CN103279918A - Volume data watermark realizing method based on three-dimension DCT and chaotic scrambling - Google Patents

Abstract

The invention discloses a volume data watermark realizing method based on three-dimension DCT and chaotic scrambling and belongs to the multimedia signal processing field. The method comprises the following steps: first, utilizing the nature of a logistic map to conduct chaotic scrambling on a watermark; then, extracting an eigenvector through conducting 3D-DCT conversion on original volume data to embed the watermark, and associating the eigenvector and the chaotic scrambled watermark to obtain a two-valued logic sequence, storing the two-valued logic sequence in a third party; conducting 3D-DCT conversion on the volume data to be detected to extract the eigenvector of the volume data to be detected, and associating the eigenvector with the two-valued logic sequence stored in the third party to conduct extraction of the watermark; finally, utilizing the nature of the logistic map and the same initial value to conduct recovery of the watermark. The volume data watermark realizing method is a volume data digital watermark technology based on the three-dimension DCT and the chaotic scrambling, and has good robustness. Moreover, embedding of the watermark does not change the content of the original volume data.

Description

A kind of volume data watermark implementing method based on three-dimensional DCT and chaos scramble

Technical field

The present invention relates to a kind of digital watermark technology of the volume data characteristics of image based on three-dimensional dct transform and chaos scramble, is a kind of multimedia data protection method, belongs to field of multimedia signal processing.

Technical background

Utilize the internet can realize distance medical diagnosis; but when passing through internet transmission patient's medical imaging; may reveal patient's personal information; how to protect the patient individual privacy, make that the personal information in patient's the medical imagings such as CT, MRI, patient's data such as electronic health record are not revealed, this problem is along with the universal of internet becomes serious day by day.Medical image digital watermark technology (Medical Image Watermarking is called for short MIW) can address this problem effectively.

Digital watermark technology is the copyright protection for the Digital Media on the internet at first; utilize the characteristics such as invisibility, robustness of digital watermarking now; can be hidden in patient's personal information in its medical image, to guarantee its safe transmission on the internet.The appearance of medical image digital watermarking, make distance medical diagnosis, when the required relevant patient data of remote operation transmits on the internet, can effectively protect patient's privacy, avoid patient's data to be distorted.

At present less for the research of the volume data digital watermarking algorithm of resist geometric attacks.And volume data exists in medical image in a large number, and as the volume data that CT, MRI image all are made up of section, it is significant how therefore to study in volume data embed digital watermark, and for medical volume data, generally is not allow to revise its content.This has improved difficulty for embed watermark in volume data again.

In a word, in three-dimensional data, embed can anti-ly rotate, the algorithm of the digital watermarking of geometric attacks such as convergent-divergent, translation, shearing, distortion, still belong to blankly at present, do not see open report.

Summary of the invention

The objective of the invention is to propose a kind of based on three-dimensional dct transform and Logistic Map chaos scramble; realization can be resisted volume data digital watermarking embedding and the extracting method that geometric attack can be resisted conventional attack again; it has higher robustness; and the embedding of watermark does not influence the voxel data value of initial body data; be a kind of zero watermarking project, thus better protect three-dimensional data.

Principle of the present invention is: at first utilize the watermark information of Logistic Map to carry out pre-service, again medical volume data is carried out overall 3D-DCT conversion, in the 3D-DCT conversion coefficient, extract the proper vector of a resist geometric attacks, and pretreated watermark is associated with this proper vector, utilize the robustness of proper vector to realize anti-geometry and the conventional attack of digital watermarking.

To achieve these goals, the present invention is performed such: use Logistic Map and produce chaos sequence (i j) carries out chaos scramble and reduction, improves the security of watermark to the watermarking images W of two-value; Based on the three-dimensional dct transform of the overall situation (here for volume data, be not divided into little stereo block and carry out three-dimensional dct transform), in three-dimensional dct transform coefficient, extract the proper vector of a resist geometric attacks, and digital watermark and cryptography combined, realized anti-geometry and the conventional attack of digital watermarking.The method applied in the present invention comprises that chaos scramble, embedding, extraction and the watermark of watermark reduce four major parts, and first is the chaos scramble of watermark, comprising: (1) produces chaos sequence X (j) by Logistic Map; (2) according to X (j) to the watermarking images W of two-value (i j) carries out scramble, obtain the chaos scramble watermark BW (i, j); Second portion is the embedding of watermark, comprise: (3) are by carrying out overall three-dimensional dct transform to volume data, obtain the proper vector V (j) of a resist geometric attacks, (4) according to the watermark BW of chaos scramble (i, j) and the proper vector V (j) of the volume data of extracting, by the Hash functional operation, generate a two-valued function key sequence Key (i, j), (j) there is the third party in i with two-valued function sequence Key then; Third part is the extraction of watermark, comprise: the proper vector V ' that (5) obtain volume data to be measured (j), (6) utilize be present in third-party two-valued function key sequence Key (i, j) and the proper vector V ' of volume data to be measured (j), extract watermark BW ' (i, j); The 4th part is the reduction of watermark, comprising: (7) use Logistic Map, obtain identical chaos sequence X (j), and reduce to watermark by X (j) (8).

