CN113160030B - Medical image robust watermarking method based on LBP-DCT - Google Patents
Medical image robust watermarking method based on LBP-DCT Download PDFInfo
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Abstract
The application discloses a medical image robust watermarking method based on LBP-DCT, comprising the following steps: performing LBP-DCT on the original medical image, and extracting the characteristic vector of the original medical image; chaotic scrambling is carried out on the original watermark by using a Logistic Map to obtain chaotic scrambling watermark, and a feature vector is associated with the chaotic scrambling watermark to obtain a logic key; performing LBP-DCT (local binary-discrete cosine transform) on the medical image to be detected, and extracting a characteristic vector of the medical image to be detected; the feature vector is correlated with the logic key to extract the encrypted watermark and decrypt the encrypted watermark to obtain a restored watermark; and carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the original medical image and embedded watermark information. Therefore, the advantages of LBP rotation invariance and the characteristics of strong DCT (discrete cosine transform) resistance to conventional attacks are considered, the feature vector resisting the geometric attacks is obtained, the content of original encrypted volume data is not changed by watermark embedding, and the quality of medical images is ensured.
Description
Technical Field
The invention relates to the field of multimedia signal processing, in particular to a medical image robust watermarking method based on LBP-DCT.
Background
Medical development is gradually transitioning from traditional medicine to telemedicine, which enables a large number of medical images to be transmitted and shared in a network; in order to solve the problems that medical images may suffer from tampering during transmission sharing, the original medical images need to be processed.
The digital watermarking technology is originally used for protecting copyright of digital media, and the characteristics of invisibility, robustness and the like of the digital watermarking are utilized at present, so that personal information of patients can be hidden in medical images of the patients to ensure safe transmission of the personal information on the Internet. Therefore, in the case where digital images are widely used in network transmission, research into digital watermarking algorithms for medical images has become extremely important.
However, the research on the digital watermarking algorithm of the medical image is less at present, most of the existing digital watermarking algorithms cannot effectively protect the medical image, and the geometric attack resistance is poor. The problem of large amount of medical data transmission which will be faced in the future, therefore, is significant in researching how to embed digital robust watermarks in medical data, and for medical data, the content of the medical data is generally not allowed to be modified, which in turn increases the difficulty of embedding the watermarks in the medical data.
Disclosure of Invention
Therefore, the invention aims to provide a medical image robust watermarking method based on LBP-DCT, which can ensure the quality of medical images, and has strong robustness and invisibility especially in the aspect of geometric attack. The specific scheme is as follows:
a medical image robust watermarking method based on LBP-DCT, comprising:
performing Local Binary Pattern (LBP) transformation on an original medical image to obtain a first LBP response matrix;
Performing discrete cosine DCT (discrete cosine transform) on the first LBP response matrix, and extracting feature vectors of the original medical image;
chaotic scrambling is carried out on the original watermark by using a Logistic Map to obtain chaotic scrambling watermark, and a logic key is obtained by associating the feature vector of the original medical image with the chaotic scrambling watermark;
carrying out local binary pattern LBP conversion on the medical image to be detected after network transmission to obtain a second LBP response matrix;
Performing discrete cosine DCT (discrete cosine transform) on the second LBP response matrix, and extracting feature vectors of the medical image to be detected;
Extracting an encrypted watermark by associating the feature vector of the medical image to be detected with the logic key, and decrypting the encrypted watermark to obtain a restored watermark;
And carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the original medical image and embedded watermark information.
Preferably, in the above-mentioned robust watermarking method for medical image based on LBP-DCT provided by the embodiment of the present invention, local binary pattern LBP transformation is performed on an original medical image to obtain a first LBP response matrix, which specifically includes:
dividing the original medical image into 16 x 16 regions;
in each region, eight pixels adjacent to a central pixel in the neighborhood of a pixel 3*3 are used as sampling points, the gray value of the central pixel is used as a threshold value, and after the gray value of the sampling points is compared with the threshold value, an eight-bit binary number is generated as an LBP value of the central pixel;
Calculating a histogram of each region according to the LBP value correspondingly generated by the original medical image, and carrying out normalization processing on the histogram to obtain a statistical histogram of each region;
And connecting the obtained statistical histograms of each region, and obtaining the LBP texture feature vector of the original medical image as a first LBP response matrix.
Preferably, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, discrete cosine DCT is performed on the first LBP response matrix, and feature vectors of the original medical image are extracted, which specifically includes:
Performing discrete cosine DCT (discrete cosine transform) on the first LBP response matrix to obtain a first coefficient matrix;
Selecting a module with a set size from the first coefficient matrix to form a new matrix;
and generating the feature vector of the original medical image by utilizing Hash function operation.
Preferably, in the method for robust watermarking of medical images based on LBP-DCT provided by the embodiment of the present invention, the chaotic scrambling is performed on the original watermark by using a Logistic Map, so as to obtain a chaotic scrambling watermark, which specifically includes:
generating a chaotic sequence through a Logistic Map;
Sorting the values in the chaotic sequence according to the order from small to large;
And scrambling the pixel position space in the original watermark according to the position change before and after each value in the chaotic sequence is sequenced, so as to obtain the chaotic scrambling watermark.
