CN115936963A

CN115936963A - Medical image robust watermarking method based on AKAZE-Curvelet-DCT

Info

Publication number: CN115936963A
Application number: CN202211655761.1A
Authority: CN
Inventors: 李京兵; 李德凯; 樊宇; 陈延伟; 黄梦醒
Original assignee: Hainan University
Current assignee: Hainan University
Priority date: 2022-12-22
Filing date: 2022-12-22
Publication date: 2023-04-07

Abstract

The application discloses a medical image robust watermarking method based on AKAZE-Curvelet-DCT, which comprises the following steps: performing AKAZE feature extraction on the medical image to obtain a feature descriptor matrix of the medical image; performing Curvelet change on the feature descriptor matrix of the medical image to obtain a low-frequency sub-band coefficient matrix of the medical image; performing DCT (discrete cosine transformation) on the low-frequency subband coefficient matrix of the medical image to generate a characteristic binary sequence of the medical image; and carrying out exclusive OR operation on the feature binary sequence of the medical image and the encrypted watermark bit by bit so as to embed the watermark information into the medical image. The AKAZE-Curvelet-DCT transformed data of the medical image are selected as the feature vectors, so that the defect that the traditional digital watermarking method cannot protect the medical image can be overcome, the robustness and the invisibility are high, and the privacy information of a patient and the data security of the medical image can be simultaneously protected.

Description

Medical image robust watermarking method based on AKAZE-Curvelet-DCT

Technical Field

The invention relates to the field of image processing, in particular to a medical image robust watermarking method based on AKAZE-Curvelet-DCT.

Background

With the continuous update and iteration of internet technology, the medical field has gradually evolved from traditional medicine to remote medicine, so that a large amount of medical information needs to be transmitted and shared in a network, which contains a large amount of medical images, so that in the process, the medical images may be subject to the problems of tampering, embezzlement and the like, and in order to solve the problems, the original medical images need to be processed: the zero watermark technology and the perceptual hash technology are combined to serve as a technology of information safety, so that not only can safe transmission be guaranteed, but also information authentication can be realized, and the method has important application in practical application.

The digital watermarking technology is to embed some identification information (i.e. digital watermark) directly into a digital carrier (including multimedia, documents, software, etc.), but does not affect the use value of the original carrier, and is not easily perceived or noticed by human perceptual systems (such as visual or auditory systems). The information hidden in the carrier can achieve the purposes of confirming content creators and purchasers, transmitting secret information, judging whether the carrier is tampered or not and the like. Digital watermarking is an important research direction of information hiding technology. In medicine, in order to protect personal information of a patient, the personal information of the patient can be hidden in a medical image by using the characteristics of invisibility, robustness and the like of a digital watermark, so that the aim of safe transmission is fulfilled. Through the unique invisibility, robustness and other characteristics of the digital watermark, the personal information of the patient is protected, and the zero watermark can avoid tampering medical data, so that the remote medical diagnosis can obtain the required relevant patient information.

At present, the number of medical images transmitted over a network is increasing, and medical data thereof is generally not allowed to be modified. Therefore, how to embed a robust digital watermark in medical data to reduce the modification of the content thereof is a technical problem to be solved urgently by those skilled in the art.

Disclosure of Invention

In view of this, the invention aims to provide a medical image robust watermarking method based on AKAZE-Curvelet-DCT, which can make up for the defect that the traditional digital watermarking method cannot protect the medical image, and has strong robustness and invisibility. The specific scheme is as follows:

a medical image robust watermarking method based on AKAZE-Curvelet-DCT comprises the following steps:

performing AKAZE feature extraction on a medical image to obtain a feature descriptor matrix of the medical image;

performing Curvelet change on the feature descriptor matrix of the medical image to obtain a low-frequency sub-band coefficient matrix of the medical image;

performing DCT (discrete cosine transformation) on the low-frequency subband coefficient matrix of the medical image to generate a characteristic binary sequence of the medical image;

and carrying out exclusive OR operation on the feature binary sequence of the medical image and the encrypted watermark bit by bit so as to embed watermark information into the medical image.

Preferably, in the method for robust watermarking of medical images based on AKAZE-Curvelet-DCT provided in the embodiment of the present invention, extracting AKAZE features from medical images to obtain a feature descriptor matrix of the medical images includes:

performing AKAZE feature extraction on the medical image to obtain feature points of the medical image;

and obtaining a feature descriptor matrix of the medical image by utilizing an M-LDB algorithm according to the feature points of the medical image.