Now be elaborated as follows to method of the present invention:

At first select a significant bianry image as the watermark that will embed medical volume data, be designated as W={w (i, j) | w (i, j)=0,1; 1≤i≤M1,1≤j≤M2}; Simultaneously, choose a MRI volume data carrying among the Matlab as the primitive medicine volume data, be expressed as: F={f (i, j, k) | f (i, j, k) ∈ R; 1≤i≤M, 1≤j≤N, 1≤k≤P}.Wherein, w (i, j) and f (i, j k) represent the grey scale pixel value of watermark and voxel (Voxel) data value of primitive medicine volume data respectively, the grey scale pixel value in this similar two dimensional image, convenient for the purpose of, establish M1=M2, M=N.

First: the chaos scramble of watermark

1) generates chaos sequence by Logistic Map;

By initial value x ₀Generate chaos sequence X (j) by Logistic Map chaos system.

2) obtain the watermark of chaos scramble;

At first, the value among the chaos sequence X (j) is sorted according to ascending order, changes according to each value ordering front-back direction among the X (j) then scramble is carried out in watermark locations of pixels space, obtain the chaos scramble watermark BW (i, j).

Second portion: the embedding of watermark

3) by the initial body data being carried out overall three-dimensional dct transform, obtain a proper vector V (j) of this volume data;

(i, j k) carry out overall three-dimensional dct transform to initial body data F earlier, obtain three-dimensional DCT matrix of coefficients FD (i, j, k), again from three-dimensional DCT matrix of coefficients FD (i, j, k) in, L Low Medium Frequency coefficient value before taking out, by three-dimensional DCT coefficient being carried out the proper vector V (j) that symbolic operation obtains this volume data, specific practice be when the DCT coefficient on the occasion of or during null value with " 1 " expression, with " 0 " expression, process prescription is as follows during for negative value for coefficient:

FD(i,j,k)=DCT3(F(i,j,k))

V(j)=Sign(FD(i,j,k))

4) according to the watermark BW of chaos scramble (i, j) and the proper vector V (j) of volume data generate a two-valued function sequence Key (i, j);

$\mathrm{Key}(i,j)=V\left(j\right)\⊕\mathrm{BW}(i,j)$

(i is that (i, j), the Hash function commonly used by cryptography generates by the proper vector V (j) of volume data and the watermark BW of chaos scramble j) to Key.(i j), need use when extracting watermark afterwards to preserve Key.By (i j) applies for to the third party as key, to obtain entitlement and the right to use of medical volume data, reaches the purpose of copyright protection with Key.

Third part: the extraction of watermark

5) the proper vector V ' that obtains volume data to be measured (j);

If volume data to be measured is that (i, j k), are that (i, j k), by the method for above-mentioned steps 3, try to achieve the proper vector V ' of volume data to be measured (j) to FD ' through obtaining three-dimensional DCT matrix of coefficients behind the overall three-dimensional dct transform of volume data to F ';

FD’(i,j,k)=DCT3(F’(i,j,k))

V’(j)=Sign(FD’(i,j,k))

6) in volume data to be measured, extract watermark BW ' (i, j);

According to the logic key sequence Key that generates when the embed watermark (i, j) and the proper vector V ' of volume data to be measured (j), the watermark BW ' that utilizes Hash function character to extract to contain in the volume data to be measured (i, j)

${\mathrm{BW}}^{,}(i,j)=\mathrm{Key}(i,j)\⊕V^{,}\left(j\right)$

The 4th part: the reduction of watermark

7) generate chaos sequence by Logistic Map;

By with initial value x that above step 1) is identical ₀Generate identical chaos sequence X (j) by Logistic Map chaos system;

8) watermark of reduction extraction;

At first with ascending ordering of value among the chaos sequence X (j), then according to each value ordering front-back direction among the X (j) change to watermark locations of pixels space reduce the watermark W ' that obtains reducing (i, j).

