Ultrasonic image processing method and ultrasonic diagnostic apparatus

Abstract

The invention discloses an ultrasonic image processing method and an ultrasonic diagnostic apparatus. The method comprises the following steps: acquiring an original ultrasonic image obtained after an ultrasonic probe transmits an ultrasonic signal to a human tissue; performing Laplacian pyramid decomposition on the original image to obtain N layers of images with different scales; n is an integer greater than 2; the larger the number of layers of the image is, the smaller the scale is; performing contrast and uniformity adjustment on the low-frequency sub-scale image of the Nth layer, performing adaptive noise processing and consistency enhancement processing on the N-2 middle-layer images respectively, and performing adaptive noise processing on the high-frequency sub-scale image of the first layer; and starting from the low-frequency sub-scale image of the Nth layer, performing up-sampling on the processing result of the current layer, and reconstructing the sampling result and the processing result of the next layer until the high-frequency sub-scale image of the first layer is completely reconstructed, thereby obtaining the target ultrasonic image. According to the embodiment of the invention, the quality of the ultrasonic image during ultrasonic diagnosis of the human tissue can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of image processing, and in particular to an ultrasonic image processing method and an ultrasonic diagnostic apparatus. Background Art

[0002] During the ultrasound diagnosis process, the ultrasound probe of the ultrasound diagnostic instrument detects human tissues and generates ultrasound images. Since the basic principle of ultrasound images is based on acoustic imaging technology, ultrasound images are easily interfered by sound wave scattering, interference, energy attenuation, internal and external noise, etc., coupled with the complexity of human tissues, interference is particularly obvious, resulting in low contrast and poor detail resolution of ultrasound images, which in turn leads to poor clarity of lesion structures.

[0003] The methods in the related art usually reduce the detail resolution of the ultrasonic image during the noise reduction process, making the details of the ultrasonic image blurred and unable to take both into account. Summary of the invention

[0004] In an exemplary embodiment of the present application, a method for processing an ultrasonic image and an ultrasonic diagnostic apparatus are provided to increase the contrast and detail resolution of an ultrasonic image and improve the quality of the ultrasonic image.

[0005] According to a first aspect of an exemplary embodiment, a method for processing an ultrasound image is provided. The method can be applied to an ultrasound diagnostic apparatus, and the method comprises at least the following steps:

[0006] Acquire an original ultrasound image obtained after the ultrasound probe transmits an ultrasound signal to human tissue;

[0007] Perform Laplace pyramid decomposition on the original image to obtain N layers of images of different scales; where N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale;

[0008] Starting from the Nth layer of images, the processing result of the current layer is upsampled, and the sampling result of the current layer is reconstructed with the processing result of the next layer until the image reconstruction of the first layer is completed to obtain the target ultrasound image;

[0009] The processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the 1st layer image respectively includes adaptive noise suppression processing.

[0010] According to a second aspect of the exemplary embodiment, there is provided an ultrasonic diagnostic apparatus, the ultrasonic diagnostic apparatus comprising a data transmission unit and a processor, wherein:

[0011] The data transfer unit is configured to perform:

[0012] Acquire an original ultrasound image obtained after the ultrasound probe transmits an ultrasound signal to human tissue;

[0013] The processor is configured to execute:

[0014] Perform Laplace pyramid decomposition on the original image to obtain N layers of images of different scales; where N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale;

[0015] Starting from the Nth layer of images, the processing result of the current layer is upsampled, and the sampling result of the current layer is reconstructed with the processing result of the next layer until the image reconstruction of the first layer is completed to obtain the target ultrasound image;

[0016] The processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the 1st layer image respectively includes adaptive noise suppression processing.

[0017] According to a third aspect of an exemplary embodiment, there is provided an apparatus for processing an ultrasound image, the apparatus comprising:

[0018] An image acquisition unit is used to: acquire an original ultrasonic image obtained after the ultrasonic probe transmits an ultrasonic signal to human tissue;

[0019] The image processing unit is used to: perform Laplace pyramid decomposition on the original image to obtain N layers of images of different scales; wherein N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale;

[0020] The reconstruction unit is used to: start from the Nth layer image, upsample the processing result of the current layer, and reconstruct the sampling result of the current layer with the processing result of the next layer, until the image reconstruction of the first layer is completed to obtain the target ultrasound image;

[0021] The processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the 1st layer image respectively includes adaptive noise suppression processing.

[0022] According to a fourth aspect of the exemplary embodiment, a computer storage medium is provided, in which computer program instructions are stored. When the instructions are executed on a computer, the computer executes the ultrasound image processing method of the first aspect.