Preferably, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, the associating the feature vector of the original medical image with the chaotic scrambling watermark to obtain a logical key specifically includes:
Performing exclusive OR operation on the feature vector of the original medical image and the chaotic scrambling watermark bit by bit so as to embed the original watermark into the original medical image and obtain a logic key.
Preferably, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, after obtaining the logical key set, the method further includes:
And applying the logic key as a key to a third party and storing the logic key in the third party.
Preferably, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, local binary pattern LBP transformation is performed on a medical image to be measured after network transmission, and a second LBP response matrix is obtained, which specifically includes:
dividing the medical image to be detected into 16 x 16 areas;
in each region, eight pixels adjacent to a central pixel in the neighborhood of a pixel 3*3 are used as sampling points, the gray value of the central pixel is used as a threshold value, and after the gray value of the sampling points is compared with the threshold value, an eight-bit binary number is generated as an LBP value of the central pixel;
Calculating a histogram of each region according to the LBP value correspondingly generated by the medical image to be detected, and carrying out normalization processing on the histogram to obtain a statistical histogram of each region;
And connecting the obtained statistical histograms of each region to obtain an LBP texture feature vector of the medical image to be detected as a second LBP response matrix.
Preferably, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, discrete cosine DCT is performed on the second LBP response matrix, and extracting a feature vector of the medical image to be detected specifically includes:
Performing discrete cosine DCT (discrete cosine transform) on the second LBP response matrix to obtain a second coefficient matrix;
selecting a module with a set size in the second coefficient matrix to form a new matrix;
and generating the feature vector of the medical image to be detected by utilizing Hash function operation.
Preferably, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, extracting an encrypted watermark by associating the feature vector of the medical image to be detected with the logical key specifically includes:
and performing exclusive OR operation on the feature vector of the medical image to be detected and the logic key to extract the encrypted watermark.
Preferably, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, decrypting the encrypted watermark to obtain a restored watermark, specifically includes:
generating the chaotic sequence through a Logistic Map;
Sorting the values in the chaotic sequence according to the order from small to large;
And performing restoration operation on the pixel position space in the encrypted watermark according to the position change before and after each value in the chaotic sequence is sequenced, so as to obtain a restored watermark.
From the above technical solution, the medical image robust watermarking method based on LBP-DCT provided by the present invention includes: performing Local Binary Pattern (LBP) transformation on an original medical image to obtain a first LBP response matrix; performing discrete cosine DCT (discrete cosine transform) on the first LBP response matrix, and extracting feature vectors of the original medical image; chaotic scrambling is carried out on the original watermark by using the Logistic Map to obtain chaotic scrambling watermark, and a logical key is obtained by associating the feature vector of the original medical image with the chaotic scrambling watermark; carrying out local binary pattern LBP conversion on the medical image to be detected after network transmission to obtain a second LBP response matrix; performing discrete cosine DCT (discrete cosine transform) on the second LBP response matrix, and extracting feature vectors of the medical image to be detected; the feature vector of the medical image to be detected is correlated with the logic key to extract the encrypted watermark, and the encrypted watermark is decrypted to obtain the restored watermark; and carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the original medical image and embedded watermark information.
The medical image robust watermarking method provided by the invention combines local binary pattern LBP and discrete cosine DCT transformation of medical images based on five parts of feature vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption of LBP-DCT, combines the advantages of LBP rotation invariance and the characteristics of strong conventional attack resistance, traversal, robustness and the like of DCT, can find a feature vector resisting geometric attack, is particularly outstanding in terms of geometric attacks such as rotation, translation, shearing and the like, and the embedding of the watermark does not change the content of original encrypted volume data, thereby well solving the defects of the traditional watermark embedding technology on original image data modification, ensuring the quality of medical images, having strong robustness and invisibility, and simultaneously protecting the privacy information of patients and the data safety of medical images.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only embodiments of the present invention, and other drawings may be obtained according to the provided drawings without inventive effort for those skilled in the art.