Again according to W (i, j) and W ' (i, degree of correlation j) is differentiated the entitlement of volume data to be measured.

The present invention and existing medical science digital watermark relatively have following advantage:

At first, because the present invention is based on the digital watermark technology of three-dimensional dct transform, the embedding of watermark and extraction are to carry out in frequency domain, by the experimental data confirmation of back, this watermark not only has stronger anti-conventional attack ability, and stronger resist geometric attacks ability is arranged; Secondly, embedding be through the watermark of Logistic Map chaos scramble, watermark information is become disorderly and unsystematic, improved the security of watermark information; At last, the embedding of watermark does not influence the voxel data value of initial body data, is a kind of zero digital watermark, better must protect medical volume data.This characteristic especially has very high practical value at aspects such as medical image processing, and usable range is wide, and can realize embedding and the extraction of many watermarks and big watermark.

Below we from the explanation of theoretical foundation and test figure:

1) 3 d-dem cosine transform (3D-DCT)

Three-dimensional dct transform formula is as follows:

Corresponding size is M * N * P volume data, and 3 d-dem cosine direct transform (DCT) formula is as follows:

$F(u,v,w)=c\left(u\right)c\left(v\right)c\left(w\right)[\underset{x=0}{\overset{M-1}{\mathrm{\Σ}}}\underset{y=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{p=0}{\overset{P-1}{\mathrm{\Σ}}}f(x,y,z)\·\cos \frac{(2x+1)\mathrm{u\π}}{2M}\cos \frac{(2y+1)\mathrm{v\π}}{2N}\cos \frac{(2z+1)\mathrm{w\π}}{2P}]$

u＝0,1,...,M-1;v＝0,1,...,N-1;w＝0,1,...,P-1;

In the formula:

$c\left(u\right)=\left\{\begin{array}{cc}\sqrt{1/M}& u=0\\ \sqrt{2/M}& u=\mathrm{1,2},...,M-1\end{array}\right.$

$c\left(v\right)=\left\{\begin{array}{cc}\sqrt{1/N}& v=0\\ \sqrt{2/N}& v=\mathrm{1,2},...,N-1\end{array}\right.$

$c\left(w\right)=\left\{\begin{array}{cc}\sqrt{1/P}& w=0\\ \sqrt{2/P}& w=\mathrm{1,2},...,P-1\end{array}\right.$

Here, f (x, y, z) be volume data V (x, y, the voxel of z) locating (voxel) data value, (u, v w) are the 3D-DCT conversion coefficient of this voxel data correspondence to F.

3 d-dem cosine inverse transformation (IDCT) formula is as follows:

$f(x,y,z)=\underset{u=0}{\overset{M-1}{\mathrm{\Σ}}}\underset{v=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{w=0}{\overset{P-1}{\mathrm{\Σ}}}\left[c\left(u\right)c\left(v\right)c\left(w\right)F(u,v,w)\cos \frac{(2x+1)\mathrm{u\π}}{2M}\cos \frac{(2y+1)\mathrm{v\π}}{2N}\cos \frac{(2z+1)\mathrm{w\π}}{2P}\right]$

x＝0,1,...,M-1;y＝0,1,...,N-1;z＝0,1,...,P-1

Wherein, (x, y z) are the spatial domain sampled value; (u, v w) are the frequency field sampled value.The acquisition of medical volume data can be by CT and MRI(Magnetic Resnane Iamge, magnetic resonance imaging), volume data (Volume data) is made up of the section (slice) of many layers, and each section is a two dimensional image, size is M * N, and the number of plies of section is P.

2）Logistic?Map

Chaos is a kind of random motion that seems to be, and refers to the similar process at random that occurs in deterministic system.Therefore, its initial value and parameter arranged, we just can generate this chaos system.Logistic Map is foremost a kind of chaos system, and it is the Nonlinear Mapping that is given by the following formula:

x _k+1＝μx _k(1-x _k)

Wherein, 0≤μ≤4 are growth parameter, x _k∈ (0,1) is system variable, and k is iterations.The research work of Chaos dynamic system points out, when growth parameter 3.569945≤μ≤4, Logistic Map works in chaos state.Can see that initial value has a slight difference will cause the significant difference of chaos sequence.Therefore, above sequence is a desirable key sequence.Set μ=4 herein, chaos sequence is by different initial value x ₀Produce.

