s is a parameterized curve for parameters.

[0089] (2) Extend the binary edge map to a gradient edge map according to the distance mapping function:

[0090]

[0091] in τ It is the bias coefficient of the gradient edge map, and its value affects th...

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Abstract

The invention discloses an anisotropy diffusion image noise reduction method based on a McIlhagga edge detection operator and belongs to the field of digital image noise reduction. The anisotropy diffusion image noise reduction method based on the McIlhagga edge detection operator is used for restraining speckle noise in an ultrasound image. The anisotropy diffusion image noise reduction method based on the McIlhagga edge detection operator comprises the steps that the McIlhagga edge detection operator with high robustness to noise is used for conducting edge detection on the ultrasound image so that a two-value edge image can be obtained; the two-value edge image is converted into a gradually-varied gray-level edge image through a distance mapping function; the gradually-varied edge image serves as the edge detection operator in an anisotropy diffusion equation, and noise reduction of the ultrasound image is achieved through a numerical solution method with a finite difference in an iterative mode. The method is capable of effectively reducing noise and maintaining the edge and high in robustness under the condition of large noise.

Description

technical field [0001] The invention belongs to the field of digital image noise reduction, in particular to a McIlhagga edge detection operator-based anisotropic diffusion image noise reduction method to suppress speckle noise in ultrasonic images. Background technique [0002] Ultrasound imaging is a commonly used clinical medical diagnostic technology, which has the advantages of real-time, convenience, no ionizing radiation, low cost, and non-invasive. However, its main disadvantage is that the image quality is not high due to the interference of speckle noise. Speckle noise is a multiplicative, spatially correlated noise. It not only increases the difficulty of image segmentation, feature extraction and quantitative analysis, but also affects the accuracy of doctors' diagnosis. The main purpose of ultrasonic image filtering is to suppress speckle noise while maintaining the edge as much as possible, which is a key link in ultrasonic image processing. [0003] Many te...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/00G06T7/13
Inventor 张麒陈帅
Owner SHANGHAI UNIV
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