FIG. 1 is a flowchart of a medical image robust watermarking method based on LBP-DCT according to an embodiment of the present invention;
FIG. 2 is a raw 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 a diagram of an encrypted chaotic scrambling watermark image provided by an embodiment of the present invention;
FIG. 5 is a watermark extracted without interference according to an embodiment of the present invention;
FIG. 6 is a medical image of the Gaussian noise disturbance intensity of 3% according to an embodiment of the present invention;
FIG. 7 is a watermark extracted when the Gaussian noise interference strength is 3% according to an embodiment of the present invention;
FIG. 8 is a median filtered medical image with a window size of [3x3] and a number of 10 filters provided in an embodiment of the present invention;
FIG. 9 is a watermark extracted after median filtering for 10 times of filtering, with a window size of [3x3] provided in an embodiment of the present invention;
FIG. 10 is a median filtered medical image with a window size of [5x5], a number of 10 times of filtering, provided in an embodiment of the present invention;
FIG. 11 is a watermark extracted after median filtering for 10 times of filtering, with a window size of [5x5] provided in an embodiment of the present invention;
FIG. 12 is a medical image rotated 10 clockwise provided by an embodiment of the present invention;
Fig. 13 is a watermark extracted when rotated 10 ° clockwise provided by an embodiment of the present invention;
FIG. 14 is a medical image rotated 3 clockwise as provided by an embodiment of the present invention;
Fig. 15 is a watermark extracted when rotated clockwise by 3 ° according to an embodiment of the present invention;
FIG. 16 is a medical image provided with 0.8 magnification according to an embodiment of the present invention;
FIG. 17 is a watermark extracted at 0.8 times scale provided by an embodiment of the present invention;
FIG. 18 is a medical image with 10% vertical shift provided by an embodiment of the present invention;
fig. 19 is a watermark extracted when the watermark is shifted down by 10% vertically according to an embodiment of the present invention;
FIG. 20 is a medical image with 20% vertical shift provided by an embodiment of the present invention;
FIG. 21 is a watermark extracted when 20% is shifted vertically downward according to an embodiment of the present invention;
FIG. 22 is a view of a medical image cut 25% along the Y-axis provided by an embodiment of the present invention;
FIG. 23 is a view of an embodiment of the present invention providing 25% cut of an extracted watermark along the Y-axis;
FIG. 24 is a view of a medical image cut 3% along the Y-axis provided by an embodiment of the present invention;
fig. 25 is a view of an embodiment of the present invention providing 3% cut of the extracted watermark along the Y-axis.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a medical image robust watermarking method based on LBP-DCT, as shown in figure 1, comprising the following steps:
s101, carrying out local binary pattern LBP conversion on an original medical image to obtain a first LBP response matrix;
in this embodiment, a 128×128 medical image may be selected as the original medical image, denoted as I (I, j), where I (I, j) represents the pixel gray value of the original medical image. The size of the original medical image may be determined according to practical situations, and is not limited herein.
Performing LBP transformation on the original medical image I (I, j) to obtain a first LBP response matrix:
[hist0,I_LBP0]＝getMBLBPFea(grayImage,1)。
LBP (Local Binary Pattern ) has significant advantages of rotation invariance and gray invariance, etc., and can be used for texture feature extraction.
S102, performing discrete cosine DCT on the first LBP response matrix, and extracting feature vectors of an original medical image;
S103, performing chaotic scrambling on the original watermark by using a Logistic Map to obtain a chaotic scrambling watermark, and correlating the feature vector of the original medical image with the chaotic scrambling watermark to obtain a logic key;
In practical application, the original watermark embedded in the medical image is a meaningful binary text image, and is marked as w= { W (i, j) |w (i, j) =0, 1; and i is more than or equal to 1 and is less than or equal to M 1,1≤j≤M2},M1 and M 2 are respectively the size length and width of the original watermark image, and W (i, j) represents the pixel gray value of the original watermark. The watermark is used for protecting personal information of a patient, and the personal information of the patient can be hidden in a medical image of the patient, so that safe transmission on a network is realized.
S104, carrying out local binary pattern LBP conversion on the medical image to be detected after network transmission to obtain a second LBP response matrix;
It is understood that the medical image to be measured herein may be considered as a medical image formed by the original medical image possibly subjected to geometric attacks such as gaussian noise interference, median filtering, compression, rotation, translation and the like or conventional attacks in the network transmission process.
S105, performing discrete cosine DCT on the second LBP response matrix, and extracting feature vectors of the medical image to be detected;
s106, extracting the encrypted watermark by associating the feature vector of the medical image to be detected with the logic key, and decrypting the encrypted watermark to obtain a restored watermark;
and S107, carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the original medical image and embedded watermark information.
In the medical image robust watermarking method based on LBP-DCT provided by the embodiment of the invention, based on the five parts of feature vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption of the LBP-DCT, the local binary pattern LBP of the medical image is combined with discrete cosine DCT transformation, the advantages of LBP rotation invariance and the characteristics of strong conventional attack resistance, traversal resistance, robustness and the like of the DCT are considered, so that a feature vector resistant to geometric attack can be found, the geometric attack such as rotation, translation and shearing is particularly outstanding, the embedding of the watermark does not change the content of original encrypted volume data, the defect caused by the traditional watermark embedding technology on original image data modification is well solved, the quality of the medical image is ensured, the robustness and the invisibility are very strong, and the privacy information of patients and the data security of the medical image can be simultaneously protected.
It should be noted that, the original LBP operator is defined in the neighborhood of the pixel 3*3, and the neighborhood center pixel is used as a threshold value, and is compared with the gray values of the adjacent 8 pixels, so that 8-bit binary numbers can be generated, and the 8-bit binary numbers are sequentially arranged into a binary number, and the binary number is the LBP value of the center pixel. An LBP operator containing P sample points for a circular region of radius R will produce P2 modes. It is apparent that the variety of binary patterns increases dramatically as the number of sampling points in a neighborhood set increases. So many binary patterns are detrimental to texture extraction, recognition and expression. Therefore, it is necessary to perform dimension reduction on the original LBP pattern so that information representing an image can be best in the case of a reduced data amount.