3) volume data principal character vector choosing method

The main cause of present most of watermarking algorithm resist geometric attacks ability is: people are embedded in digital watermarking in voxel or the conversion coefficient, and the slight geometric transformation of volume data usually can cause the bigger variation suddenly of voxel data value or transform coefficient values.The watermark that is embedded in like this in the volume data is just attacked easily.If can find the proper vector of an antimer data geometrical feature, when little geometric transformation takes place in volume data, tangible sudden change can not take place in this proper vector value, we are associated the proper vector of the digital watermarking that will embed and this volume data then, and the digital watermarking of Qian Ruing just has resist geometric attacks ability preferably so.By being observed, the overall DCT coefficient of a large amount of volume datas finds, when being carried out common geometric transformation, (realizes by geometric transformation is carried out in each section) individual data items, some variations may take place in the size of three-dimensional DCT Low Medium Frequency coefficient value, but its coefficient symbols remains unchanged substantially, and we illustrate by some experimental datas of table 1.The former figure that is used as test in the table 1 is Fig. 1, it is a section (getting the tenth) of a MRI volume data carrying among the matlab, " the 1st row " demonstration is volume data type under attack in the table 1, this sectioning image that is subjected to behind the conventional attack is seen Fig. 2 to Fig. 4, and Fig. 5 to Fig. 8 is seen in the three-dimensional imaging of conventional attack correspondence; The sectioning image that is subjected to behind the geometric attack is seen Fig. 9 to Figure 12, and Figure 13 to Figure 16 is seen in its corresponding three-dimensional imaging." the 2nd row " expression of table 1 be the Y-PSNR (PSNR) of volume data after under attack; " the 3rd row " of table 1 arrive " the 10th row ", are F (1,1,1)-eight Low Medium Frequency coefficients of F (2,2,2) of getting in the three-dimensional DCT matrix of coefficients.For conventional attack, these Low Medium Frequency coefficient values F (1,1,1)-F (2,2,2) remains unchanged and the DCT coefficient value approximately equal of former volume data substantially; For geometric attack, the part coefficient has bigger variation, but we can find that volume data is after being subjected to geometric attack, and the size of most of DCT Low Medium Frequency coefficient has taken place to change but its symbol does not change substantially.We represent positive DCT coefficient (containing value is zero coefficient) with " 1 ", negative DCT coefficient is represented with " 0 ", so for the initial body data, F (1 in the three-dimensional DCT matrix of coefficients, 1,1)-F (2,2,2) coefficient, corresponding coefficient symbols sequence is: " 11001011 " specifically see Table 1 the 11st row, observing these row can find, it is similar with the maintenance of initial body data no matter conventional attack still is this symbol sebolic addressing of geometric attack, all bigger with initial body data normalization related coefficient, sees Table 1 " the 12nd row " (having got 8 three-dimensional DCT coefficient symbols here for the purpose of convenient).

The Low Medium Frequency part coefficient of table 1 volume data overall situation 3D-DCT conversion and be subjected to different the attack after changing value

* the 1.0e+002 of 3D-DCT conversion coefficient unit

In order to prove that further the proper vector of extracting as stated above is a key character of this volume data, we are again different tested objects (seeing Figure 17 to Figure 24), carry out overall three-dimensional dct transform, obtain corresponding DCT coefficient F (1,1,1)-F (4,4,4), from the statistics angle, preceding 64 DCT coefficients have been got here.And obtain related coefficient each other, result of calculation is as shown in table 2.

As can be seen from Table 2, at first, the related coefficient maximum between the volume data self is 1.00; Secondly, the related coefficient between Figure 23 and Figure 24 also more greatly 0.62, and the volume data that these two figure are two similar livers of shape; Figure 18 and Figure 19, related coefficient is 0.28, and is also bigger, be the third-largest related coefficient in table, and these two heads that figure is human body is also more similar.Facies relationship numerical value between other volume data proper vector is less, this with our eye-observation to be consistent, the volume data eigenwert that this explanation is extracted by the method for this invention has reflected the main resemblance of volume data.