[0023] In the embodiment of the present application, the original ultrasound image obtained after the ultrasound probe transmits an ultrasound signal to human tissue is obtained; the original image is decomposed into a Laplace pyramid to obtain N layers of images of different scales; wherein N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale; starting from the Nth layer image, the processing result of the current layer is upsampled, and the sampling result of the current layer is reconstructed with the processing result of the next layer until the image reconstruction of the first layer is completed to obtain the target ultrasound image; wherein, the processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the processing of the first layer image respectively include adaptive noise suppression processing. Such a design, combined with the inherent characteristics of ultrasound images, processes images in different scale spaces in a targeted manner, not only well filters out the noise components, but also enhances the structural information, improves the contrast of the original ultrasound image and the clarity of the structural details, and thus better improves the overall quality of the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0025] Figure 1 The hardware configuration block diagram of an ultrasonic diagnostic instrument provided in an embodiment of the present application is exemplarily shown;

[0026] Figure 2 An application scenario diagram of an ultrasound data processing method provided by an embodiment of the present application is exemplarily shown;

[0027] Figure 3 A flowchart of a method for processing an ultrasound image provided by an embodiment of the present application is exemplarily shown;

[0028] Figure 4 A schematic diagram of a pretreatment process provided by an embodiment of the present application is exemplarily shown;

[0029] Figure 5 A schematic diagram of a Laplace pyramid decomposition provided by an embodiment of the present application is exemplarily shown;

[0030] Figure 6 A schematic diagram of contrast and uniformity adjustment provided by an embodiment of the present application is exemplarily shown;

[0031] Figure 7 A schematic diagram of an edge tangent and normal direction provided by an embodiment of the present application is exemplarily shown;

[0032] Figure 8 A schematic diagram of a feature vector provided by an embodiment of the present application is exemplarily shown;

[0033] Fig. 9 The following is a schematic diagram showing the structure of an ultrasonic image processing device provided in an embodiment of the present application;

[0034] Fig.10 The structural diagram of an ultrasonic diagnostic instrument provided in an embodiment of the present application is exemplarily shown. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0036] For ease of understanding, the terms involved in the embodiments of the present application are explained below:

[0037] During the ultrasound diagnosis process, the ultrasound probe of the ultrasound diagnostic instrument detects human tissues and generates ultrasound images. Since the basic principle of ultrasound images is based on acoustic imaging technology, ultrasound images are easily interfered by sound wave scattering, interference, energy attenuation, internal and external noise, etc., coupled with the complexity of human tissues, interference is particularly obvious, resulting in low contrast and poor detail resolution of ultrasound images, which in turn leads to poor clarity of lesion structures.

[0038] The methods in the related art usually reduce the detail resolution of the ultrasonic image during the noise reduction process, making the details of the ultrasonic image blurred and unable to take both into account.

[0039] After introducing the design ideas of the embodiments of the present application, the following briefly introduces the application scenarios to which the technical solutions of the embodiments of the present application can be applied. It should be noted that the application scenarios introduced below are only used to illustrate the embodiments of the present application and are not limited. In specific implementation, the technical solutions provided by the embodiments of the present application can be flexibly applied according to actual needs.

[0040] It should be understood that Figure 1 The ultrasonic diagnostic apparatus shown is only an example, and the ultrasonic diagnostic apparatus may have more Figure 1 The more or less components shown in the figure can be combined with two or more components, or can have different component configurations. The various components shown in the figure can be implemented in hardware, software, or a combination of hardware and software including one or more signal processing and / or application specific integrated circuits.

[0041] Figure 1 Schematically shows a hardware configuration block diagram of an ultrasonic diagnostic apparatus according to an exemplary embodiment.

[0042] like Figure 1 As shown, the ultrasonic diagnostic apparatus may include, for example: a processor 11, a memory 12, a display unit 13 and an ultrasonic probe 14; wherein:

[0043] An ultrasonic probe 14, configured to transmit ultrasonic signals to human tissue;

[0044] The processor 11 is connected to the ultrasound probe 14, the display unit 13 and the memory 12 respectively, and is configured to execute: acquiring an original ultrasound image obtained after the ultrasound probe transmits an ultrasound signal to human tissue; performing Laplace pyramid decomposition on the original image to obtain N layers of images of different scales; wherein N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale; performing contrast and uniformity adjustment on the low-frequency sub-scale image of the Nth layer, performing adaptive noise processing and consistency enhancement processing on each of the images of the N-2 intermediate layers, and performing adaptive noise processing on the high-frequency sub-scale image of the first layer; starting from the low-frequency sub-scale image of the Nth layer, upsampling the processing result of the current layer, and reconstructing the sampling result with the processing result of the next layer until the reconstruction of the high-frequency sub-scale image of the first layer is completed to obtain the target ultrasound image;

[0045] A display unit 13, used for displaying a target ultrasound image;

[0046] The memory 12 is configured to store data required for displaying a target ultrasound image, and may include software programs, application interface data, and the like.