In order to solve the problem of excessive binary patterns and improve statistics, the invention adopts an 'equivalent Pattern' (Pattern) to reduce the dimension of Pattern types of LBP operators. "equivalent mode" is defined as: when a cyclic binary number corresponding to a certain LBP jumps from 0 to 1 or from 1 to 0 at most twice, the binary number corresponding to the LBP is called an equivalence pattern class. For example 00000000 (0 hops), 00000111 (only one hop from 0 to 1), 10001111 (first from 1 to 0 and then from 0 to 1, and two hops) are all equivalent pattern classes. Modes other than the equivalent mode class fall into another class, called a mixed mode class, such as 10010111 (total four hops). By such improvement, the variety of binary patterns is greatly reduced without losing any information. The number of modes is reduced from the original P2 types to P (P-1) +2 types, wherein P represents the number of sampling points in the neighborhood set. For 8 sampling points in the 3*3 neighborhood, the binary pattern is reduced from the original 256 to 58, which makes the feature vector fewer in dimension and can reduce the influence of high-frequency noise.
Further, in a specific implementation, in the above-mentioned robust watermarking method for medical image based on LBP-DCT provided by the embodiment of the present invention, step S101 performs local binary pattern LBP transformation on an original medical image I (I, j) to obtain a first LBP response matrix, which may specifically include: firstly, dividing an original medical image I (I, j) into 16 x 16 areas (cells); in each cell, eight pixels adjacent to the central pixel in the neighborhood of the pixel 3*3 are used as sampling points, the gray value of the central pixel is used as a threshold value, and after the gray value of the sampling points is compared with the threshold value, eight binary numbers are generated to be used as LBP values of the central pixel; specifically, comparing a pixel in each cell with gray values of eight adjacent pixels around, if the gray value of the adjacent pixels around is greater than that of the central pixel, marking the position of the adjacent pixels around as 1, otherwise, marking the position of the adjacent pixels around as 0; then, according to LBP values correspondingly generated by the original medical images I (I, j), calculating a histogram of each cell, namely the occurrence frequency of each number (assumed to be a decimal number LBP value), and carrying out normalization processing on the histogram to obtain a statistical histogram of each region; and finally, connecting the obtained statistical histograms of each cell (namely connecting the statistical histograms into a feature vector) to obtain an LBP texture feature vector of the original medical image I (I, j) as a first LBP response matrix.
In a specific implementation, in the above-mentioned robust watermarking method for medical image based on LBP-DCT provided by the embodiment of the present invention, step S102 performs discrete cosine DCT on the first LBP response matrix, and extracts a feature vector of an original medical image I (I, j), which may specifically include: firstly, performing discrete cosine DCT (discrete cosine transform) on a first LBP response matrix to obtain a first coefficient matrix F0 (I, j), F0 (I, j) =dct 2 (I_LBP0); then, selecting a module with a set size in the first coefficient matrix F0 (i, j) to form a new matrix key_orig (i, j); the feature vector V (I, j) of the original medical image I (I, j) is generated using a hash function operation. For example, a new matrix key_orig (I, j) can be formed by selecting 2×4 modules in the first coefficient matrix F0 (I, j), and a feature vector V (I, j) of the 8-bit original medical image I (I, j) is generated by using a hash function operation; or a 4*4 module in the first coefficient matrix F0 (I, j) can be selected to form a new matrix key_orig (I, j), and a hash function operation is utilized to generate a feature vector V (I, j) of the 16-bit original medical image I (I, j); alternatively, 4*8 modules in the first coefficient matrix F0 (I, j) may be selected to form a new matrix key_orig (I, j), and a hash function operation is used to generate the feature vector V (I, j) of the 32-bit original medical image I (I, j).
The size of the module in the first coefficient matrix F0 (i, j) may be selected according to the number of bits of the feature vector V (i, j). According to human visual characteristics (HVS), the low-intermediate frequency signal has a large visual impact on humans, representing the main features of medical images. Therefore, the feature vector V (i, j) of the medical image selected by the present invention is a sign of low intermediate frequency coefficient, and the number of low intermediate frequency coefficients is selected in relation to the size of the original medical image subjected to the full-image LBP-DCT conversion and the correlation between the medical images, and the smaller the L value, the larger the correlation. In the latter test, the length of L is preferably chosen to be 32.
It should be noted that the main reasons for the poor resistance to geometric attacks of most medical image watermarking algorithms at present are: the digital watermark is embedded in the pixel or transformation coefficient, and the slight geometric transformation of the medical image often causes a large change in the pixel value or transformation coefficient value, 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 small geometric transformation, the visual characteristic value of the image is not obviously suddenly changed, and the watermark image can be compared through the comparison of the visual characteristic vector, so that watermark information authentication is completed. Through experimental data discovery, a feature vector resisting geometric attack can be found by combining the local binary pattern of the medical image with discrete cosine transform. When a medical image is subjected to conventional geometric transformations, the magnitude of the DCT low-IF coefficient values may vary somewhat, but their coefficient signs remain substantially unchanged.