The related coefficient of the different volume data proper vectors of table 2 (vector length 64bit)

?	Va	Vb	Vc	Vd	Ve	Vf	Vg	Vh
									Va	1.00	-0.31	-0.21	0.21	-0.15	0.12	-0.15	-0.15
Vb	-0.31	1.00	0.28	-0.15	-0.09	0.18	0.15	0.15
									Vc	-0.21	0.28	1.00	-0.25	-0.12	-0.21	0.00	-0.06
Vd	0.21	-0.15	-0.25	1.00	0.06	-0.09	-0.06	-0.06
									Ve	-0.15	-0.09	-0.12	0.06	1.00	-0.03	0.12	0.25
Vf	0.12	0.18	-0.21	-0.09	-0.03	1.00	0.03	0.15
									Vg	-0.15	0.15	0.00	-0.06	0.12	0.03	1.00	0.62
Vh	-0.15	0.15	-0.06	-0.06	0.25	0.15	0.62	1.00

3) position of watermark embedding and the length of disposable embedding

According to human visual system (HVS), the Low Medium Frequency signal is bigger to people's visual impact, is image outline for two dimensional image, is exactly the appearance profile of volume data for 3-D view.Therefore, the proper vector of the volume data that we are selected also is the symbol sebolic addressing of Low Medium Frequency coefficient, the number of Low Medium Frequency coefficient is selected and the size of carrying out the initial body data of overall three-dimensional dct transform, and the robustness of the quantity of information of disposable embedding and requirement is relevant, the length L of the proper vector of choosing is more little, the quantity of information of disposable embedding is more few, but robustness is more high.In the experiment of back, the length that we choose L is 32.

In sum, we pass through the analysis to the overall three-dimensional DCT coefficient of volume data, utilize the symbol sebolic addressing of three-dimensional DCT Low Medium Frequency coefficient to obtain a kind of method that obtains the visual feature vector of volume data.

Description of drawings

Fig. 1 is a section (acquiescence is the 10th section of volume data) of initial body data.

Fig. 2 is the sectioning image after disturbing through 5% Gaussian noise.

Fig. 3 is through the sectioning image after the JPEG compression (compression quality is 4%).

Fig. 4 is through the sectioning image behind the medium filtering (filtering parameter is [3x3])).

Fig. 5 is the three-dimensional imaging of initial body data correspondence.

Fig. 6 is that to be subjected to intensity be that 5% Gauss disturbs the corresponding three-dimensional imaging in back to volume data.

Fig. 7 is the corresponding three-dimensional imaging in JPEG compression (compression quality is 4%) back.

Fig. 8 is through three-dimensional imaging corresponding behind the medium filtering (filtering parameter is [3x3]).

Fig. 9 is the sectioning image through up time rotation 20 degree.

Figure 10 is the sectioning image through 0.5 times of convergent-divergent.

Figure 11 is that vertical direction moves down 10% sectioning image.

Figure 12 is that Z-direction is sheared first sectioning image after 10%.

Figure 13 is the three-dimensional imaging of up time rotation 20 degree.

Figure 14 is that zoom factor is 0.5 three-dimensional imaging.

Figure 15 is that vertical direction moves down 10% three-dimensional imaging.

Figure 16 is that Z-direction is sheared 10% three-dimensional imaging.

Figure 17 is the three-dimensional imaging of volume data MRI_1.

Figure 18 is the three-dimensional imaging of volume data MRI_2.

Figure 19 is the three-dimensional imaging of volume data MRI_3.

Figure 20 is the three-dimensional imaging of volume data Engine.

Figure 21 is the three-dimensional imaging of volume data Teddy bear.

Figure 22 is the three-dimensional imaging of volume data Tooth.

Figure 23 is the three-dimensional imaging of volume data Liver_1.

Figure 24 is the three-dimensional imaging of volume data Liver_2.

Figure 25 is original watermark.

Figure 26 is through the watermark behind the Logistic Map chaos scramble.

Figure 27 is the watermark section that does not add when disturbing.

Figure 28 is the three-dimensional reconstruction figure that does not add when disturbing.

Figure 29 does not add the watermark of extracting when disturbing.

Figure 30 is the sectioning image (Gaussian noise intensity 5%) after Gaussian noise is disturbed.

Figure 31 is the three-dimensional reconstruction figure (Gaussian noise intensity 5%) after Gaussian noise is disturbed.

Figure 32 is the watermark (Gaussian noise intensity 5%) that Gaussian noise disturbs the back to extract.

Figure 33 is the sectioning image (the compression quality parameter is 8%) after the JPEG compression.

Figure 34 is the volume data three-dimensional imaging (the compression quality parameter is 8%) after the JPEG compression.Figure 35 is the watermark (the compression quality parameter is 8%) that extract JPEG compression back.

