CN113160030A - LBP-DCT (local binary pattern-discrete cosine transformation) -based medical image robust watermarking method - Google Patents
LBP-DCT (local binary pattern-discrete cosine transformation) -based medical image robust watermarking method Download PDFInfo
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Abstract
The application discloses a medical image robust watermarking method based on LBP-DCT, which comprises the following steps: carrying out LBP-DCT (local binary pattern-discrete cosine transformation) on the original medical image, and extracting a characteristic vector of the original medical image; performing chaotic scrambling on the original watermark by using a Logistic Map to obtain a chaotic scrambled watermark, and associating the eigenvector with the chaotic scrambled watermark to obtain a logic key; carrying out LBP-DCT transformation on the medical image to be detected, and extracting a characteristic vector of the medical image to be detected; associating the feature vector with a logic key to extract an encrypted watermark 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 to determine ownership of the original medical image and embedded watermark information. Therefore, the advantages of LBP rotation invariance and the characteristics of high conventional attack resistance of DCT and the like are considered, the characteristic vector of resisting geometric attack is obtained, the content of the original encrypted volume data is not changed by embedding the watermark, and the quality of the medical image 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 shifting from traditional medicine to telemedicine, which enables large amounts of medical images to be transmitted and shared in a network; in order to solve the problems that the medical image can be tampered in the transmission sharing process and stolen, the original medical image needs to be processed.
The digital watermarking technology is originally used for copyright protection of digital media, and the characteristics of invisibility, robustness and the like of the digital watermarking are utilized to hide personal information of a patient in a medical image of the patient so as to ensure the safe transmission of the personal information on the Internet. Therefore, in the case where digital images are widely used in network transmission, research on digital watermarking algorithms for medical images becomes extremely important.
However, at present, the research on the digital watermarking algorithm of the medical image is less, most of the existing digital watermarking algorithms cannot effectively protect the medical image, and the geometric attack resistance is poor. However, the problem of transmission of a large amount of medical data to be faced in the future is significant in researching how to embed the digital robust watermark in the medical data, and for the medical data, the content of the medical data is generally not allowed to be modified, which in turn raises difficulty for embedding the watermark in the medical data.
Disclosure of Invention
In view of this, the present invention provides a robust watermarking method for medical images based on LBP-DCT, which can ensure the quality of medical images, and especially has strong robustness and invisibility in the aspect of geometric attack. The specific scheme is as follows:
a medical image robust watermarking method based on LBP-DCT comprises the following steps:
performing Local Binary Pattern (LBP) transformation on an original medical image to acquire a first LBP response matrix;
performing discrete cosine DCT (discrete cosine transform) on the first LBP response matrix, and extracting a characteristic vector of the original medical image;
performing chaotic scrambling on an original watermark by using a Logistic Map to obtain a chaotic scrambled watermark, and associating a feature vector of the original medical image with the chaotic scrambled watermark to obtain a logic key;
local Binary Pattern (LBP) transformation is carried out on the medical image to be detected after network transmission, and a second LBP response matrix is obtained;
performing Discrete Cosine Transform (DCT) on the second LBP response matrix, and extracting a characteristic vector of the medical image to be detected;
associating the feature vector of the medical image to be detected with the logic key to extract an encrypted watermark, 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 to determine ownership of the original medical image and embedded watermark information.
Preferably, in the LBP-DCT-based medical image robust watermarking method provided by the embodiment of the present invention, the local binary pattern LBP transformation is performed on the original medical image to obtain the first LBP response matrix, which specifically includes:
dividing the original medical image into 16 x 16 regions;
in each area, taking eight pixels adjacent to a central pixel in a pixel 3x3 neighborhood as sampling points, taking the gray value of the central pixel as a threshold, and generating eight-bit binary number as an LBP value of the central pixel after comparing the gray value of the sampling points with the threshold;
calculating a histogram of each region according to an LBP value correspondingly generated by the original medical image, and performing normalization processing on the histogram to obtain a statistical histogram of each region;
and connecting the obtained statistical histograms of all the regions, and acquiring an LBP texture characteristic vector of the original medical image as a first LBP response matrix.
Preferably, in the LBP-DCT-based medical image robust watermarking method provided by the embodiment of the present invention, performing discrete cosine DCT transform on the first LBP response matrix, and extracting a feature vector of the original medical image specifically includes:
performing Discrete Cosine Transform (DCT) on the first LBP response matrix to obtain a first coefficient matrix;
selecting a module with a set size in the first coefficient matrix to form a new matrix;
and generating a feature vector of the original medical image by utilizing a hash function operation.
Preferably, in the LBP-DCT-based medical image robust watermarking method provided by the embodiment of the present invention, the original watermark is chaotically scrambled using a Logistic Map to obtain a chaos scrambling watermark, which specifically includes:
generating a chaotic sequence through a Logistic Map;
sequencing all values in the chaotic sequence from small to large;
and scrambling the pixel position space in the original watermark according to the position change before and after the ordering of each value in the chaotic sequence to obtain the chaotic scrambled watermark.