According to the rules, the invention firstly carries out local binary pattern transformation on the medical image, and then carries out global DCT transformation on the response matrix. The experimental data after selecting some conventional attacks and geometric attacks are shown in the table I. The medical images used for the test in table one (128 x 128), see fig. 1. Column 1 of table one shows the type of medical image that is attacked. Columns 4 to 7 are F (1, 1) -F (1, 4) taken in the LBP-DCT coefficient matrix, for a total of 4 low intermediate frequency coefficients. Wherein the real part of the coefficient F (1, 1) represents the dc component value of the medical image. For conventional attacks, the sign of these low intermediate frequency coefficient values remains substantially unchanged, approximately equal to the medical image values; for geometric attacks, part of coefficients are changed greatly, but the invention can find that when the medical image is subjected to geometric attacks, the magnitudes of the low intermediate frequency coefficients of the part of LBP-DCT are changed but the signs of the low intermediate frequency coefficients are not changed basically. The invention uses positive LBP-DCT coefficient as 1 (coefficient with zero value), and negative coefficient as 0, then for medical image, F (1, 1) -F (1, 4) coefficient in LBP-DCT coefficient matrix, the corresponding coefficient sign sequence is: "101110110", see column 8 of Table 1, which shows that the symbol sequence and the original medical image can remain similar regardless of conventional or geometric attacks, the normalized correlation coefficient with the original medical image is large (see column 9), and 4 DCT coefficient symbols are conveniently taken here.
Table-image full-image LBP-DCT conversion low intermediate frequency part coefficient and change value after different attacks
The LBP-DCT coefficient unit is 1.0e+003, and the correlation coefficient takes the 32bit comparison result.
In a specific implementation, in the above-mentioned robust watermarking method for medical image based on LBP-DCT provided by the embodiment of the present invention, step S103 uses Logistic Map to perform chaotic scrambling on an original watermark W (i, j) to obtain chaotic scrambled watermark BW (i, j), which may specifically include: firstly, generating a chaotic sequence X (j) through a Logistic Map according to an initial value X 0; wherein the initial value of the chaos coefficient is set to 0.2, the growth parameter is 4, and the iteration number is 32; sorting the values in the chaotic sequence X (j) according to the order from small to large; and scrambling the pixel position space in the original watermark W (i, j) according to the position change before and after each value in the chaotic sequence X (j) to obtain the chaotic scrambling watermark BW (i, j).
In a specific implementation, in the above-mentioned robust watermarking method for medical image based on LBP-DCT provided by the embodiment of the present invention, step S103 correlates the feature vector of the original medical image I (I, j) with the chaotic scrambling watermark to obtain a logical key, which may specifically include: performing exclusive OR operation on the feature vector V (I, j) of the original medical image I (I, j) and the chaotic scrambling watermark BW (I, j) bit by bit to embed the original watermark W (I, j) into the original medical image I (I, j) and simultaneously obtaining a logic Key Key (I, j):
After performing step S103 to obtain the logical key set, it may further include: the logic Key (i, j) is used as a Key to apply to a third party and is stored in the third party, so that ownership and use right of the original medical image can be obtained later, the purpose of protecting the medical image is achieved, and the concept of the third party is utilized, so that the method is suitable for the practical application and standardization of the current network technology.
Specifically, in the embodiment of the invention, firstly, local binary pattern LBP transformation is carried out on a medical image, then discrete cosine DCT transformation is carried out on an image LBP response matrix, a texture image visual feature vector resisting geometric attack is extracted from DCT coefficients, and a watermarking technology is organically combined with chaotic encryption, a Hash function and a third party concept, so that geometric attack resistance and conventional attack resistance of digital watermarking can be realized.
Similarly, in a specific implementation, in the above-mentioned robust watermarking method for medical image based on LBP-DCT provided by the embodiment of the present invention, step S104 performs local binary pattern LBP transformation on the medical image I' (I, j) to be detected after network transmission, and the obtaining of the second LBP response matrix may specifically include: firstly, dividing a medical image I' (I, j) to be detected into areas cells of 16 x 16; in each cell, eight pixels adjacent to the central pixel in the neighborhood of the pixel 3*3 are used as sampling points, the gray value of the central pixel is used as a threshold value, and after the gray value of the sampling points is compared with the threshold value, eight binary numbers are generated to be used as LBP values of the central pixel; specifically, comparing a pixel in each cell with gray values of eight adjacent pixels around, if the gray value of the adjacent pixels around is greater than that of the central pixel, marking the position of the adjacent pixels around as 1, otherwise, marking the position of the adjacent pixels around as 0; then, calculating a histogram of each region according to LBP values correspondingly generated by the medical image I' (I, j) to be detected, and carrying out normalization processing on the histogram to obtain a statistical histogram of each region; and finally, connecting the obtained statistical histograms of each region to obtain LBP texture feature vectors of the medical image I' (I, j) to be detected as a second LBP response matrix.
In a specific implementation, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, step S105 performs discrete cosine DCT on the second LBP response matrix, and extracts a feature vector of a medical image I' (I, j) to be detected, which may specifically include: firstly, performing discrete cosine DCT (discrete cosine transform) on a second LBP response matrix to obtain a second coefficient matrix F1 (I, j), wherein F1 (I, j) =dct 2 (I_LBP1); then, selecting a module with a set size in the second coefficient matrix F1 (i, j) to form a new matrix; and generating a characteristic vector V '(I, j) of the medical image I' (I, j) to be detected by utilizing Hash function operation. For example, a new matrix may be formed by selecting 2×4 modules in the second coefficient matrix F1 (I, j), and generating the feature vector V '(I, j) of the 8-bit medical image I' (I, j) to be detected by using hash function operation; or a new matrix can be formed by selecting 4*4 modules in the second coefficient matrix F1 (I, j), and a characteristic vector V '(I, j) of the 16-bit medical image I' (I, j) to be detected is generated by utilizing Hash function operation; or a 4*8 module in the second coefficient matrix F1 (I, j) can be selected to form a new matrix key_orig (I, j), and a hash function operation is utilized to generate a feature vector V '(I, j) of the 32-bit medical image I' (I, j) to be detected.