[0047] In addition, the present application embodiment also provides an optional application scenario of the ultrasonic image processing method, such as Figure 2 As shown, it includes an ultrasonic diagnostic apparatus 20 and a server 21;

[0048] The ultrasonic diagnostic apparatus 20 includes a display screen 2001 and an ultrasonic probe 2002;

[0049] The ultrasonic diagnostic instrument 20 acquires an original ultrasonic image after transmitting an ultrasonic signal to human tissue through an ultrasonic probe 2002, and sends the original ultrasonic image to a server 21. The server 21 performs Laplace pyramid decomposition, contrast and uniformity processing on the original ultrasonic image, reconstructs a target ultrasonic image obtained by processing each layer, and displays the ultrasonic image on a display screen 2001.

[0050] The server 21 is used to perform Laplace pyramid decomposition on the original image to obtain N layers of images of different scales, wherein N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale; perform contrast and uniformity adjustment on the low-frequency sub-scale image of the Nth layer, perform adaptive noise processing and consistency enhancement processing on each of the images of the N-2 intermediate layers, and perform adaptive noise processing on the high-frequency sub-scale image of the first layer; starting from the low-frequency sub-scale image of the Nth layer, up-sample the processing result of the current layer, and reconstruct the sampling result and the processing result of the next layer until the reconstruction of the high-frequency sub-scale image of the first layer is completed to obtain the target ultrasound image.

[0051] Of course, the method provided in the embodiment of the present application is not limited to Figure 1 or Figure 2 The application scenario shown can also be used in other possible application scenarios, which are not limited in the embodiments of the present application.

[0052] To further illustrate the technical solution provided by the embodiment of the present application, this is described in detail below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiment of the present application provides the method operation steps shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided in the embodiment of the present application.

[0053] refer to Figure 3 A flowchart of a method for processing an ultrasonic image is shown, which can be applied to an ultrasonic diagnostic instrument to illustrate the technical solution provided in an embodiment of the present application.

[0054] S301: The ultrasonic diagnostic apparatus obtains an original ultrasonic image obtained after the ultrasonic probe transmits an ultrasonic signal to human tissue.

[0055] S302: The ultrasonic diagnostic apparatus performs Laplace pyramid decomposition on the original image to obtain N layers of images of different scales.

[0056] Where N is an integer greater than 2; the larger the number of image layers, the smaller the scale.

[0057] S303: Starting from the Nth layer of image, the ultrasonic diagnostic apparatus upsamples the processing result of the current layer, and reconstructs the sampling result of the current layer with the processing result of the next layer, until the image reconstruction of the first layer is completed, thereby obtaining a target ultrasonic image;

[0058] The processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the 1st layer image respectively includes adaptive noise suppression processing.

[0059] In the embodiment of the present application, the original ultrasound image obtained after the ultrasound probe transmits an ultrasound signal to human tissue is obtained; the original image is decomposed into a Laplace pyramid to obtain N layers of images of different scales; wherein N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale; starting from the Nth layer image, the processing result of the current layer is upsampled, and the sampling result of the current layer is reconstructed with the processing result of the next layer until the image reconstruction of the first layer is completed to obtain the target ultrasound image; wherein, the processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the processing of the first layer image respectively include adaptive noise suppression processing. Such a design, combined with the inherent characteristics of ultrasound images, processes images in different scale spaces in a targeted manner, not only well filters out the noise components, but also enhances the structural information, improves the contrast of the original ultrasound image and the clarity of the structural details, and thus better improves the overall quality of the image.

[0060] In S301, the transmitting antenna of the ultrasonic probe transmits an ultrasonic signal to human tissue, and the receiving antenna of the ultrasonic probe receives echo data generated after the ultrasonic signal acts on human tissue or organ. The ultrasonic diagnostic instrument receives the echo data from the ultrasonic probe and obtains an original ultrasonic image according to the echo data.

[0061] If the original ultrasound image is not processed in any way, the original ultrasound image may have problems such as low contrast and poor detail resolution. Therefore, in the embodiment of the present application, in order to improve the contrast and detail resolution of the ultrasound image, images of different scales are extracted from the original ultrasound image, and these images of different scales are adaptively processed in a targeted manner to make full use of the information of the image of each scale, and then the processed image is reconstructed to obtain the target ultrasound image.

[0062] Due to the interference of noise signals, there are usually many broken spots and burr features in the original ultrasound image, which reduce the clarity of the original structure. Therefore, in the embodiment of the present application, the original ultrasound image is preprocessed by combining the spatial domain and the frequency domain, which can effectively remove the burr features and improve the clarity of the structure.