Preferably, in the medical image robust watermarking method based on akage-Curvelet-DCT provided in the embodiment of the present invention, performing Curvelet change on a feature descriptor matrix of the medical image includes:

and performing Curvelet change on the feature descriptor matrix of the medical image by using a two-dimensional FFT algorithm and a Wrap algorithm of non-uniform spatial sampling.

Preferably, in the method for robust watermarking of a medical image based on AKAZE-Curvelet-DCT provided in the embodiment of the present invention, the DCT transform is performed on a low-frequency subband coefficient matrix of the medical image to generate a feature binary sequence of the medical image, including:

performing DCT (discrete cosine transformation) on the low-frequency subband coefficient matrix of the medical image to obtain a coefficient matrix of the medical image;

selecting 2 x 16 matrixes at the low frequency of the coefficient matrix of the medical image to form a new matrix;

and generating a 32-bit feature binary sequence of the medical image by using a hash function.

Preferably, in the method for robust watermarking of medical images based on AKAZE-Curvelet-DCT provided in the embodiment of the present invention, before performing an exclusive or operation on the binary sequence of features of the medical images and the encrypted watermark bit by bit, the method further includes:

generating a chaotic sequence;

and generating a binary sequence by using a hash function for the chaotic sequence, and carrying out exclusive-or scrambling on the position space of the original watermark according to the binary sequence to obtain the chaotic scrambled watermark as the encrypted watermark.

Preferably, in the method for robust watermarking of medical images based on AKAZE-Curvelet-DCT provided in the embodiment of the present invention, the method further includes:

generating a logical key while embedding watermark information into the medical image;

performing AKAZE feature extraction on a medical image to be detected to obtain a feature descriptor matrix of the medical image to be detected;

performing Curvelet change on the feature descriptor matrix of the medical image to be detected to obtain a low-frequency sub-band coefficient matrix of the medical image to be detected;

performing DCT (discrete cosine transformation) on the low-frequency sub-band coefficient matrix of the medical image to be detected to generate a visual characteristic sequence of the medical image to be detected;

and carrying out XOR operation on the visual characteristic sequence of the medical image to be detected and the logic key to extract a new encrypted watermark.

Preferably, in the method for robust watermarking of a medical image based on AKAZE-Curvelet-DCT according to the embodiment of the present invention, extracting AKAZE features of a medical image to be detected to obtain a feature descriptor matrix of the medical image to be detected includes:

performing AKAZE feature extraction on a medical image to be detected to obtain feature points of the medical image to be detected;

and obtaining a characteristic descriptor matrix of the medical image to be detected by utilizing an M-LDB algorithm according to the characteristic points of the medical image to be detected.

Preferably, in the akage-currvelet-DCT-based medical image robust watermarking method provided in the embodiment of the present invention, performing DCT transform on a low-frequency sub-band coefficient matrix of the medical image to be detected to generate a visual feature sequence of the medical image to be detected includes:

performing DCT (discrete cosine transformation) on the low-frequency sub-band coefficient matrix of the medical image to be detected to obtain a coefficient matrix of the medical image to be detected;

selecting 2 x 16 matrixes at the low frequency of the coefficient matrix of the medical image to be detected to form a new matrix;

and generating a 32-bit visual feature sequence of the medical image to be detected by utilizing a hash function.

Preferably, in the method for robust watermarking of medical images based on akage-Curvelet-DCT provided in the embodiment of the present invention, after extracting a new encrypted watermark, the method further includes:

and carrying out XOR reduction on the position space of the new encrypted watermark according to the binary sequence to obtain the decrypted watermark.

Preferably, in the medical image robust watermarking method based on akage-Curvelet-DCT provided in the embodiment of the present invention, after obtaining the decrypted watermark, the method further includes:

and calculating a correlation coefficient NC between the original watermark and the decrypted watermark, and determining ownership of the medical image and embedded watermark information.

According to the technical scheme, the AKAZE-Curvelet-DCT-based medical image robust watermarking method comprises the following steps: performing AKAZE feature extraction on the medical image to obtain a feature descriptor matrix of the medical image; performing Curvelet change on the feature descriptor matrix of the medical image to obtain a low-frequency sub-band coefficient matrix of the medical image; performing DCT (discrete cosine transformation) on a low-frequency sub-band coefficient matrix of the medical image to generate a characteristic binary sequence of the medical image; and carrying out exclusive OR operation on the feature binary sequence of the medical image and the encrypted watermark bit by bit so as to embed the watermark information into the medical image.