In a specific implementation, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, step S106 correlates a feature vector V '(I, j) of a medical image I' (I, j) to be detected with a logical Key (I, j) to extract an encrypted watermark, which may specifically include: performing exclusive or operation on the feature vector V ' (I, j) of the medical image I ' (I, j) to be detected and the logic Key Key (I, j) to extract an encrypted watermark BW ' (I, j):
the algorithm only needs a Key Key (i, j) when extracting the watermark, does not need the participation of an original medical image, and is a zero watermark extraction algorithm.
In a specific implementation, in the above-mentioned medical image robust watermarking method based on LBP-DCT provided by the embodiment of the present invention, step S106 decrypts the encrypted watermark BW '(i, j) to obtain a restored watermark W' (i, j), which may specifically include: generating the same chaotic sequence X (j) through a Logistic Map by using the same method as watermark encryption; sorting the values in the chaotic sequence X (j) according to the order from small to large; and (3) performing a restoration operation on the pixel position space in the encrypted watermark BW '(i, j) according to the position change before and after each value in the chaotic sequence X (j) to obtain a restored watermark W' (i, j).
Next, by calculating the correlation coefficients NC of W (i, j) and W' (i, j), ownership of the medical image and embedded watermark information can be determined, measuring the robustness of the algorithm.
The invention is further described below with reference to the accompanying drawings: as shown in fig. 2, the subject of the experimental test is a 128 x 128 head medical image, denoted by I (I, j), where 1.ltoreq.i, j.ltoreq.128. Selecting a meaningful binary image as an original watermark, and marking as: w= { W (i, j) |w (i, j) =0, 1; 1.ltoreq.i.ltoreq.M 1,1≤j≤M2. As shown in FIG. 3, the watermark here has a size of 32 x 32.
Firstly, processing an original medical image I (I, j) by using local binary pattern transformation to obtain an LBP response matrix, performing DCT (discrete cosine transform) on the response matrix, and taking 32 coefficients, namely a 4*8 module, in consideration of robustness and capacity of embedding the watermark at one time. The initial value of the chaos coefficient is set to 0.2, the increment parameter is 4, and the iteration number is 32. Then, logistic chaotic encryption is carried out on the original watermark W (i, j), and the encrypted chaotic scrambling watermark EW (i, j) is shown in fig. 4. After W' (i, j) is detected by the watermark algorithm, whether watermark embedding exists or not is judged by calculating a normalized correlation coefficient NC, and when the numerical value of the watermark embedding is closer to 1, the similarity is higher, so that the robustness of the algorithm is judged. 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, the watermark can be extracted accurately.
The conventional attack resistance and the geometric attack resistance of the digital watermarking method are judged through specific examples.
First, adding gaussian noise: gaussian noise is added to the watermark using imnoise () function.
And the second table is experimental data of watermark anti-Gaussian noise interference. As can be seen from table two, when the gaussian noise intensity is as high as 11%, the PSNR of the image after attack is reduced to 2.95dB, and the watermark extracted at this time has a correlation coefficient nc=0.68, the watermark can still be accurately extracted, and the overall data is larger than 0.6 and is close to 1. This illustrates that gaussian noise can be resisted with the present invention. FIG. 6 shows a medical image at a Gaussian noise intensity of 3%, which is visually significantly different from the original medical image of the head; fig. 7 shows the watermark extracted at a gaussian noise intensity of 3%, nc=0.82.
Anti-Gaussian noise interference data of surface two watermarks
| Noise intensity (%) | 1 | 3 | 5 | 7 | 9 | 11 |
| PSNR(dB) | 20.46 | 16.21 | 14.32 | 11.86 | 10.59 | 9.77 |
| NC | 0.85 | 0.82 | 0.87 | 0.70 | 0.71 | 0.69 |
Second, median filtering
Table three is the median filtering resistance of the watermark of the medical image, and as seen from table three, when the median filtering parameter is [3x3], and the number of filtering repetitions is 5, the existence of the watermark can still be measured, nc=0.91. FIG. 8 shows a medical image with median filter parameters [3x3], filter repetition number 10, the image having been blurred; fig. 9 shows the watermark extracted at a median filter parameter of [3x3] and a filter repetition number of 10, nc=0.81, and the watermark can be extracted. FIG. 10 shows a medical image with a median filter parameter of [5x5], a filter repetition number of 10; fig. 11 shows the watermark extracted at a median filter parameter of [5x5] and a filter repetition number of 10, nc=0.60, and the watermark can be extracted.
Table three watermark median filtering resisting experimental data
Third, rotation transformation
Table four is watermark anti-rotation attack experimental data. It can be seen from table four that nc=0.71 can still extract the watermark when the image is rotated by 20 ° in time. Fig. 12 shows a medical image rotated 10 ° in time; fig. 13 shows that watermark extracted by rotating 10 ° in time, nc=0.71, can be extracted accurately. Fig. 14 shows a medical image rotated 3 ° in time; fig. 15 shows that watermark extracted by rotating 3 ° in time, nc=0.82, can be extracted accurately.