Preferably, in the LBP-DCT-based medical image robust watermarking method provided by the embodiment of the present invention, associating the feature vector of the original medical image with the chaotic scrambling watermark to obtain a logical key specifically includes:
and carrying out 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 at the same time.
Preferably, in the method for robust watermarking of medical images based on LBP-DCT provided in 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 LBP-DCT-based medical image robust watermarking method provided in the embodiment of the present invention, the local binary pattern LBP transformation is performed on the medical image to be detected after the network transmission to obtain the second LBP response matrix, which specifically includes:
dividing the medical image to be detected into 16-by-16 areas;
in each area, taking eight pixels adjacent to a central pixel in a pixel 3x3 neighborhood as sampling points, taking the gray value of the central pixel as a threshold, and generating eight-bit binary number as an LBP value of the central pixel after comparing the gray value of the sampling points with the threshold;
calculating a histogram of each region according to the LBP value correspondingly generated by the medical image to be detected, and performing normalization processing on the histogram to obtain a statistical histogram of each region;
and connecting the obtained statistical histograms of the regions, and acquiring an LBP texture characteristic vector of the medical image to be detected as a second LBP response matrix.
Preferably, in the LBP-DCT-based medical image robust watermarking method provided in the embodiment of the present invention, performing discrete cosine DCT transform on the second LBP response matrix to extract the 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 characteristic vector of the medical image to be detected by utilizing hash function operation.
Preferably, in the LBP-DCT-based medical image robust watermarking method provided in the embodiment of the present invention, associating the feature vector of the medical image to be detected with the logic key to extract the encrypted watermark includes:
and carrying out XOR operation on the feature vector of the medical image to be detected and the logic key to extract an encrypted watermark.
Preferably, in the method for robust watermarking of medical images based on LBP-DCT provided in the embodiment of the present invention, the decrypting the encrypted watermark to obtain the restored watermark includes:
generating the chaotic sequence through a Logistic Map;
sequencing all values in the chaotic sequence from small to large;
and restoring the pixel position space in the encrypted watermark according to the position change before and after the sequencing of each value in the chaotic sequence to obtain the restored watermark.
According to the technical scheme, the robust watermarking method for the medical image based on the LBP-DCT, provided by the invention, comprises the following steps: performing Local Binary Pattern (LBP) transformation on an original medical image to acquire a first LBP response matrix; performing Discrete Cosine Transform (DCT) on the first LBP response matrix, and extracting a characteristic vector of an original medical image; performing chaotic scrambling on the original watermark by using a Logistic Map to obtain a chaotic scrambled watermark, and associating a feature vector of the original medical image with the chaotic scrambled watermark to obtain a logic key; local Binary Pattern (LBP) transformation is carried out on the medical image to be detected after network transmission, and a second LBP response matrix is obtained; performing Discrete Cosine Transform (DCT) on the second LBP response matrix, and extracting a characteristic vector of the medical image to be detected; associating the feature vector of the medical image to be detected with the logic key to extract an encrypted watermark, 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 to determine ownership of the original medical image and embedded watermark information.
The robust watermarking method for the medical image, provided by the invention, is based on five parts of characteristic vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption of LBP-DCT, combines the local binary pattern LBP of the medical image with discrete cosine DCT, and has the advantages of LBP rotation invariance and the characteristics of the DCT such as strong conventional attack resistance, ergodicity, robustness and the like, so that a characteristic vector resisting geometric attack can be found, the method is particularly prominent in terms of geometric attacks such as rotation, translation, shearing and the like, the content of original encryption body data is not changed by embedding the watermark, the defects caused by original image data modification by the traditional watermark embedding technology are well solved, the quality of the medical image is ensured, the robustness and invisibility are strong, and the privacy information of a patient and the data security of the medical image can be simultaneously protected.