According to the robust watermarking method for the medical image, provided by the invention, the characteristic vector matrix of the image is extracted through an AKAZE algorithm, curvelet transformation is carried out on the vector matrix to obtain a low-frequency sub-band coefficient matrix, DCT transformation is carried out on the low-frequency sub-band coefficient matrix to obtain a stable characteristic binary sequence, so that data obtained after AKAZE-Curvelet-DCT transformation of the selected medical image is used as a characteristic vector, and is subjected to bitwise XOR operation with the encrypted watermark, and therefore, watermarking information is embedded into the medical image, the defect that the traditional digital watermarking method cannot protect the medical image can be overcome, the robust watermarking method has strong robustness and invisibility, the quality of the medical image is ensured, and privacy information of a patient and data security of the medical image can be simultaneously protected.

Drawings

In order to more clearly illustrate the embodiments of the present invention or technical solutions in related arts, the drawings used in the description of the embodiments or related arts will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a flowchart of a robust watermarking method for a medical image based on AKAZE-Curvelet-DCT according to an embodiment of the present invention;

FIG. 2 is an original medical image provided by an embodiment of the present invention;

fig. 3 is an original watermark image provided by an embodiment of the present invention;

fig. 4 is an encrypted watermark image provided by an embodiment of the present invention;

fig. 5 shows a watermark extracted without adding interference according to an embodiment of the present invention;

FIG. 6 is a medical image with a Gaussian noise interference level of 5% according to an embodiment of the present invention;

fig. 7 shows a watermark extracted when the gaussian noise interference strength is 5% according to an embodiment of the present invention;

FIG. 8 is a JPEG compressed medical image with a compression quality of 20% provided by an embodiment of the present invention;

FIG. 9 shows a watermark extracted during JPEG compression with a compression quality of 20% according to an embodiment of the present invention;

fig. 10 is a medical image with a window size of [3x3] and 10 times of median filtering according to an embodiment of the present invention;

fig. 11 shows a watermark extracted after 10 times of median filtering, with a window size of [3x3] according to an embodiment of the present invention;

FIG. 12 is a medical image rotated 5 clockwise according to an embodiment of the present invention;

fig. 13 shows a watermark extracted by rotating 5 ° in time according to an embodiment of the present invention;

FIG. 14 is a medical image rotated 30 clockwise according to an embodiment of the present invention;

fig. 15 shows a watermark extracted by rotating 30 ° in time according to an embodiment of the present invention;

FIG. 16 is a medical image after zooming when the zoom factor is 0.5 according to an embodiment of the present invention;

fig. 17 shows a watermark extracted when the scaling factor is 0.5 according to an embodiment of the present invention;

FIG. 18 shows a zoomed medical image with a zoom factor of 2 according to an embodiment of the invention;

fig. 19 shows a watermark extracted when the scaling factor is 2 according to an embodiment of the present invention;

FIG. 20 is an image of a medical image provided by an embodiment of the present invention after being horizontally shifted to the right by 20%;

fig. 21 shows the watermark extracted after 20% horizontal right shift according to the embodiment of the present invention;

FIG. 22 is an image of a medical image shifted vertically by 30% according to an embodiment of the present invention;

fig. 23 shows the watermark extracted after vertically shifting up by 30% according to an embodiment of the present invention;

FIG. 24 is a medical image after 20% clipping along the Y-axis provided by an embodiment of the present invention;

fig. 25 shows an extracted watermark after 20% clipping along the Y-axis according to an embodiment of the present invention;

FIG. 26 is a medical image after 30% shearing along the X-axis provided by an embodiment of the present invention;

fig. 27 shows an extracted watermark after 30% clipping along the X-axis according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The invention provides a medical image robust watermarking method based on AKAZE-Curvelet-DCT, which comprises the following steps as shown in figure 1:

s101, performing AKAZE feature extraction on the medical image to obtain a feature descriptor matrix of the medical image;

specifically, the medical image of step S101 may be a 512 × 512 medical image as the original medical image, denoted as I (I, j), where I (I, j) represents a pixel gray value of the medical image. AKAZE feature extraction is carried out on the medical image I (I, j), and a feature descriptor matrix descriptors of the medical image I (I, j) can be obtained.

S102, performing Curvelet change on the feature descriptor matrix of the medical image to obtain a low-frequency sub-band coefficient matrix of the medical image;

specifically, curvelet change is performed on the feature descriptor matrix descriptors of the medical image I (I, j), so that the low-frequency subband coefficient matrix a (I, j) of the medical image I (I, j) can be obtained.