Anti-rotation attack experimental data of four-watermark table
| Degree of rotation ° | 1 | 3 | 5 | 10 | 15 | 20 |
| PSNR(dB) | 25.01 | 18.93 | 16.19 | 13.49 | 12.70 | 12.38 |
| NC | 0.82 | 082 | 0.82 | 071 | 0.71 | 0.71 |
Fourth, scaling transform
Table five is experimental data of watermark anti-scaling attack of medical image, and it can be seen from table five that when the scaling factor is as small as 0.5, the correlation coefficient nc=0.78, and the watermark can be extracted. FIG. 16 shows a scaled medical image (scale factor of 0.8); fig. 17 shows the watermark extracted after a scaling attack, nc=0.91, and the watermark can be extracted accurately.
Table five watermark anti-scaling attack experimental data
| Scaling factor | 0.5 | 0.6 | 0.7 | 0.8 | 1.0 | 1.5 | 1.8 | 2.0 |
| NC | 0.78 | 0.78 | 0.78 | 0.91 | 1.00 | 1.00 | 0.96 | 0.96 |
Fifth, translational transformation
Table six is watermark anti-translation transformation experimental data. When the image data moves vertically by 20% from the sixth table, NC values are higher than 0.61, so that the watermark can be extracted accurately, and the watermark method has a strong anti-translation transformation capability. FIG. 18 shows the image after the medical image has been shifted down vertically by 10%; fig. 19 shows the watermark extracted after being shifted down vertically by 10%, and the watermark can be extracted accurately, nc=0.81. FIG. 20 shows the image after the medical image has been shifted down vertically by 20%; fig. 21 shows the watermark extracted after 20% vertical shift, and the watermark can be accurately extracted, nc=0.61.
Table six watermark anti-translation transformation experimental data
| Downshifting distance (%) | 2 | 4 | 6 | 8 | 10 | 20 |
| PSNR(dB) | 15.33 | 12.59 | 12.33 | 11.96 | 11.69 | 10.20 |
| NC | 1.00 | 0.81 | 0.81 | 0.81 | 0.81 | 0.61 |
Sixth, shearing attack
The table seven is watermark anti-shearing attack experimental data, and it can be seen from the table seven that when the medical image is sheared along the coordinate axis Y, the NC value is greater than 0.7 when the shearing amount is 25%, and the watermark can still be extracted, so that the watermark algorithm has stronger anti-shearing attack capability. FIG. 22 shows the medical image after 25% of the image is cropped along the Y-axis; fig. 23 shows that watermark extraction after 25% image cropping along the Y-axis can accurately extract watermark, nc=0.78. FIG. 24 shows the medical image after 3% of the image is cropped along the Y-axis; fig. 25 shows that watermark extraction after cutting 3% of the image along the Y axis can accurately extract watermark, nc=1.00.
Seven-watermark shearing attack resistant experimental data
| Y-direction shear (%) | 3 | 6 | 9 | 15 | 20 | 25 |
| NC | 1.00 | 1.00 | 1.00 | 1.00 | 0.91 | 0.78 |
From the above description, the invention is based on the medical image digital watermarking technology with perceptual hash and data enhancement, enhances the feature set of the extracted feature sequence, has better robustness, and can accurately extract the watermark against conventional attacks such as Gaussian noise interference, median filtering processing and geometric attacks such as rotation transformation, scaling transformation, translation transformation and shearing attack, and has stronger capability of resisting the conventional attacks and the geometric attacks.
The medical image robust watermarking method based on LBP-DCT provided by the embodiment of the invention comprises the following steps: performing Local Binary Pattern (LBP) transformation on an original medical image to obtain a first LBP response matrix; performing discrete cosine DCT (discrete cosine transform) on the first LBP response matrix, and extracting feature vectors of the original medical image; chaotic scrambling is carried out on the original watermark by using the Logistic Map to obtain chaotic scrambling watermark, and a logical key is obtained by associating the feature vector of the original medical image with the chaotic scrambling watermark; carrying out local binary pattern LBP conversion on the medical image to be detected after network transmission to obtain a second LBP response matrix; performing discrete cosine DCT (discrete cosine transform) on the second LBP response matrix, and extracting feature vectors of the medical image to be detected; the feature vector of the medical image to be detected is correlated with the logic key to extract the encrypted watermark, and the encrypted watermark is decrypted to obtain the restored watermark; and carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the original medical image and embedded watermark information. According to the medical image robust watermarking method, based on the five major parts of feature vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption of the LBP-DCT, the local binary pattern LBP and discrete cosine DCT transformation of the medical image are combined, the advantages of LBP rotation invariance and the characteristics of strong conventional attack resistance, ergodic property, robustness and the like of the DCT are considered, so that a feature vector resistant to geometric attacks can be found, the geometric attacks such as rotation, translation and shearing are particularly prominent, the content of original encrypted volume data is not changed by watermark embedding, the defect caused by the traditional watermark embedding technology on original image data modification is well overcome, the quality of the medical image is guaranteed, the robustness and the invisibility are high, and the privacy information of a patient and the data safety of the medical image can be simultaneously protected.