[0063] Specifically, the original ultrasound image usually shows slowly changing features in the spatial domain, which are low-frequency components in the Fourier transform results. However, there are also rapidly changing feature areas in the ultrasound image, such as strong boundary areas and isolated noise points. Such changes are high-frequency components in the Fourier transform results. Therefore, the high-frequency structural detail information corresponding to the frequency domain results can be enhanced, and the high-frequency noise information corresponding to the frequency domain results can be suppressed, which can improve the quality of the original ultrasound image to a certain extent.

[0064] Optional, Figure 4A schematic diagram of a pretreatment process provided in an embodiment of the present application, combined with Figure 4 , the ultrasonic diagnostic instrument can process the original ultrasonic image through steps A1-A3 to obtain a pre-processed image:

[0065] A1: The ultrasonic diagnostic apparatus performs Fourier transform on the original ultrasonic image, performs filtering processing on the result after the Fourier transform, and performs inverse Fourier transform on the result after the filtering processing to obtain a first image.

[0066] Among them, the ultrasonic diagnostic instrument will apply Fourier transform to convert the original ultrasonic image into frequency domain space, and filter and suppress the high frequency in the frequency domain result to remove high-frequency noise information.

[0067] A2: The ultrasonic diagnostic instrument performs low-pass filtering on the original ultrasonic image, and performs edge structure feature analysis on the result after low-pass filtering to obtain edge confidence.

[0068] In the process of removing high-frequency noise information, the high-frequency result features of the noise part may be lost. Therefore, the ultrasonic diagnostic instrument synchronously performs low-pass filtering on the original ultrasonic image, and can use algorithms such as Sobel and Canny to analyze the edge structure features and obtain edge confidence.

[0069] A3: The ultrasonic diagnostic apparatus determines a fusion coefficient according to the edge confidence, and uses the fusion coefficient to fuse the first image and the original ultrasonic image to obtain a preprocessed image.

[0070] Among them, for the area with high edge confidence, the fusion coefficient of the original ultrasound image is higher; for the area with low edge confidence, the fusion coefficient of the first image (inverse Fourier transform result) is higher.

[0071] In S302, after preprocessing the original ultrasonic image, the ultrasonic diagnostic apparatus continues to perform Laplace pyramid decomposition on the original image to obtain N layers of images of different scales.

[0072] Where N is an integer greater than 2. Images of different scales contain different information of the original input image. The larger the number of image layers, the smaller the scale. For example, among N layers of images of different scales, the length of the n+1th layer image is half the length of the nth layer image, and the width of the n+1th layer image is half the width of the nth layer image, 1≤n≤N. Figure 5 A schematic diagram of a Laplacian pyramid decomposition provided in an embodiment of the present application, for example, the original ultrasound image size is X*Y, and after Gaussian pyramid decomposition, images of multiple scales such as X*Y, 1 / 2X*1 / 2*Y, 1 / 4*X*1 / 4*Y..., etc., with the size decreasing layer by layer, can be obtained. Figure 5 Four layers are used to illustrate. Figure 5The 4 layers of images of different scales are the 4th layer LP3 layer, the 3rd layer LP2 layer, the 2nd layer LP1 layer and the 1st layer LP0 layer.

[0073] In S303, combined with the inherent characteristics of the ultrasound image, the low-frequency sub-scale image and the high-frequency sub-scale image are processed respectively in a targeted manner in different scale spaces, and the comprehensive quality of the image is further improved through differentiated processing methods.

[0074] First, for N-layer images, the processing of the N-layer image by the ultrasonic diagnostic apparatus includes contrast and uniformity adjustment. This type of operation can be achieved in the following ways:

[0075] The ultrasonic diagnostic instrument performs Fourier transform on the low-frequency sub-scale image of the Nth layer of the image, performs filtering processing on the result after the Fourier transform processing, and then performs inverse Fourier transform on the result after the filtering processing to adjust the contrast and uniformity of the low-frequency sub-scale image of the Nth layer.

[0076] Since the main purpose of multi-scale decomposition of an image is to reflect the structural features of different sizes of the image at different scales, and although the number of layers of multi-scale decomposition can be set to many layers, after too many downsampling times, the small-scale image detail information will become very small, which is not very meaningful for structural feature analysis. Therefore, in the embodiment of the present application, N can be set to 4.

[0077] Among them, N takes 4 as an example, then for the highest layer, that is, the 4th layer ( Figure 5 The low-frequency sub-scale image of the LP0 layer in the 4th layer is characterized in that the small texture area becomes relatively smooth due to multiple downsampling processes, but the main information such as large structural features is basically retained. Therefore, this layer of image is more suitable for contrast images and uniformity adjustment, and other processing such as texture enhancement is not performed. Exemplarily, the adjustment method is to first perform Fourier transform on the low-frequency sub-scale image of the 4th layer, filter the result after Fourier transform, and then perform inverse Fourier transform on the result after filtering.