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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 medical images based on LBP-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 chaos scrambled watermark image according to 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 3% according to an embodiment of the present invention;
fig. 7 shows a watermark extracted when the gaussian noise interference level is 3% according to an embodiment of the present invention;
fig. 8 is a median filtered medical image with a window size of [3x3] filtered 10 times according to an embodiment of the present invention;
fig. 9 shows a watermark extracted after median filtering with a window size of [3x3] for 10 filtering times according to an embodiment of the present invention;
fig. 10 is a median filtered medical image with a window size of [5x5] filtered 10 times according to an embodiment of the present invention;
fig. 11 shows a watermark extracted after median filtering with a window size of [5x5] for 10 filtering times according to an embodiment of the present invention;
FIG. 12 is a medical image rotated 10 clockwise according to an embodiment of the present invention;
fig. 13 shows a watermark extracted when the watermark is rotated clockwise by 10 ° according to an embodiment of the present invention;
FIG. 14 is a medical image rotated 3 clockwise according to an embodiment of the present invention;
fig. 15 shows a watermark extracted when the watermark is rotated clockwise by 3 ° according to an embodiment of the present invention;
FIG. 16 is a medical image scaled by a factor of 0.8 provided by an embodiment of the present invention;
fig. 17 shows the watermark extracted when the scaling is 0.8 times, according to an embodiment of the present invention;
FIG. 18 is a medical image shifted vertically by 10% according to an embodiment of the present invention;
FIG. 19 shows an embodiment of the present invention providing a watermark extracted when shifted vertically down by 10%;
FIG. 20 is a medical image shifted vertically by 20% according to an embodiment of the present invention;
FIG. 21 shows an embodiment of the present invention providing a watermark extracted when the vertical shift is 20%;
FIG. 22 is a medical image cut 25% along the Y-axis provided by an embodiment of the present invention;
fig. 23 shows an extracted watermark cut by 25% along the Y-axis according to an embodiment of the present invention;
FIG. 24 is a medical image cut at 3% along the Y-axis provided by an embodiment of the present invention;
fig. 25 shows an extracted watermark cut by 3% along the Y-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 LBP-DCT, which comprises the following steps as shown in figure 1:
s101, Local Binary Pattern (LBP) transformation is carried out 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, and is denoted as I (I, j), where I (I, j) represents the pixel grayscale value of the original medical image. The size of the original medical image may be determined according to actual conditions, and is not limited herein.
Carrying out LBP transformation on an 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 scale invariance, and can be used for texture feature extraction.
S102, performing Discrete Cosine Transform (DCT) on the first LBP response matrix, and extracting a characteristic vector of the original medical image;
s103, performing chaotic scrambling on the original watermark by using a Logistic Map to obtain a chaotic scrambling watermark, and associating the feature vector of the original medical image with the chaotic scrambling watermark to obtain a logic key;
in practical application, an original watermark embedded in a medical image is a meaningful binary text 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 M1,1≤j≤M2}，M1And M2Respectively, the size length and width of the original watermark image, W (i, j) represents the pixel gray scale 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 the secure transmission on a network is realized.
S104, Local Binary Pattern (LBP) transformation is carried out on the medical image to be detected after network transmission, and a second LBP response matrix is obtained;
it can be understood that the medical image to be measured here can be regarded as a medical image formed after the original medical image may be subjected to geometric attacks such as gaussian noise interference, median filtering, compression, rotation, translation and the like or conventional attacks during the network transmission process.
S105, performing Discrete Cosine Transform (DCT) on the second LBP response matrix, and extracting a feature vector of the medical image to be detected;
s106, associating the feature vector of the medical image to be detected with the logic key to extract the encrypted watermark, and decrypting the encrypted watermark to obtain a restored watermark;
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 above robust watermarking method for medical images based on LBP-DCT provided by the embodiment of the present invention, based on five parts of characteristic vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption of 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 DCT anti-conventional attack capability, ergodicity, robustness and the like are considered, therefore, a characteristic vector resisting geometric attacks can be found, the method is particularly prominent in terms of geometric attacks such as rotation, translation, shearing and the like, the content of original encrypted body data is not changed by embedding the watermark, the defect caused by the modification of original image data by the traditional watermark embedding technology is well overcome, the quality of medical images is ensured, the robustness and invisibility are high, and the privacy information of patients and the data security of the medical images can be simultaneously protected.
It should be noted that the original LBP operator is defined in the neighborhood of pixel 3 × 3, and compares the neighboring central pixel as a threshold with the gray values of the adjacent 8 pixels, so as to generate 8-bit binary numbers, and the 8-bit binary numbers are sequentially arranged into a binary number, where the binary number is the LBP value of the central pixel. For an LBP operator with a circular region of radius R containing P sample points, P2 patterns will result. It is clear that the variety of binary patterns increases dramatically as the number of samples in the neighborhood set increases. So many binary patterns are disadvantageous for texture extraction, recognition and expression. Therefore, it is necessary to perform dimension reduction on the original LBP pattern so as to represent the information of the image best in the case of reducing the data amount.
In order to solve the problem of excessive binary patterns and improve the statistics, the invention adopts an equivalent Pattern (Uniform Pattern) to reduce the dimension of the Pattern type of the LBP operator. The "equivalent mode" is defined as: when a cyclic binary number corresponding to an LBP has at most two transitions from 0 to 1 or from 1 to 0, the binary number corresponding to the LBP is called an equivalent pattern class. For example, 00000000(0 hops), 00000111 (only one hop from 0 to 1), 10001111 (two hops from 1 to 0, then from 0 to 1) are all equivalent pattern classes. Modes other than the equivalent mode class fall into another class, called mixed mode class, e.g., 10010111 (four total hops). With such an improvement, the variety of binary patterns is greatly reduced without losing any information. The number of the patterns is reduced from the original P2 to P (P-1) +2, wherein P represents the number of sampling points in the neighborhood set. For 8 samples in 3 × 3 neighborhood, the binary pattern is reduced from the original 256 to 58, which makes the feature vector have fewer dimensions and reduces the effect of high frequency noise.