Finally, it is further noted that relational terms such as first and second, and the like are 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. Moreover, 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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The invention has been described in detail with reference to the LBP-DCT-based medical image robust watermarking method provided by the present invention, and specific examples are applied herein to illustrate the principles and embodiments of the present invention, and the above examples are only for aiding in understanding the method of the present invention and its core ideas; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (8)
1. A medical image robust watermarking method based on LBP-DCT, comprising:
Performing Local Binary Pattern (LBP) transformation on an original medical image to obtain a first LBP response matrix, wherein the method specifically comprises the following steps of: dividing the original medical image into 16 x 16 regions; in each region, eight pixels adjacent to a central pixel in the neighborhood of a pixel 3*3 are used as sampling points, the gray value of the central pixel is used as a threshold value, and after the gray value of the sampling points is compared with the threshold value, an eight-bit binary number is generated as an LBP value of the central pixel; calculating a histogram of each region according to the LBP value correspondingly generated by the original medical image, and carrying out normalization processing on the histogram to obtain a statistical histogram of each region; connecting the obtained statistical histograms of each region to obtain an LBP texture feature vector of the original medical image as a first LBP response matrix;
Performing discrete cosine DCT (discrete cosine transform) on the first LBP response matrix, and extracting feature vectors of the original medical image;
chaotic scrambling is carried out on the original watermark by using a Logistic Map to obtain chaotic scrambling watermark, and a logic key is obtained by associating the feature vector of the original medical image with the chaotic scrambling watermark;
Carrying out local binary pattern LBP conversion on the medical image to be detected after network transmission to obtain a second LBP response matrix, wherein the method specifically comprises the following steps: dividing the medical image to be detected into 16 x 16 areas; in each region, eight pixels adjacent to a central pixel in the neighborhood of a pixel 3*3 are used as sampling points, the gray value of the central pixel is used as a threshold value, and after the gray value of the sampling points is compared with the threshold value, an eight-bit binary number is generated as an LBP value of the central pixel; calculating a histogram of each region according to the LBP value correspondingly generated by the medical image to be detected, and carrying out normalization processing on the histogram to obtain a statistical histogram of each region; connecting the obtained statistical histograms of each region to obtain an LBP texture feature vector of the medical image to be detected as a second LBP response matrix;
Performing discrete cosine DCT (discrete cosine transform) on the second LBP response matrix, and extracting feature vectors of the medical image to be detected;
Extracting an encrypted watermark by associating the feature vector of the medical image to be detected with the logic key, and decrypting the encrypted watermark to obtain a restored watermark;
And carrying out normalized correlation coefficient calculation on the original watermark and the restored watermark, and determining ownership of the original medical image and embedded watermark information.
2. The LBP-DCT-based medical image robust watermarking method according to claim 1, characterized in that performing a discrete cosine DCT transformation on the first LBP response matrix, extracting feature vectors of the original medical image, specifically comprising:
Performing discrete cosine DCT (discrete cosine transform) on the first LBP response matrix to obtain a first coefficient matrix;
Selecting a module with a set size from the first coefficient matrix to form a new matrix;
and generating the feature vector of the original medical image by utilizing Hash function operation.
3. The LBP-DCT-based medical image robust watermarking method according to claim 2, wherein the chaotic scrambling is performed on the original watermark by using a Logistic Map to obtain a chaotic scrambling watermark, and specifically comprising:
generating a chaotic sequence through a Logistic Map;
Sorting the values in the chaotic sequence according to the order from small to large;
And scrambling the pixel position space in the original watermark according to the position change before and after each value in the chaotic sequence is sequenced, so as to obtain the chaotic scrambling watermark.
4. A medical image robust watermarking method based on LBP-DCT according to claim 3 characterized in that the correlation of the feature vector of the original medical image with the chaotic scrambling watermark yields a logical key, comprising in particular:
Performing exclusive OR operation on the feature vector of the original medical image and the chaotic scrambling watermark bit by bit so as to embed the original watermark into the original medical image and obtain a logic key.
5. The LBP-DCT-based medical image robust watermarking method according to claim 4, further comprising, after deriving the logical key set:
And applying the logic key as a key to a third party and storing the logic key in the third party.
6. The LBP-DCT-based medical image robust watermarking method according to claim 5, characterized by performing discrete cosine DCT on the second LBP response matrix, extracting feature vectors of the medical image to be detected, specifically comprising:
Performing discrete cosine DCT (discrete cosine transform) on the second LBP response matrix to obtain a second coefficient matrix;
selecting a module with a set size in the second coefficient matrix to form a new matrix;
and generating the feature vector of the medical image to be detected by utilizing Hash function operation.
7. The LBP-DCT-based medical image robust watermarking method according to claim 6, characterized in that extracting an encrypted watermark by associating the feature vector of the medical image to be detected with the logical key, in particular comprises:
and performing exclusive OR operation on the feature vector of the medical image to be detected and the logic key to extract the encrypted watermark.
8. The LBP-DCT-based medical image robust watermarking method according to claim 7, wherein decrypting the encrypted watermark to obtain a restored watermark, specifically comprises:
generating the chaotic sequence through a Logistic Map;
Sorting the values in the chaotic sequence according to the order from small to large;
And performing restoration operation on the pixel position space in the encrypted watermark according to the position change before and after each value in the chaotic sequence is sequenced, so as to obtain a restored watermark.
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