[0078] Optionally, filtering is performed using the following formula:

[0079]

[0080] Among them, H(u,v) is the filter, DFT(u,v) is the result after Fourier transform, α, β, c are all estimated parameters; D0 is the cutoff frequency; the estimated parameters are adaptively adjusted based on the global grayscale threshold determined by the histogram result of the low-frequency sub-scale image of the Nth layer.

[0081] Figure 6A schematic diagram of contrast and uniformity adjustment is provided for an embodiment of the present application, wherein a histogram is calculated based on an input original ultrasound image, and then a threshold parameter is estimated, and the obtained estimated parameters can be used as estimated parameters of a filter.

[0082] Second, for the Nth intermediate layer image and the first layer image, the ultrasonic diagnostic apparatus performs adaptive noise suppression processing. This type of operation can be achieved in the following ways:

[0083] The ultrasonic diagnostic apparatus uses a partial differential denoising method to perform adaptive noise suppression processing on the high-frequency sub-scale images in the N-2 intermediate layer images and the high-frequency sub-scale images in the first layer images.

[0084] In the example where N=4, the N-2 intermediate layers are respectively the third LP2 layer and the second LP1 layer. Optionally, the target image is adaptively processed by using the partial differential denoising method in the following manner, including:

[0085]

[0086]

[0087] Wherein, I represents the target image, which is a high-frequency sub-scale image in any image of the N-2 intermediate layers or a high-frequency sub-scale image in the first layer image. Indicates that Gaussian low-pass filtering is performed on the target image. represents the gradient of the target image, Represents the absolute value of the image gradient.

[0088] In addition, since the first layer image is the bottom layer image and the scale of this application is relatively large, there is no need to use consistency enhancement processing, but only adaptive noise suppression processing is performed on its high-frequency sub-scale image.

[0089] Next, the reconstruction process is described. Starting from the Nth layer image, the processing result of the current layer is upsampled, and the sampling result of the current layer is reconstructed with the processing result of the next layer until the image reconstruction of the first layer is completed to obtain the target ultrasound image.

[0090] The reconstruction operation may be a pyramid reconstruction operation, that is, the processing results at different scales are reconstructed respectively, and finally an ultrasound image processing result with the same size as the original ultrasound image is obtained. For example, the sizes of the 4th, 3rd, 2nd, and 1st layer images are X*Y, 1 / 2X*1 / 2Y, 1 / 4X*1 / 4Y, and 1 / 8X*1 / 8Y, respectively. The reconstruction process starts from the 4th layer, so that the size of the next layer is expanded to the size of the previous layer, and then reconstructed with the result of the previous layer until the 1st layer, and the target ultrasound image is obtained.

[0091] Specifically, the reconstruction process can be implemented through steps B1-B3:

[0092] B1: Fuse the low-frequency sub-scale image in the sampling result of the current layer with the high-frequency sub-scale image in the processing result of the next layer, and perform consistency enhancement processing on the fusion result. The consistency enhancement processing here can also be called structural information enhancement processing, which can enhance the structural information in the high-frequency sub-scale image.

[0093] Specifically, the fusion result can be processed for consistency enhancement through steps B1-1 to B1-4:

[0094] B1-1: Identify the structure tensor matrix in the fusion result.

[0095] Among them, the fusion result is also a fused image. Each image has a corresponding structure tensor matrix. Therefore, the structure tensor matrix of the fused image can be calculated

[0096] B1-2: Decompose the structure tensor matrix to obtain the eigenvalues.

[0097] Optionally, the structure tensor matrix is ​​decomposed to obtain the eigenvalues ​​using the following formula:

[0098]

[0099] Among them, J represents the structure tensor matrix, I x represents the horizontal gradient, I y represents the vertical gradient, Represents the square value of the horizontal gradient, G σ represents a Gaussian kernel with standard deviation σ, ω 1 ,ω 2 They represent the eigenvectors respectively. These two eigenvectors reflect the two directions with the largest and smallest grayscale differences in the neighborhood. 1 ,μ 2 They represent the eigenvalues ​​respectively, and these two eigenvalues ​​reflect the maximum and minimum intensity of the neighborhood grayscale difference.

[0100] Figure 7 A schematic diagram of an edge tangent and normal direction provided in an embodiment of the present application, Figure 8 A schematic diagram of a characteristic vector provided in an embodiment of the present application, wherein T1 and T2 represent characteristic vectors in the tangent direction, and N1 and N2 represent characteristic vectors in the normal direction, respectively.

[0101] B1-3: Apply the preset eigenvalues ​​to replace the eigenvalues ​​and determine the new structure tensor matrix.

[0102] Among them, the preset characteristic values ​​are as follows:

[0103]

[0104] μ 21 =α

[0105] Among them, α, β, and K are setting parameters. In this example, μ 11 Replace μ 1 , μ 21 Replace μ 2 , and then determine the new structure tensor matrix.