Further, in specific implementation, in the method for robust watermarking of medical image based on LBP-DCT provided in the embodiment of the present invention, step S101 performs local binary pattern LBP transformation on the original medical image I (I, j) to obtain a first LBP response matrix, which may specifically include: first, the original medical image I (I, j) is divided into 16 × 16 regions (cells); in each cell, taking eight pixels adjacent to the central pixel in the neighborhood of pixel 3x3 as sampling points, taking the gray value of the central pixel as a threshold, and generating eight-bit binary numbers as an LBP value of the central pixel after comparing the gray value of the sampling points with the threshold; specifically, comparing the gray values of one pixel in each cell with the gray values of eight adjacent pixels around the cell, and if the gray values of the adjacent pixels around the cell are greater than the gray value of the central pixel, marking the position of the adjacent pixels around the cell as 1, otherwise, marking the position of the adjacent pixels around the cell as 0; then, according to the LBP value correspondingly generated by the original medical image I (I, j), calculating the histogram of each cell, namely the frequency of each figure (assumed to be a decimal LBP value), and carrying out normalization processing on the histogram to obtain a statistical histogram of each area; and finally, connecting the obtained statistical histograms of each cell (namely connecting the statistical histograms into a feature vector), and obtaining an LBP texture feature vector of the original medical image I (I, j) as a first LBP response matrix.
In specific implementation, in the LBP-DCT-based medical image robust watermarking method provided in the embodiment of the present invention, step S102 performs discrete cosine DCT on the first LBP response matrix, and extracts a feature vector of the original medical image I (I, j), which may specifically include: firstly, performing discrete cosine DCT on the first LBP response matrix to obtain a first coefficient matrix F0(I, j), where F0(I, j) is DCT2(I _ LBP 0); 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); and generating a feature vector V (I, j) of the original medical image I (I, j) by utilizing a hash function operation. For example, 2 × 4 blocks 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 a feature vector V (I, j) of the 8 original medical images I (I, j); or, 4 × 4 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 a feature vector V (I, j) of the 16-bit original medical image I (I, j); alternatively, 4 × 8 blocks 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 blocks in the first coefficient matrix F0(i, j) may be selected according to the number of bits of the eigenvector V (i, j). According to the human visual characteristics (HVS), low and medium frequency signals have a large effect on human vision and represent a major feature of medical images. Therefore, the feature vector V (i, j) of the medical image selected by the invention is the sign of the low-intermediate frequency coefficient, the number of the low-intermediate frequency coefficient is selected to be related to the size of the original medical image subjected to the full-map LBP-DCT conversion and the correlation between the medical images, and the correlation is increased when the L value is smaller. In the later experiments, the length of L is preferably selected to be 32.
It should be noted that the main reasons for the poor capability of most of the current medical image watermarking algorithms against geometric attacks are: the slight geometric transformation of medical images often results in large changes in pixel or transform coefficient values, which makes the embedded watermark 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 cannot be obviously mutated, and the watermark image can be watermarked through comparison of the visual characteristic vector, so that watermark information authentication is completed. Through experimental data, a feature vector resisting geometric attack can be found by combining a local binary pattern of a medical image with discrete cosine transform. When a medical image is subjected to conventional geometric transformation, the magnitude of the DCT low-if coefficient values may change somewhat, but their coefficient signs remain substantially unchanged.
According to the rule, 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 of the invention after selecting some conventional attacks and geometric attacks are shown in the table I. The medical image (128 x 128) used as a test in table one is shown in fig. 1. Column 1 of the table i shows the type of attack that the medical image has been subjected to. Columns 4 to 7 are the 4 low-IF coefficients taken from F (1,1) -F (1,4) in the LBP-DCT coefficient matrix. Wherein the real part of the coefficient F (1,1) represents the value of the dc component of the medical image. For conventional attacks, the sign of these low-if coefficient values remains substantially unchanged, and approximately equal to the medical image values; for geometric attack, part of coefficients are greatly changed, but the invention can find that when the medical image is subjected to geometric attack, the size of part of LBP-DCT low-intermediate frequency coefficients is changed but the signs of the LBP-DCT low-intermediate frequency coefficients are basically not changed. In the invention, positive LBP-DCT coefficient is represented by '1' (containing coefficient with zero value), negative coefficient is represented by '0', and for medical image, F (1,1) -F (1,4) coefficient in LBP-DCT coefficient matrix is corresponding to the symbol sequence of coefficient as follows: "101110110", see column 8 of table 1, which shows that the sequence of symbols remains similar to the original medical image regardless of normal or geometric attacks, and that the normalized correlation coefficient with the original medical image is large (see column 9), where 4 DCT coefficient symbols are taken for convenience.