S103, performing DCT (discrete cosine transformation) on the low-frequency subband coefficient matrix of the medical image to generate a characteristic binary sequence of the medical image;

specifically, the feature binary sequence V (I, j) of the medical image I (I, j) can be generated by performing DCT transformation on the low-frequency subband coefficient matrix a (I, j) of the medical image I (I, j).

In the invention, the medical image I (I, j) is subjected to feature extraction based on AKAZE, curvelet and DCT. Medical image I (I, j), as a special type of image, requires integrity of the raw data. The invention adopts the zero watermark embedding technology, well overcomes the defects caused by the modification of the original image data by the traditional watermark embedding technology, and ensures the quality of the medical image I (I, j).

S104, performing exclusive OR operation on the feature binary sequence of the medical image and the encrypted watermark bit by bit to embed watermark information into the medical image;

specifically, the feature binary sequence V (I, j) of the medical image I (I, j) and the encrypted watermark BW (I, j) are subjected to a bitwise exclusive or operation to embed watermark information into the medical image I (I, j).

It should be noted that the encrypted watermark BW (i, j) in step S104 may be obtained by encrypting the original watermark. The original watermark can be a meaningful binary text image as a watermark embedded in the medical image, and is marked as W = { W (i, j) | W (i, j) =0,1; i is more than or equal to 1 and less than or equal to M ₁ ,1≤j≤M ₂ }，M ₁ And M ₂ Respectively the size length and width of the original watermark; w (i, j) represents the pixel grey value of the original watermark.

In the medical image robust watermarking method based on AKAZE-Curvelet-DCT provided by the embodiment of the invention, the characteristic vector matrix of the image is extracted through an AKAZE algorithm, then Curvelet transformation is carried out on the vector matrix to obtain a low-frequency sub-band coefficient matrix, and DCT transformation is carried out on the low-frequency sub-band coefficient matrix to obtain a stable characteristic binary sequence, so that the data after AKAZE-Curvelet-DCT transformation of the selected medical image is taken as the characteristic vector and subjected to bitwise XOR operation with the encrypted watermark, and the watermark information is embedded into the medical image, thereby overcoming the defect that the traditional digital watermarking method cannot protect the medical image, having strong robustness and invisibility, ensuring the quality of the medical image, and simultaneously protecting the privacy information of a patient and the data security of the medical image.

In specific implementation, in the method for robust watermarking of a medical image based on akage-Curvelet-DCT provided by the embodiment of the present invention, step S101 performs akage feature extraction on the medical image to obtain a feature descriptor matrix of the medical image, which may specifically include: performing AKAZE feature extraction on the medical image I (I, j) to obtain feature points of the medical image I (I, j); and obtaining a feature descriptor matrix descriptors of the medical image I (I, j) by using an M-LDB algorithm according to the feature points of the medical image I (I, j).

It should be noted that, the akage algorithm is based on nonlinear diffusion filtering, but it uses FED mathematical framework to dynamically speed up the computation speed of the nonlinear scale space, and the present invention proposes an improved local differential binary (M-LDB) descriptor. The AKAZE algorithm mainly comprises the following steps: constructing a nonlinear scale space, detecting and positioning feature points, and generating an M-LDB feature descriptor.

A nonlinear scale space is first constructed. The nonlinear diffusion filter describes that the image shows the change of brightness due to the difference of flow functions in different scale spaces, and can be expressed as the following formula:

in the formula: div is a divergence operator;

is a gradient operator; l is the image brightness; c (x, y, t) is the diffusion conduction function; and t is a scale parameter. Wherein the diffusion conduction function c (x, y, t) can be expressed in the form:

in the formula:

g can be defined according to different processing requirements for a gradient image obtained by performing Gaussian smooth filtering on an original image L.

The nonlinear scale space is based on a pyramid model and consists of O groups of images,wherein each group of images contains S sub-levels, O and S being identified by respective discrete indices O and S, respectively. In the invention, the resolution of each layer is consistent with that of the original image, and o and s and the scale parameter sigma of Gaussian filter _i The following mapping relationship exists:

in the formula: sigma ₀ Is an initial scale parameter; m = O × S is the total number of filtered images. The nonlinear diffusion filter performs an operation with a time-unit factor, and thus it is necessary to parametrize a scale in units of pixels to σ _i Mapping to a scale parameter t in time _i 。

Feature point detection and localization are then performed. AKAZE algorithm utilizes the above formula to obtain a filtering image L [ i epsilon (1 \8230M); of nonlinear scale space)]i, calculating Hessian determinant of each image normalized at different scales, wherein a normalization factor sigma of each specific image group based on a nonlinear scale space is blended in _i，norm :

In the formula:

and &>

Respectively the second-order partial derivatives of the ith image in the horizontal direction and the vertical direction; />

And

the second mixed partial derivatives in the horizontal direction and the vertical direction of the ith image are respectively.