Figure 36 is the sectioning image (filtering parameter is [5x5], and filter times is 10 times) behind the medium filtering.

Figure 37 is the three-dimensional imaging (filtering parameter is [5x5], and filter times is 10 times) of the volume data behind the medium filtering.

Figure 38 is the watermark (filtering parameter is [5x5], and filter times is 10 times) of extracting behind the medium filtering.

Figure 39 is the sectioning image behind up time rotation 20 degree.

Figure 40 is the three-dimensional imaging of up time rotation 20 degree back volume datas.

Figure 41 is the watermark that extract up time rotation 20 degree backs.

Figure 42 is the section of initial body data correspondence.

Figure 43 is that zoom factor is 0.5 sectioning image.

Figure 44 is that zoom factor is 0.5 three-dimensional imaging.

Figure 45 is that zoom factor is the watermark of extracting in 0.5 o'clock.

Figure 46 vertically moves down 6% sectioning image.

Figure 47 is the three-dimensional imaging that vertically moves down 6% volume data correspondence.

Figure 48 vertically moves down the watermark that extract 6% back.

Figure 49 is after Z-direction shears 20%, first sectioning image of volume data.

Figure 50 is after Z-direction shears 20%, the three-dimensional imaging of volume data.

Figure 51 is after Z-direction shears 20%, the watermark of extraction.

Figure 52 is that the distortion frequency factor is 13 o'clock sectioning image.

Figure 53 is that the distortion frequency factor is the three-dimensional imaging of 13 o'clock volume data.

Figure 54 is that the distortion frequency factor is the watermark of extracting in 13 o'clock.

Embodiment

The invention will be further described below in conjunction with accompanying drawing, selects a significant bianry image as original watermark, is designated as: and W={w (i, j) | w (i, j)=0,1; 1≤i≤M1,1≤j≤M2} sees Figure 25, the size of watermark here is 32 * 32.See Figure 26 by the watermark behind the Logistic Map chaos scramble, can see obviously that very big variation has taken place in watermark, security improves.Fig. 1 is seen in a section of primitive medicine volume data, is to take from the nuclear magnetic resonance 3-D view volume data (MRI.mat) that carries among the matlab, and the size of volume data is 128x128x27, sees Fig. 5.The initial body data be expressed as F (i, j, k), 1≤i wherein, j≤128; 1≤k≤27, corresponding 3D-DCT matrix of coefficients be FD (i, j, k), 1≤i wherein, j≤128; 1≤k≤27.Consider robustness and disposable embed watermark capacity we get 32 coefficients.By watermarking algorithm detect W ' (i, j) after, we have judged whether that by calculating normalized correlation coefficient NC (Normalized Cross Correlation) watermark embeds.

Do not add the watermarking images (acquiescence is selected the tenth section, and test is made up of 27 sections altogether with volume data) when disturbing here.

Figure 27 is the watermark section that does not add when disturbing;

Figure 28 is the volume data three-dimensional imaging that does not add when disturbing;

Figure 29 does not add the watermark of extracting when disturbing, and can see NC=1.00, can accurately must extract watermark.

Below we judge anti-conventional attack ability and the resist geometric attacks ability of this digital watermark method by concrete experiment.

Test the ability of the anti-conventional attack of this watermarking algorithm earlier.

(1) adds Gaussian noise

Use imnoise () function in watermarking images, to add Gaussian noise.

Table 3 is experimental datas that the anti-Gaussian noise of watermark is disturbed.Therefrom can see, when Gaussian noise intensity up to 25% the time, the PSNR of watermark volume data is down to 0.09dB, the watermark of Ti Quing at this moment, related coefficient NC=0.66 still can accurately must extract watermark.This explanation adopts this invention that good anti-Gaussian noise ability is arranged.

Watermark section when Figure 30 is Gaussian noise intensity 5%, visually very fuzzy;

Figure 31 is corresponding volume data three-dimensional imaging, and is visually very fuzzy, and the PSNR=6.01dB of volume data is lower;

Figure 32 is the watermark of extracting, and can accurately must extract watermark, NC=0.94.

The anti-Gaussian noise interfering data of table 3 watermark

Noise intensity (%)	1	3	5	10	15	20	25
								PSNR(dB)	12.51	8.02	6.01	3.32	1.81	0.78	0.09
NC	1.00	0.95	0.94	0.86	0.84	0.76	0.66

(2) the JPEG compression is handled

Adopt image compression quality percentage as parameter the watermark volume data to be carried out the JPEG compression; Table 4 is the anti-JPEG compression experiment of watermark volume data data.When compression quality is 2% only, at this moment compression quality is lower, still can extract watermark, NC=0.89.