[0106] B1-4: Apply the new structure tensor to differentiate the fusion result to enhance the consistency of the fusion result.

[0107] Optionally, the fusion result can be processed for consistency enhancement using the following formula: Wherein, J1 is the new structure tensor matrix, and I1 is the fused image in step B1-1. Such a design can obtain a consistency enhancement result, and the structural information in the fused image will be enhanced.

[0108] B2: Reconstruct the sampling result of the current layer and the result of the consistency enhancement processing.

[0109] The reconstruction operation may refer to the image reconstruction process in the related art, which will not be described in detail here.

[0110] In the embodiment of the present application, large structural features usually require better detection accuracy at low scales, while small structural features generally require better prediction accuracy at high scales. Therefore, for sub-images of different scales after multi-scale decomposition, different size structural feature information needs to be considered. In addition, the high-frequency sub-scale image contains the main contour structure of the image, but also carries a large amount of noise information. Therefore, in the embodiment of the present application, not only the low-frequency sub-scale image is processed, because this does not remove the noise components in the image very well, but also the high-frequency sub-scale image is subjected to structural enhancement and noise suppression processing, which improves the contrast and structural detail clarity of the original ultrasound image, thereby better improving the overall quality of the image.

[0111] like Fig. 9 As shown, based on the same inventive concept, an embodiment of the present application provides an ultrasound image processing device including an image acquisition unit 91, an image processing unit 92 and a reconstruction unit 93.

[0112] The image acquisition unit 91 is used to: acquire an original ultrasonic image obtained after the ultrasonic probe transmits an ultrasonic signal to human tissue;

[0113] The image processing unit 92 is used to: perform Laplace pyramid decomposition on the original image to obtain N layers of images of different scales, wherein N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale;

[0114] The reconstruction unit 93 is used to: start from the Nth layer image, upsample the processing result of the current layer, and reconstruct the sampling result of the current layer with the processing result of the next layer, until the image reconstruction of the first layer is completed to obtain the target ultrasound image;

[0115] The processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the 1st layer image respectively includes adaptive noise suppression processing.

[0116] In an optional implementation manner, the reconstruction unit 93 is specifically configured to:

[0117] Fuse the low-frequency sub-scale image in the sampling result of the current layer with the high-frequency sub-scale image in the processing result of the next layer, and perform consistency enhancement processing on the fusion result;

[0118] The sampling result of the current layer is reconstructed with the result of the consistency enhancement processing.

[0119] In an optional implementation, the reconstruction unit 93 is specifically configured to perform consistency enhancement processing on the fusion result in the following manner:

[0120] Identify the structure tensor matrix in the fusion result;

[0121] Decompose the structure tensor matrix to obtain the eigenvalues;

[0122] Applying the preset eigenvalues ​​to replace the eigenvalues, and determining a new structure tensor matrix;

[0123] The new structure tensor is applied to differentiate the fusion result to enhance the consistency of the fusion result.

[0124] In an optional implementation, the reconstruction unit 93 is specifically configured to: process the Nth layer image including contrast and uniformity adjustment in the following manner:

[0125] The low-frequency sub-scale image of the N-th layer image is Fourier transformed, and the result after the Fourier transform is filtered, and then the result after the filtering is inversely Fourier transformed to adjust the contrast and uniformity of the low-frequency sub-scale image of the N-th layer.

[0126] In an optional implementation, the reconstruction unit 93 is specifically configured to perform filtering processing using the following formula:

[0127]

[0128] Among them, DFT(u,v) is the result after Fourier transform, α, β, c are all estimated parameters; D0 is the cutoff frequency; the estimated parameters are adaptively adjusted based on the global grayscale threshold determined by the histogram result of the low-frequency sub-scale image of the Nth layer.

[0129] In an optional implementation manner, the reconstruction unit 93 is specifically configured to:

[0130] The partial differential denoising method is used to perform adaptive noise suppression processing on the high-frequency sub-scale images in the N-2 intermediate layer images and the high-frequency sub-scale images in the first layer image.

[0131] In an optional implementation, the reconstruction unit 93 is specifically configured to: perform adaptive noise suppression processing on the high-frequency sub-scale images in the N-2 intermediate layer images and the high-frequency sub-scale images in the first layer image respectively by using the partial differential denoising method in the following manner:

[0132]

[0133]

[0134] Wherein, I represents the target image, which is a high-frequency sub-scale image in any image of the N-2 intermediate layers or a high-frequency sub-scale image in the first layer image. Indicates that the target image is subjected to Gaussian low-pass filtering. represents the gradient of the target image, Represents the absolute value of the image gradient.