And finally generating the M-LDB feature descriptor. After the feature point detection is completed, the feature point needs to be further described. The AKAZE algorithm is based on a local binary differential algorithm (LDB), and the invention obtains the feature descriptor matrix descriptors of the medical image by using an M-LDB algorithm. The M-LDB utilizes gradient and intensity information extracted from a nonlinear space, and has strong robustness in rotation invariance and scale invariance.

In specific implementation, in the method for robust watermarking of a medical image based on akage-Curvelet-DCT provided by the embodiment of the present invention, step S102 performs Curvelet change on a feature descriptor matrix of the medical image to obtain a low-frequency subband coefficient matrix of the medical image, which may specifically include: and performing Curvelet change on the feature descriptor matrix descriptors of the medical image I (I, j) by using a two-dimensional FFT algorithm and a Wrap algorithm of non-uniform spatial sampling to obtain a low-frequency sub-band coefficient matrix A (I, j) of the medical image I (I, j).

Specifically, the method for realizing the Curvelet transform of the fast discrete curve waves comprises a two-dimensional FFT algorithm and a Wrap algorithm of non-uniform space sampling, wherein curve coefficients are inner products of signals and wavelet functions.

In specific implementation, in the method for robust watermarking of a medical image based on akage-Curvelet-DCT provided in the embodiment of the present invention, step S103 performs DCT transform on a low-frequency sub-band coefficient matrix of the medical image to generate a feature binary sequence of the medical image, which may specifically include: performing DCT (discrete cosine transformation) on the low-frequency subband coefficient matrix A (I, j) of the medical image I (I, j) to obtain a coefficient matrix F (I, j) of the medical image I (I, j); wherein F (i, j) = DCT2 (a (i, j)); selecting a matrix of 2 x 16 at the low frequency of a coefficient matrix F (I, j) of the medical image I (I, j) to form a new matrix D (I, j); a hash function is used to generate a feature binary sequence V (I, j) of the 32-bit medical image I (I, j).

It can be understood that the main reason why most medical image watermarking algorithms cannot resist geometric attacks well is that: the digital watermark is embedded in pixels or transform coefficients, and a slight geometric transformation of the medical image often causes a large change in the values of the pixels or transform coefficients, so that the embedded watermark is easily attacked. If the visual characteristic vector reflecting the geometric characteristics of the image can be found, when the image is subjected to geometric transformation, the visual characteristic value of the image cannot be obviously mutated, and the watermark image can be compared through the visual characteristic vector, so that the watermark information authentication is completed. The AKAZE algorithm has the characteristics of resisting attacks such as rotation, scaling, translation, shearing and the like, a characteristic vector matrix of an image is extracted through the AKAZE algorithm, curvelet transformation is carried out on the vector matrix to obtain a low-frequency sub-band coefficient matrix, DCT transformation is carried out on the low-frequency sub-band coefficient matrix, a 2 x 16 matrix is selected in the low-frequency coefficient part of the transformed matrix, and a stable binary sequence can be obtained by utilizing a Hash function. According to human visual characteristics (HVS), the low-intermediate frequency signals have large influence on human vision and represent the main characteristics of the image, so that the AKAZE-Curvelet-DCT transformed data of the medical image selected by the invention is used as a visual characteristic vector.

In specific implementation, in the method for robust watermarking of a medical image based on AKAZE-Curvelet-DCT provided in the embodiment of the present invention, before performing an exclusive or operation on the binary sequence of characteristics of the medical image and the encrypted watermark bit by bit in step S104, the method may further include: generating a chaotic sequence X (j) according to the initial value X0, preferably setting the initial value of the chaotic coefficient to 0.2, the growth parameter to 4, and the iteration number to 1023; and generating a binary sequence from the chaotic sequence X (j) by using a hash function, and carrying out XOR scrambling on the position space of the original watermark W (i, j) according to the sequence of the binary sequence to obtain the chaotically scrambled watermark as an encrypted watermark BW (i, j).