Table-image full-image LBP-DCT conversion low-intermediate frequency partial coefficient and variation value after different attacks
The unit of LBP-DCT coefficient is 1.0e +003, and the correlation coefficient is 32bit compared with the result.
In specific implementation, in the method for robust watermarking of medical images based on LBP-DCT provided in the embodiment of the present invention, step S103 uses a Logistic Map to perform chaotic scrambling on an original watermark W (i, j) to obtain a chaotic scrambled watermark BW (i, j), which may specifically include: firstly, according to the initial value x0Generating a chaos sequence X (j) by a Logistic Map; wherein the initial value of the chaotic coefficient is set to be 0.2, the growth parameter is 4, and the iteration number is 32; sequencing all values in the chaotic sequence X (j) 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 the sequencing of each value in the chaotic sequence X (j) to obtain a chaotic scrambled watermark BW (i, j).
In specific implementation, in the above robust watermarking method for medical images based on LBP-DCT provided in the embodiment of the present invention, step S103 associates 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: carrying out bitwise 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) to embed the original watermark W (I, j) into the original medical image I (I, j) and simultaneously obtain a logic Key Key (I, j):
after the step S103 is executed to obtain the logical key set, the method may further include: the logic Key Key (i, j) is used as a Key to apply to a third party and is stored in the third party, so that the ownership and the 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 practicability and standardization of the network technology at present.
Specifically, in the embodiment of the invention, local binary pattern LBP transformation is firstly carried out on the medical image, then discrete cosine DCT transformation is carried out on an image LBP response matrix, a texture image visual characteristic vector resisting geometric attack is extracted from DCT coefficients, and the watermark technology is organically combined with chaotic encryption, Hash function and third-party concept, so that the geometric attack and conventional attack resistance of the digital watermark can be realized.
Similarly, in a specific implementation, in the LBP-DCT-based medical image robust watermarking method provided in the embodiment of the present invention, step S104 performs local binary pattern LBP transformation on a medical image I' (I, j) to be detected after network transmission to obtain a second LBP response matrix, which may specifically include: firstly, dividing a medical image I' (I, j) to be measured into 16 × 16 area cells); in each cell, taking eight pixels adjacent to the central pixel in the neighborhood of pixel 3x3 as sampling points, taking the gray value of the central pixel as a threshold, and generating eight-bit binary numbers as an LBP value of the central pixel after comparing the gray value of the sampling points with the threshold; specifically, comparing the gray values of one pixel in each cell with the gray values of eight adjacent pixels around the cell, and if the gray values of the adjacent pixels around the cell are greater than the gray value of the central pixel, marking the position of the adjacent pixels around the cell as 1, otherwise, marking the position of the adjacent pixels around the cell as 0; then, according to LBP values correspondingly generated by the medical image I' (I, j) to be detected, calculating a histogram of each region, and carrying out normalization processing on the histogram to obtain a statistical histogram of each region; and finally, connecting the obtained statistical histograms of the regions to obtain an LBP texture feature vector of the medical image I' (I, j) to be detected as a second LBP response matrix.
In specific implementation, in the LBP-DCT-based medical image robust watermarking method provided in the embodiment of the present invention, step S105 performs discrete cosine DCT on the second LBP response matrix, and extracts a feature vector of the medical image I' (I, j) to be detected, which may specifically include: firstly, discrete cosine DCT transform is performed on the second LBP response matrix to obtain a second coefficient matrix F1(I, j), where F1(I, j) is DCT2(I _ LBP 1); 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 measured by utilizing a hash function operation. For example, 2 × 4 blocks in the second coefficient matrix F1(I, j) may be selected to form a new matrix, and a hash function operation is used to generate a feature vector V '(I, j) of the 8-bit medical image I' (I, j) to be measured; or, 4 × 4 modules in the second coefficient matrix F1(I, j) may be selected to form a new matrix, and a hash function operation is used to generate a feature vector V '(I, j) of the 16 medical images I' (I, j) to be measured; or, 4 × 8 modules in the second coefficient matrix F1(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 medical image I' (I, j) to be measured.
In specific implementation, in the method for robust watermarking of medical image based on LBP-DCT provided in the embodiment of the present invention, the step S106 associates the feature vector V '(I, j) of the medical image I' (I, j) to be detected with the logic Key (I, j) to extract the encrypted watermark may specifically include: and carrying out exclusive or operation on the characteristic vector V ' (I, j) of the medical image I ' (I, j) to be detected and the logic Key Key (I, j), and extracting an encrypted watermark BW ' (I, j):
the algorithm only needs the 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 specific implementation, in the method for robust watermarking of medical images based on LBP-DCT provided in the embodiment of the present invention, the step S106 decrypts the encrypted watermark BW '(i, j) to obtain the restored watermark W' (i, j), which may specifically include: generating the same chaotic sequence X (j) by using a method same as watermark encryption through a Logistic Map; sequencing all values in the chaotic sequence X (j) from small to large; and restoring the pixel position space in the encrypted watermark BW '(i, j) according to the position change before and after the sequencing of each value in the chaotic sequence X (j) to obtain a restored watermark W' (i, j).