Figure 33 is that compression quality is 8% sectioning image, and blocking artifact has appearred in this figure;

Figure 34 is corresponding volume data three-dimensional imaging, and three-dimensional blocking artifact has appearred in this figure;

Figure 35 is the watermark of extracting, and NC=0.95 can accurately extract watermark.

The anti-JPEG compression experiment of table 4 watermark data

Compression quality (%)	2	4	8	10	20	40	60	80
									PSNR(dB)	16.57	17.82	20.21	21.20	23.10	25.06	26.61	29.31
NC	0.89	0.78	0.95	0.94	0.95	0.95	1.00	1.00

(3) medium filtering is handled

Table 5 is the anti-medium filtering ability of watermark volume data, and it can be seen from the table, when the medium filtering parameter is [5x5], the filtering multiplicity is 20 o'clock, still can record the existence of watermark, NC=0.95.

Figure 36 is that the medium filtering parameter is [5x5], and the filtering multiplicity is 10 sectioning image, and bluring has appearred in image;

Figure 37 is corresponding volume data three-dimensional imaging, and at this moment profile such as ear is not too clearly demarcated;

Figure 38 is the watermark of extracting, and NC=0.95 can accurately extract watermark.

The anti-medium filtering experimental data of table 5 watermark

Watermark resist geometric attacks ability

(1) rotational transform

Table 6 is attacked experimental data for the anti-rotation of watermark.Can see that from table NC=0.83 still can extract watermark when watermark volume data up time is rotated 35 °.

Figure 39 is the watermark sectioning image of up time rotation 20 degree;

Figure 40 is corresponding volume data three-dimensional imaging, and at this moment, the signal to noise ratio (S/N ratio) of watermark volume data is lower, PSNR=12.44dB;

Figure 41 is the watermark of extracting, and NC=0.87 can extract watermark exactly.

Experimental data is attacked in the anti-rotation of table 6 watermark

The up time rotation	5 degree	10 degree	15 degree	20 degree	25 degree	30 degree	35 degree
								PSNR(dB)	16.54	13.97	12.98	12.44	12.04	11.68	11.33
NC	0.87	0.87	0.87	0.87	0.83	0.83	0.83

(2) scale transformation

Table 7 is the nonshrink attack experimental data of putting of watermark volume data, as can be seen from Table 7 when watermark volume data zoom factor little to 0.2 the time, related coefficient NC=1.00 can accurately extract watermark.

Figure 42 is the sectioning image of initial body data;

Figure 43 is the watermark sectioning image (zoom factor is 0.5) behind the convergent-divergent;

Figure 44 is after convergent-divergent is attacked, the three-dimensional imaging of volume data correspondence (zoom factor is 0.5);

Figure 45 is after convergent-divergent is attacked, the watermark of extraction, and NC=1.00 can accurately must extract watermark.

The nonshrink attack experimental data of putting of table 7 watermark

Zoom factor	0.2	0.5	0.8	1.0	1.2	2.0	4.0
								NC	1.00	1.00	1.00	1.00	1.00	1.00	1.00

(3) translation transformation

Table 8 is the anti-translation transformation experimental datas of watermark.From table, learn when level or when vertically moving 10%, the NC value all is higher than 0.5, can accurately extract watermark, so this water mark method has stronger anti-translation transformation ability.

Figure 46 cuts into slices vertically to move down 6% image;

Figure 47 is after each section of volume data vertically moves down 6%, corresponding three-dimensional imaging, and PSNR=11.66dB at this moment, signal to noise ratio (S/N ratio) is lower;

Figure 48 is the watermark of extracting, and can accurately extract watermark, NC=0.94.

The anti-translation transformation experimental data of table 8 watermark

(4) shearing attack

Table 9 is the anti-shearing attack experimental data of watermark, can see from table, and when shearing from Z-direction, shearing displacement is 20% o'clock, still can extract watermark, and NC=0.88 illustrates that this watermarking algorithm has stronger anti-shearing attacking ability.

Figure 49 is after shearing 20% by Z-direction, first sectioning image;

Figure 50 shears the corresponding three-dimensional imaging in 20% back by Z-direction, can find that the effect of shearing attack is obvious; The three-dimensional imaging of the former relatively figure in top has cut one.

Figure 51 is the watermark of extracting, and can accurately must extract watermark, NC=0.88.