[0135] In an optional implementation, a preprocessing unit is further included, which is used to preprocess the original ultrasound image before performing Laplace pyramid decomposition on the original image to obtain a preprocessed image.

[0136] In an optional implementation manner, the preprocessing unit is specifically used for:

[0137] Performing Fourier transform on the original ultrasonic image, performing filtering on the result after the Fourier transform, and performing inverse Fourier transform on the result after the filtering to obtain a first image;

[0138] The original ultrasound image is low-pass filtered, and the edge structure feature analysis is performed on the result after low-pass filtering to obtain the edge confidence;

[0139] According to the edge confidence, a fusion coefficient is determined, and the fusion coefficient is applied to fuse the first image and the original ultrasound image to obtain a preprocessed image.

[0140] Since the device is the device in the method in the embodiment of the present application, and the principle of solving the problem by the device is similar to that of the method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0141] like Fig.10 As shown, based on the same inventive concept, an embodiment of the present application provides an ultrasonic diagnostic instrument, which includes: a processor 1001 and a data transmission unit 1002.

[0142] The data transmission unit 1002 is configured to perform:

[0143] Acquire an original ultrasound image obtained after the ultrasound probe transmits an ultrasound signal to human tissue;

[0144] The processor 1001 is configured to execute:

[0145] Perform Laplace pyramid decomposition on the original image to obtain N layers of images of different scales; where N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale;

[0146] Starting from the Nth layer of images, the processing result of the current layer is upsampled, and the sampling result of the current layer is reconstructed with the processing result of the next layer until the image reconstruction of the first layer is completed to obtain the target ultrasound image;

[0147] The processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the 1st layer image respectively includes adaptive noise suppression processing.

[0148] In an optional implementation, the processor 1001 is configured to execute:

[0149] Fuse the low-frequency sub-scale image in the sampling result of the current layer with the high-frequency sub-scale image in the processing result of the next layer, and perform consistency enhancement processing on the fusion result;

[0150] The sampling result of the current layer is reconstructed with the result of the consistency enhancement processing.

[0151] In an optional implementation, the processor 1001 is configured to execute:

[0152] The fusion results are processed for consistency enhancement in the following ways:

[0153] Identify the structure tensor matrix in the fusion result;

[0154] Decompose the structure tensor matrix to obtain the eigenvalues;

[0155] Applying the preset eigenvalues ​​to replace the eigenvalues, and determining a new structure tensor matrix;

[0156] The new structure tensor is applied to differentiate the fusion result to enhance the consistency of the fusion result.

[0157] In an optional implementation, the processor 1001 is configured to execute:

[0158] The Nth layer image is processed as follows including contrast and uniformity adjustment:

[0159] The low-frequency sub-scale image of the N-th layer image is Fourier transformed, and the result after the Fourier transform is filtered, and then the result after the filtering is inversely Fourier transformed to adjust the contrast and uniformity of the low-frequency sub-scale image of the N-th layer.

[0160] In an optional implementation, the processor 1001 is configured to execute:

[0161] The filtering is performed using the following formula:

[0162]

[0163] Among them, DFT(u,v) is the result after Fourier transform, α, β, c are all estimated parameters; D0 is the cutoff frequency; the estimated parameters are adaptively adjusted based on the global grayscale threshold determined by the histogram result of the low-frequency sub-scale image of the Nth layer.

[0164] In an optional implementation, the processor 1001 is further configured to execute:

[0165] The partial differential denoising method is used to perform adaptive noise suppression processing on the high-frequency sub-scale images in the N-2 intermediate layer images and the high-frequency sub-scale images in the first layer image.

[0166] In an optional implementation, the processor 1001 is configured to execute:

[0167] The partial differential denoising method is used to perform adaptive noise suppression on the high-frequency sub-scale images in the N-2 intermediate layer images and the high-frequency sub-scale images in the first layer image respectively in the following manner:

[0168]

[0169]

[0170] Wherein, I represents the target image, which is a high-frequency sub-scale image in any image of the N-2 intermediate layers or a high-frequency sub-scale image in the first layer image. Indicates that Gaussian low-pass filtering is performed on the target image. represents the gradient of the target image, Represents the absolute value of the image gradient.

[0171] In an optional implementation, before performing Laplacian pyramid decomposition on the original image, the processor 1001 is further configured to execute:

[0172] The original ultrasound image is preprocessed to obtain a preprocessed image.

[0173] The processor 1001 is configured to execute:

[0174] Performing Fourier transform on the original ultrasonic image, performing filtering on the result after the Fourier transform, and performing inverse Fourier transform on the result after the filtering to obtain a first image;

[0175] The original ultrasound image is low-pass filtered, and the edge structure feature analysis is performed on the result after low-pass filtering to obtain the edge confidence;

[0176] According to the edge confidence, a fusion coefficient is determined, and the fusion coefficient is applied to fuse the first image and the original ultrasound image to obtain a preprocessed image.