Then, by calculating the correlation coefficient NC of W (i, j) and W' (i, j), the ownership of the medical image and the embedded watermark information can be determined, and the robustness of the algorithm is measured.
The invention will be further described with reference to the accompanying drawings in which: as shown in FIG. 2, the subject of the experimental test is a 128X 128 medical image of the head, denoted as I (I, j), where 1 ≦ I, j ≦ 128. SelectingA meaningful binary image as the original watermark is recorded as: w ═ { W (i, j) | W (i, j) ═ 0, 1; i is more than or equal to 1 and less than or equal to M1,1≤j≤M2As shown in fig. 3, where the size of the watermark is 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 transformation on the response matrix, and taking 32 coefficients in consideration of robustness and the capacity of embedding a watermark at one time, namely a module of 4 x 8. 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, Logistic chaotic encryption is performed 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 a watermark is embedded is judged by calculating a normalized correlation coefficient NC, and the similarity is higher when the numerical value is closer to 1, 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 is 1.00, and the watermark can be accurately extracted.
The conventional attack resistance and the geometric attack resistance of the digital watermarking method are judged by specific examples.
First, adding gaussian noise: gaussian noise is added to the watermark using an immunity () function.
And the second table is experimental data of resisting Gaussian noise interference of the watermark. As can be seen from table two, when the gaussian noise strength is as high as 11%, the PSNR of the image after the attack is reduced to 2.95dB, and the extracted watermark, with the correlation coefficient NC being 0.68, can still be accurately extracted, and the whole data is greater than 0.6 and close to 1. This demonstrates that gaussian noise can be combated with the present invention. FIG. 6 shows a medical image at 3% Gaussian noise intensity, visually distinct from the original head medical image; fig. 7 shows the extracted watermark at 3% gaussian noise level, NC 0.82.
Table two watermark anti-Gaussian noise interference data
| 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 filter processing
Table three shows the watermark anti-median filtering capability of the medical image, and it can be seen from table three that when the median filtering parameter is [3x3] and the filtering repetition number is 5, the existence of the watermark can still be measured, and NC is 0.91. Fig. 8 shows a medical image with a median filter parameter of [3x3] and a filter repetition of 10, the image having appeared blurred; fig. 9 shows a watermark extracted when the median filter parameter is [3x3] and the filter repetition number is 10, and NC is 0.81, which makes it possible to extract a watermark. Fig. 10 shows a medical image with a median filter parameter of [5x5] and a filter repetition number of 10; fig. 11 shows a watermark extracted when the median filter parameter is [5x5] and the filter repetition number is 10, and NC is 0.60, which makes it possible to extract a watermark.
Anti-median filtering experimental data of table three watermarks
Third, rotational transformation
And the fourth table is the experimental data of watermark anti-rotation attack. It can be seen from table four that when the image is rotated by 20 ° clockwise, NC is 0.71, and still the watermark can be extracted. Fig. 12 shows a medical image rotated 10 ° clockwise; fig. 13 shows the watermark extracted by rotating 10 ° clockwise, and NC is 0.71, which makes it possible to accurately extract the watermark. Fig. 14 shows a medical image rotated 3 ° clockwise; fig. 15 shows the watermark extracted by rotating 3 ° clockwise, and NC is 0.82, which makes it possible to accurately extract the watermark.
Table four watermark anti-rotation attack experimental data
| Degree of rotation (DEG) | 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 shows that when the scaling factor is as small as 0.5, the correlation coefficient NC is 0.78, and the watermark can be extracted. Fig. 16 shows the zoomed medical image (zoom factor 0.8); fig. 17 shows the watermark extracted after the scaling attack, where NC is 0.91, and the watermark can be accurately extracted.
Anti-scaling attack experimental data of table five watermarks
| 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, translation transformation
And the sixth table shows experimental data of watermark anti-translation transformation. From the sixth table, it is known that when the image data vertically moves by 20%, the NC values are all higher than 0.61, and the watermark can be accurately extracted, so that the watermarking method has strong translation transformation resistance. FIG. 18 shows the medical image shifted vertically by 10%; fig. 19 shows the watermark extracted after 10% vertical shift, and the watermark can be accurately extracted, where NC is 0.81. FIG. 20 shows the medical image vertically shifted down by 20%; fig. 21 shows the watermark extracted after being shifted down by 20% vertically, and the watermark can be accurately extracted, where NC is 0.61.