The anti-shearing attack experimental data of table 9 watermark

The Z axle is sheared (%)	2	4	6	8	10	12	14	16	18	20
											NC	1.00	1.00	0.94	0.94	0.94	0.94	0.94	0.94	0.88	0.88

(5) distortion is attacked

Table 10 is the anti-twist attack experimental data of watermark, and the distortion parameter is the distortion factor, and the distortion factor is more big, the frequency of expression distortion is more high, and when the distortion factor was 24, at this moment the signal to noise ratio (S/N ratio) of volume data was hanged down PSNR=9.68dB, but at this moment NC=0.61 still can extract watermark.And from table 10, find, bigger to the low frequency characteristic influence of volume data when the distortion factor is low, so the NC value is less; And when the distortion factor is big, bigger to the high frequency characteristics influence of volume data, namely less to the exterior contour influence of volume data, so the NC value is bigger; Data in the table are consistent to the analysis of the medium and low frequency coefficient of volume data in front with us.

Figure 52 is the sectioning image (the distortion factor is 13) after distortion is attacked;

Figure 53 is that the corresponding volume data three-dimensional imaging in back is attacked in distortion, PSNR=9.83dB, and signal to noise ratio (S/N ratio) is lower;

Figure 54 is the watermark of extracting, and NC=0.62 can extract watermark comparatively exactly.

The anti-twist attack experimental data of table 10 watermark

The distortion frequency factor	5	7	9	11	13	17	20	22	24
										PSNR(dB)	10.16	9.89	9.58	9.44	9.83	9.86	9.68	9.78	9.68
NC	0.50	0.43	0.45	0.68	0.62	0.55	0.58	0.50	0.61

Claims (1)

1. volume data watermark implementing method based on three-dimensional DCT and chaos scramble, it is characterized in that: based on the three-dimensional dct transform of the overall situation, obtain the proper vector of the resist geometric attacks of medical volume data, and chaos Chaotic Technology and digital watermark combined, resist geometric attacks and the conventional attack of medical volume data digital watermarking have been realized, this volume data digital watermarking implementation method is divided into four parts, amounts to eight steps:

First is the chaos scramble of watermark: utilize Logistic Map character to produce chaos sequence scramble carried out in watermark, obtain the chaos scramble watermark BW (i, j);

1) by logic initial value x ₀Generate chaos sequence X (j) by Logistic Map;

2) value among the chaos sequence X (j) is arranged according to from small to large order, according to each value ordering front-back direction variation among the X (j) scramble is carried out in watermark locations of pixels space again, obtain the chaos scramble watermark BW (i, j);

Second portion is the embedding of watermark: by the embedding operation to watermark, obtain corresponding two-valued function sequence Key (i, j);

3) the initial body data are carried out overall three-dimensional dct transform, in the DCT coefficient, obtain the vectorial V (j) of the resist geometric attacks of this volume data according to the symbol sebolic addressing of Low Medium Frequency coefficient;

4) utilize Hash function character and chaos scramble watermark BW (i, j), obtain a two-valued function sequence Key (i, j), $\mathrm{Key}(i,j)=V\left(j\right)\⊕\mathrm{BW}(i,j);$

(i j), will use when extracting watermark below, by (i j) applies for to the third party as key, to obtain the entitlement to the primitive medicine volume data Key to preserve Key;

Third part is the extraction of watermark: by two-valued function sequence Key (i, j) and the proper vector V ' of the resist geometric attacks of volume data to be measured (j), extract watermark BW ' (i, j);

5) volume data to be measured is carried out overall three-dimensional dct transform, in the DCT coefficient, the visual feature vector V ' that goes out volume data to be measured according to the symbol extraction of Low Medium Frequency coefficient (j);

6) (i j), extracts watermark with being present in third-party Key to utilize Hash function character ${\mathrm{BW}}^{,}(i,j)=\mathrm{Key}(i,j)\⊕V^{,}\left(j\right);$

The 4th part is the reduction of watermark: utilize Logistic Map character to obtain chaos sequence X (j), the reduction watermark;

7) by logic initial value x ₀Generate chaos sequence X (j);

8) to ascending ordering of value among the chaos sequence X (j), change according to each value ordering front-back direction among the X (j), the watermark locations of pixels space of extracting is reduced, the watermark W ' that obtains reducing (i, j);

With W (i, j) and W ' (i j) carries out normalized correlation coefficient and calculates, and determines the entitlement of medical volume data.

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