In specific implementation, in the method for robust watermarking of a medical image based on akage-Curvelet-DCT provided in the embodiment of the present invention, the method may further include: generating a logic Key Key (I, j) while embedding the watermark information into the medical image I (I, j); wherein

Saving Key (i, j), which later on extracts the watermarkIt is used. The Key (I, j) is used as a Key to apply to a third party, so that ownership and use right of the original medical image I (I, j) can be obtained, and the purpose of protecting the medical image is achieved.

Secondly, performing AKAZE feature extraction on the medical image I ' (I, j) to be detected to obtain a feature descriptor matrix descriptors ' (I, j) of the medical image I ' (I, j) to be detected; performing Curvelet change on the feature descriptor matrix descriptors '(I, j) of the medical image I' (I, j) to be detected to obtain a low-frequency sub-band coefficient matrix A '(I, j) of the medical image I' (I, j) to be detected; performing DCT (discrete cosine transformation) on the low-frequency subband coefficient matrix A '(I, j) of the medical image I' (I, j) to be detected to generate a visual feature sequence V '(I, j) of the medical image I' (I, j) to be detected; performing exclusive-or operation on a visual characteristic sequence V ' (I, j) of a medical image I ' (I, j) to be detected and a logic Key Key (I, j), and extracting a new encrypted watermark BW ' (I, j); wherein,

the algorithm only needs the Key Key (i, j) when extracting the watermark, does not need the participation of an original image, and is a zero watermark extraction algorithm.

It is understood that the medical image I' (I, j) to be measured can be regarded as a medical image formed after the original medical image I (I, j) is subjected to geometric attacks such as rotation, translation, shearing and the like or conventional attacks after network transmission.

In specific implementation, in the medical image robust watermarking method based on akage-Curvelet-DCT provided in the embodiment of the present invention, the extracting of the akage feature of the medical image to be detected is performed to obtain a feature descriptor matrix of the medical image to be detected, which may specifically include: performing AKAZE feature extraction on the medical image I '(I, j) to be detected to obtain feature points of the medical image I' (I, j) to be detected; and obtaining a feature descriptor matrix descriptors ' (I, j) of the medical image I ' (I, j) to be detected by using an M-LDB algorithm according to the feature points of the medical image I ' (I, j) to be detected.

In specific implementation, in the method for robust watermarking of a medical image based on akage-currvelet-DCT provided in the embodiment of the present invention, DCT transform is performed on a low-frequency sub-band coefficient matrix of a medical image to be detected to generate a visual feature sequence of the medical image to be detected, which may specifically include: performing DCT (discrete cosine transformation) on the low-frequency sub-band coefficient matrix of the medical image I ' (I, j) to be detected to obtain a coefficient matrix D ' (I, j) of the medical image I ' (I, j) to be detected; wherein, D '(i, j) = DCT2 (a' (i, j)); selecting a matrix of 2 x 16 at the low frequency of a coefficient matrix D '(I, j) of the medical image I' (I, j) to be detected to form a new matrix; and generating a visual feature sequence V '(I, j) of the 32-bit medical image I' (I, j) to be tested by using a hash function.

In specific implementation, in the method for robust watermarking of medical images based on akage-Curvelet-DCT provided in the embodiment of the present invention, after extracting a new encrypted watermark, the method may further include: firstly, obtaining the same binary sequence by using the same method as watermark encryption; then, carrying out exclusive-or reduction on the position space of the new encrypted watermark BW '(i, j) according to the sequence of the binary sequence to obtain a decrypted watermark W' (i, j); wherein,

in specific implementation, in the method for robust watermarking of medical images based on akage-Curvelet-DCT provided in the embodiment of the present invention, after obtaining the decrypted watermark, the method may further include: and calculating a correlation coefficient NC between the original watermark W (I, j) and the decrypted watermark W' (I, j), and determining ownership and embedded watermark information of the medical image I (I, j).

Specifically, a Normalized Cross-correlation (NC) method is used to measure the quantitative similarity between the embedded original watermark and the extracted restored watermark, which is defined as:

the normalized correlation coefficient is a method for measuring the similarity of two images, and the similarity of the images can be objectively evaluated by data more accurately by solving the normalized correlation coefficient.

Therefore, the robust watermarking method for the medical image, provided by the invention, can comprise the processes of feature vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption based on AKAZE-Curvelet-DCT.