Table six watermark anti-translation transformation experimental data
| Distance moved downward (%) | 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, shear attack
And a seventh table shows the experimental data of the watermark shearing attack resistance, and it can be seen from the seventh table that when the medical image is sheared along the coordinate axis Y, and the shearing amount is 25%, the NC value is more than 0.7, the watermark can still be extracted, which indicates that the watermarking algorithm has stronger shearing attack resistance. FIG. 22 shows the medical image after cropping the 25% image along the Y-axis; fig. 23 shows the watermark extracted after cutting 25% of the image along the Y axis, and the watermark can be accurately extracted, where NC is 0.78. FIG. 24 shows the medical image after cropping the 3% image along the Y-axis; fig. 25 shows the watermark extracted after cutting 3% of the image along the Y axis, and the watermark can be accurately extracted, NC ═ 1.00.
Shear attack resisting experimental data of table seven watermarks
| 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 perceptual hashing and data enhancement medical image digital watermarking technology, enhances the feature set of the extracted feature sequence, has better robustness, can still accurately extract the watermark aiming at the conventional attacks such as gaussian noise interference, median filtering processing and the like, and the geometric attacks such as rotation transformation, scaling transformation, translation transformation, shearing attack and the like, and has stronger capabilities of resisting the conventional attack and resisting the geometric attack.
The embodiment of the invention provides a medical image robust watermarking method based on LBP-DCT, which comprises the following steps: performing Local Binary Pattern (LBP) transformation on an original medical image to acquire a first LBP response matrix; performing Discrete Cosine Transform (DCT) on the first LBP response matrix, and extracting a characteristic vector of an original medical image; performing chaotic scrambling on the original watermark by using a Logistic Map to obtain a chaotic scrambled watermark, and associating a feature vector of the original medical image with the chaotic scrambled watermark to obtain a logic key; local Binary Pattern (LBP) transformation is carried out on the medical image to be detected after network transmission, and a second LBP response matrix is obtained; performing Discrete Cosine Transform (DCT) on the second LBP response matrix, and extracting a characteristic vector of the medical image to be detected; associating the feature vector of the medical image to be detected with the logic key to extract an encrypted watermark, 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 to determine ownership of the original medical image and embedded watermark information. The robust watermarking method for the medical image combines Local Binary Pattern (LBP) of the medical image and Discrete Cosine Transform (DCT) based on five parts of characteristic vector extraction, watermark encryption, watermark embedding, watermark extraction and watermark decryption, and has the advantages of LBP rotation invariance and the characteristics of high conventional attack resistance, ergodicity, robustness and the like of the DCT, so that a characteristic vector resisting geometric attack can be found, the robust watermarking method is particularly prominent in terms of geometric attacks such as rotation, translation, shearing and the like, the content of original encryption body data is not changed by embedding of the watermark, the defects caused by original image data modification by the traditional watermark embedding technology are well overcome, the quality of the medical image is ensured, the robust watermarking method has strong robustness and invisibility, and the privacy information of a patient and the data security of the medical image can be simultaneously protected.
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 phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The LBP-DCT-based medical image robust watermarking method provided by the present invention is described in detail above, and a specific example is applied in the text to explain the principle and the implementation of the present invention, and the description of the above embodiment is only used to help understand the method of the present invention and the core idea thereof; 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 (10)
1. A robust watermarking method for medical images based on LBP-DCT is characterized by comprising the following steps:
performing Local Binary Pattern (LBP) transformation on an original medical image to acquire a first LBP response matrix;
performing discrete cosine DCT (discrete cosine transform) on the first LBP response matrix, and extracting a characteristic vector of the original medical image;
performing chaotic scrambling on an original watermark by using a Logistic Map to obtain a chaotic scrambled watermark, and associating a feature vector of the original medical image with the chaotic scrambled watermark to obtain a logic key;
local Binary Pattern (LBP) transformation is carried out on the medical image to be detected after network transmission, and a second LBP response matrix is obtained;
performing Discrete Cosine Transform (DCT) on the second LBP response matrix, and extracting a characteristic vector of the medical image to be detected;
associating the feature vector of the medical image to be detected with the logic key to extract an encrypted watermark, 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 to determine ownership of the original medical image and embedded watermark information.
2. The LBP-DCT-based medical image robust watermarking method according to claim 1, wherein performing local binary pattern LBP transformation on an original medical image to obtain a first LBP response matrix specifically comprises:
dividing the original medical image into 16 x 16 regions;
in each area, taking eight pixels adjacent to a central pixel in a pixel 3x3 neighborhood as sampling points, taking the gray value of the central pixel as a threshold, and generating eight-bit binary number as an LBP value of the central pixel after comparing the gray value of the sampling points with the threshold;
calculating a histogram of each region according to an LBP value correspondingly generated by the original medical image, and performing normalization processing on the histogram to obtain a statistical histogram of each region;
and connecting the obtained statistical histograms of all the regions, and acquiring an LBP texture characteristic vector of the original medical image as a first LBP response matrix.
3. The LBP-DCT-based medical image robust watermarking method according to claim 2, wherein performing discrete cosine DCT transform on the first LBP response matrix to extract the feature vector of the original medical image specifically comprises:
performing Discrete Cosine Transform (DCT) on the first LBP response matrix to obtain a first coefficient matrix;
selecting a module with a set size in the first coefficient matrix to form a new matrix;
and generating a feature vector of the original medical image by utilizing a hash function operation.