[0177] The embodiment of the present application further provides a computer storage medium, in which computer program instructions are stored. When the instructions are executed on a computer, the computer executes the steps of the above-mentioned ultrasound image processing method.

[0178] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0179] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0180] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0182] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for processing an ultrasonic image, characterized in that: include: Acquire an original ultrasound image obtained after the ultrasound probe transmits an ultrasound signal to human tissue; Performing Laplace pyramid decomposition on the original image to obtain N layers of images of different scales, wherein N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale; Starting from the Nth layer of image, upsampling the processing result of the current layer, and reconstructing the sampling result of the current layer and the processing result of the next layer, until the image reconstruction of the first layer is completed, thereby obtaining a target ultrasound image; The processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the 1st layer image respectively includes adaptive noise suppression processing.

2. The method according to claim 1, characterized in that The reconstructing operation of the sampling result of the current layer and the processing result of the next layer includes: Fusing the low-frequency sub-scale image in the sampling result of the current layer with the high-frequency sub-scale image in the processing result of the next layer, and performing consistency enhancement processing on the fusion result; The sampling result of the current layer and the result of the consistency enhancement processing are reconstructed.

3. The method according to claim 2, characterized in that The fusion results are processed for consistency enhancement in the following ways: identifying a structure tensor matrix in the fusion result; Decomposing the structure tensor matrix to obtain eigenvalues; Applying a preset eigenvalue to replace the eigenvalue to determine a new structure tensor matrix; The new structure tensor is applied to perform differentiation processing on the fusion result to perform consistency enhancement processing on the fusion result.

4. The method according to claim 1, characterized in that The Nth layer image is processed as follows including contrast and uniformity adjustment: The low-frequency sub-scale image of the N-th layer image is subjected to Fourier transformation, and the result after the Fourier transformation is subjected to filtering processing, and then the result after the filtering processing is subjected to inverse Fourier transformation, so as to adjust the contrast and uniformity of the low-frequency sub-scale image of the N-th layer.

5. The method according to claim 4, characterized in that The filtering is performed using the following formula: Among them, DFT (u, v) is the result after Fourier transform, α, β, c are all estimated parameters; D0 is the cutoff frequency; the estimated parameter is adaptively adjusted according to the global gray threshold determined by the histogram result of the low-frequency sub-scale image of the Nth layer.

6. The method according to claim 1, characterized in that The method further comprises: The partial differential denoising method is used to perform adaptive noise suppression processing on the high-frequency sub-scale images in the N-2 intermediate layer images and the high-frequency sub-scale images in the first layer image.

7. The method according to claim 6, characterized in that The partial differential denoising method is used to perform adaptive noise suppression on the high-frequency sub-scale images in the N-2 intermediate layer images and the high-frequency sub-scale images in the first layer image respectively in the following manner: Wherein, I represents a target image, and the target image is a high-frequency sub-scale image in an image of any one of the N-2 intermediate layers or a high-frequency sub-scale image in an image of the first layer, Indicates that Gaussian low-pass filtering is performed on the target image. represents the gradient of the target image, Represents the absolute value of the image gradient.

8. The method according to any one of claims 1 to 7, characterized in that: Before performing Laplace pyramid decomposition on the original image, the method further includes: The original ultrasound image is preprocessed to obtain a preprocessed image.

9. The method according to claim 8, characterized in that The preprocessing of the original ultrasonic image to obtain the preprocessed image includes: Performing Fourier transform on the original ultrasound image, performing filtering on the result after the Fourier transform, and performing inverse Fourier transform on the result after the filtering to obtain a first image; Low-pass filtering the original ultrasound image, and performing edge structure feature analysis on the result after low-pass filtering to obtain edge confidence; A fusion coefficient is determined according to the edge confidence, and the fusion coefficient is applied to fuse the first image and the original ultrasound image to obtain a preprocessed image.

10. An ultrasonic diagnostic apparatus, characterized in that: including a data transmission unit and a processor; The data transmission unit is configured to perform: Acquire an original ultrasound image obtained after the ultrasound probe transmits an ultrasound signal to human tissue; The processor is configured to perform: Performing Laplace pyramid decomposition on the original image to obtain N layers of images of different scales, wherein N is an integer greater than 2; the larger the number of layers of the image, the smaller the scale; Starting from the Nth layer of image, upsampling the processing result of the current layer, and reconstructing the sampling result of the current layer and the processing result of the next layer, until the image reconstruction of the first layer is completed, thereby obtaining a target ultrasound image; The processing of the Nth layer image includes contrast and uniformity adjustment; the processing of the N-2 intermediate layer images and the 1st layer image respectively includes adaptive noise suppression processing.

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