In the following, the invention will be further explained with reference to the drawings, and the subject of experimental testing is a 512X 512 abdominal medical image, see FIG. 2, denoted by I (I, j), where 1. Ltoreq. I, j. Ltoreq.512. Selecting a meaningful binary image as an original watermark, and recording as: w = { W (i, j) | W (i, j) =0,1; i is more than or equal to 1 and less than or equal to M1, j is more than or equal to 1 and less than or equal to M2, see FIG. 3, wherein the size of the watermark is 32 x 32.

Firstly, AKAZE-Curvelet-DCT transformation is carried out on an original image, and the invention takes 32 coefficients, namely a module of 4 x 8, in consideration of robustness and the capacity of one-time embedded watermarks. The initial value of the chaotic coefficient is set to be 0.2, the increment parameter is 4, and the iteration number is 32. Then, chaos encryption is carried out on the original watermark, and the encrypted watermark is shown in figure 4. After W' (i, j) is detected by a watermark algorithm, the invention judges whether a watermark is embedded or not by calculating a normalized correlation coefficient NC, and when the numerical value is closer to 1, the similarity is higher, thereby judging the robustness of the algorithm. The degree of distortion of a picture expressed by PSNR is smaller as the PSNR value is larger.

Fig. 5 shows the watermark extracted without interference, and it can be seen that NC =1.00, and the watermark can be accurately extracted.

The invention judges the conventional attack resistance and the geometric attack resistance of the digital watermarking method through specific experiments.

Gaussian noise is added to the watermark using an immunity () function. The table shows experimental data of the watermark resisting Gaussian noise interference. As can be seen from the table, when the gaussian noise strength is as high as 13%, the PSNR of the image after the attack is reduced to 11.61dB, and at this time, the extracted watermark, with the correlation coefficient NC =0.83, can still be accurately extracted, and the whole data is in the vicinity of 0.8. This demonstrates that gaussian noise can be combated with the invention.

FIG. 6 is a medical image at 5% Gaussian noise level, visually distinct from the original abdominal medical image;

fig. 7 shows a watermark extracted at 5% gaussian noise level, NC =0.86.

Table-watermark anti-gaussian noise interference data

	Noise intensity (%)	1	3	5	7	9	11	13
									AKAZE-Cuvelet-DCT	PSNR(dB)	22.01	17.42	15.36	14.02	13.01	12.23	11.61
AKAZE-Cuvelet-DCT	NC	1	0.86	0.86	0.82	0.78	0.86	0.83

JPEG compression is carried out on the abdominal medical image by adopting the image compression mass percentage as a parameter; and the second table is experimental data of resisting JPEG compression of the watermark. When the compression quality is 5%, the image quality is low, and the watermark can still be extracted, NC =0.90.

FIG. 8 is a medical image with a compression quality of 20%;

fig. 9 is a watermark extracted with a compression quality of 20%, NC =0.96.

anti-JPEG compression experimental data of table two watermark

	Compression quality (%)	5	10	15	20	25	30	35
									AKAZE-Cuvelet-DCT	PSNR(dB)	26.9	30.58	31.8	33.03	33.86	34.53	35.14
AKAZE-Cuvelet-DCT	NC	0.90	0.96	0.96	0.96	0.96	1	1

And the third table is experimental data of watermark median filtering attack resistance. It can be seen from the table that when the image is median filtered [7 × 7], 10 times filtered, NC =0.52, the watermark can still be extracted.

FIG. 10 is a medical image with mean filtering [3X3] 10 times;

fig. 11 is the watermark extracted 10 times by mean filtering [3x3], NC =0.81.

Anti-median filtering experimental data of table three watermarks

And the fourth table is the experimental data of watermark anti-rotation attack. It can be seen from the table that when the image is rotated 60 ° clockwise, NC =0.89, the watermark can still be extracted.

FIG. 12 is a medical image rotated 5 clockwise;

fig. 13 shows the watermark extracted by rotating 5 ° clockwise, and NC =1, which makes it possible to accurately extract the watermark.

FIG. 14 is a medical image rotated 30 clockwise;

fig. 15 shows the watermark extracted by rotating 30 ° clockwise, and NC =0.94, the watermark can be accurately extracted.

Table four watermark anti-rotation attack experimental data

	Rotate clockwise	5	10	20	30	40	50	60
									AKAZE-Cuvelet-DCT	PSNR(dB)	18.44	16.68	15.33	14.89	14.63	14.27	13.92
AKAZE-Cuvelet-DCT	NC	1	0.91	0.96	0.94	0.91	0.96	0.89

Table five shows the experimental data of the watermark anti-scaling attack of the medical image, and it can be seen from table five that when the scaling factor is as small as 0.3, the correlation coefficient NC =0.70, and the watermark can be extracted.