4. The LBP-DCT-based medical image robust watermarking method according to claim 3, wherein the original watermark is chaotically scrambled using a Logistic Map to obtain a chaos scrambled watermark, specifically comprising:
generating a chaotic sequence through a Logistic Map;
sequencing all values in the chaotic sequence from small to large;
and scrambling the pixel position space in the original watermark according to the position change before and after the ordering of each value in the chaotic sequence to obtain the chaotic scrambled watermark.
5. The LBP-DCT-based medical image robust watermarking method according to claim 4, wherein associating the feature vector of the original medical image with the chaotic scrambling watermark to obtain a logical key specifically comprises:
and carrying out 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 at the same time.
6. The LBP-DCT-based medical image robust watermarking method according to claim 5, further comprising, after obtaining 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.
7. The LBP-DCT-based medical image robust watermarking method according to claim 6, wherein performing local binary pattern LBP transformation on the medical image to be detected after network transmission to obtain a second LBP response matrix specifically comprises:
dividing the medical image to be detected into 16-by-16 areas;
in each area, taking eight pixels adjacent to a central pixel in a pixel 3x3 neighborhood as sampling points, taking the gray value of the central pixel as a threshold, and generating eight-bit binary number as an LBP value of the central pixel after comparing the gray value of the sampling points with the threshold;
calculating a histogram of each region according to the LBP value correspondingly generated by the medical image to be detected, and performing normalization processing on the histogram to obtain a statistical histogram of each region;
and connecting the obtained statistical histograms of the regions, and acquiring an LBP texture characteristic vector of the medical image to be detected as a second LBP response matrix.
8. The LBP-DCT-based medical image robust watermarking method according to claim 7, wherein performing discrete cosine DCT transform on the second LBP response matrix to extract the feature vector of the medical image to be detected specifically comprises:
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 characteristic vector of the medical image to be detected by utilizing hash function operation.
9. The LBP-DCT-based medical image robust watermarking method according to claim 8, wherein associating the feature vector of the medical image to be tested with the logical key to extract the encrypted watermark specifically comprises:
and carrying out XOR operation on the feature vector of the medical image to be detected and the logic key to extract an encrypted watermark.
10. The LBP-DCT-based medical image robust watermarking method according to claim 9, wherein decrypting the encrypted watermark to obtain a restored watermark specifically comprises:
generating the chaotic sequence through a Logistic Map;
sequencing all values in the chaotic sequence from small to large;
and restoring the pixel position space in the encrypted watermark according to the position change before and after the sequencing of each value in the chaotic sequence to obtain the restored watermark.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114092302A (en) * | 2021-10-25 | 2022-02-25 | 海南大学 | Medical image robust watermarking method based on residual error network and depth separable convolution |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101200378B1 (en) * | 2011-08-30 | 2012-11-12 | 인하대학교 산학협력단 | A robust texture feature extraction using the localized angular phase |
| KR20140082385A (en) * | 2012-12-24 | 2014-07-02 | 계명대학교 산학협력단 | medical image retrieval method based on image clustering |
| CN111783704A (en) * | 2020-07-07 | 2020-10-16 | 中电万维信息技术有限责任公司 | Face recognition system based on particle swarm optimization radial basis |
| CN111968025A (en) * | 2020-08-19 | 2020-11-20 | 海南大学 | Bandlelet-DCT-based medical image robust zero watermarking method |
-
2021
- 2021-03-31 CN CN202110350365.7A patent/CN113160030B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101200378B1 (en) * | 2011-08-30 | 2012-11-12 | 인하대학교 산학협력단 | A robust texture feature extraction using the localized angular phase |
| KR20140082385A (en) * | 2012-12-24 | 2014-07-02 | 계명대학교 산학협력단 | medical image retrieval method based on image clustering |
| CN111783704A (en) * | 2020-07-07 | 2020-10-16 | 中电万维信息技术有限责任公司 | Face recognition system based on particle swarm optimization radial basis |
| CN111968025A (en) * | 2020-08-19 | 2020-11-20 | 海南大学 | Bandlelet-DCT-based medical image robust zero watermarking method |
Non-Patent Citations (3)
| Title |
|---|
| 刘瑶利;李京兵;: "一种基于DCT和Logistic Map的医学图像鲁棒多水印方法", 计算机应用研究, no. 11, pages 3430 - 3437 * |
| 田野等: "基于LBP和多层DCT的人脸活体检测算法", 计算机研究与发展, no. 03, pages 643 - 650 * |
| 韩友德;杨鹏;任高山;张元龙;林泉;: "基于DWT和LBP的纹理特征提取研究", 计算机与数字工程, no. 03 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114092302A (en) * | 2021-10-25 | 2022-02-25 | 海南大学 | Medical image robust watermarking method based on residual error network and depth separable convolution |
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