FIG. 16 is a zoomed medical image (zoom factor of 0.5);

fig. 17 shows the watermark extracted after the scaling attack, and NC =0.74, which makes it possible to accurately extract the watermark.

FIG. 18 is a zoomed medical image (zoom factor of 2);

fig. 19 shows the watermark extracted after the scaling attack, where NC =0.87, and the watermark can be accurately extracted.

Anti-scaling attack experimental data of table five watermark

	Scaling factor	0.3	0.5	0.8	1.2	1.5	2
								AKAZE-Cuvelet-DCT	NC	0.70	0.74	0.67	0.94	0.82	0.87

And the sixth table shows experimental data of watermark anti-translation transformation. When the vertical movement of the image data is 40%, the NC value is higher than 0.5, and the watermark can be accurately extracted, so that the watermark method has stronger translation transformation resistance.

FIG. 20 is an image after a medical image is horizontally shifted to the right by 20%;

fig. 21 shows a watermark extracted after a horizontal shift of 20%, where NC =0.86 can be accurately extracted.

FIG. 22 is an image of a medical image vertically shifted by 30%;

fig. 23 shows a watermark extracted after vertical shift by 30%, where NC =0.96 can be accurately extracted.

Table six watermark anti-translation transformation experimental data

The seventh table is the experimental data of the watermark shear attack resistance, and it can be seen from the table that when the medical image is sheared along the coordinate axis Y with a shearing amount of 40%, NC =0.80, and the medical image is sheared along the coordinate axis X with a shearing amount of 40%, NC =0.75, the watermark can still be extracted, which indicates that the watermarking algorithm has a strong shear attack resistance.

FIG. 24 is a medical image after 20% cropping along the Y-axis;

fig. 25 shows the watermark extracted after being cut by 20% along the Y axis, and the watermark can be accurately extracted, with NC =0.87.

FIG. 26 is a medical image after 30% cropping along the X-axis;

fig. 27 shows the watermark extracted after 30% of the watermark is cut along the X axis, and the watermark can be accurately extracted, with NC =0.94.

Shear attack resisting experimental data of table seven watermarks

The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.

The method for robust watermarking of medical images based on AKAZE-Curvelet-DCT provided by the invention is described in detail above, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the above embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A medical image robust watermarking method based on AKAZE-Curvelet-DCT is characterized by comprising the following steps:

2. The AKAZE-Curvelet-DCT-based medical image robust watermarking method according to claim 1, wherein AKAZE feature extraction is performed on a medical image to obtain a feature descriptor matrix of the medical image, and the method comprises the following steps:

3. The AKAZE-Curvelet-DCT-based medical image robust watermarking method according to claim 2, wherein Curvelet transformation is performed on a feature descriptor matrix of the medical image, and comprises:

4. The AKAZE-Curvelet-DCT-based medical image robust watermarking method according to claim 3, wherein DCT transformation is performed on a low-frequency sub-band coefficient matrix of the medical image to generate a feature binary sequence of the medical image, and comprises the following steps:

and generating a 32-bit feature binary sequence of the medical image by utilizing a hash function.

5. The AKAZE-Curvelet-DCT-based medical image robust watermarking method as claimed in claim 4, wherein before bitwise XOR operation of the feature binary sequence of the medical image and the encrypted watermark, further comprising:

generating a chaotic sequence;

6. The AKAZE-Curvelet-DCT-based medical image robust watermarking method according to claim 5, further comprising:

7. The AKAZE-Curvelet-DCT-based medical image robust watermarking method according to claim 6, wherein AKAZE feature extraction is performed on a medical image to be detected to obtain a feature descriptor matrix of the medical image to be detected, and the method comprises the following steps:

8. The AKAZE-Curvelet-DCT-based medical image robust watermarking method according to claim 7, wherein DCT transformation is performed on a low-frequency sub-band coefficient matrix of the medical image to be detected to generate a visual feature sequence of the medical image to be detected, and the method comprises the following steps:

selecting a matrix of 2 x 16 at the low frequency of the coefficient matrix of the medical image to be detected to form a new matrix;

9. The AKAZE-Curvelet-DCT-based medical image robust watermarking method according to claim 8, further comprising, after extracting a new encrypted watermark:

10. The akage-Curvelet-DCT-based medical image robust watermarking method according to claim 9, further comprising, after obtaining the decrypted watermark: