CN103279920A - Volume data watermark realizing method based on three-dimension DFT and chaotic scrambling - Google Patents

Abstract

The invention discloses a volume data watermark realizing method based on three-dimension DFT 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-DFT 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-DFT 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 three-dimension DFT and 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 DFT and chaos scramble

Technical field

The present invention relates to a kind of based on three-dimensional DFT(discrete Fourier transformation) and the digital watermark technology of the volume data characteristics of image of chaos scramble, be a kind of multimedia data protection method, belong to field of multimedia signal processing.

Technical background

Utilize the internet can realize distance medical diagnosis, but during the medical imaging by the internet transmission patient, 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, in the digital watermarking research field, for how the Study on Problems of embed watermark is less in volume data, and volume data exists in medical image in a large number, as: the volume data that CT, MRI image all are made up of section, therefore research how in volume data embed digital watermark significant, and generally be not allow to revise its content for medical volume data.In a word, bigger for embed watermark difficulty in three-dimensional data, have not yet to see report, still belong to blank.

Summary of the invention

The objective of the invention is to propose a kind of based on three-dimensional DFT conversion and Logistic Map chaos scramble, the method for embed watermark in volume data.This watermarking algorithm has stronger robustness, and can resist geometric attack and can resist conventional attack again, and the embedding of watermark do not influence the content of initial body data, be a kind of Zero watermarking method, better must 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-DFT conversion, in the 3D-DFT 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; (volume data is made the as a whole Fourier transform of carrying out here based on the three-dimensional DFT conversion of the overall situation, rather than be divided into little stereo block and carry out three-dimensional DFT conversion), in three-dimensional DFT conversion 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 the chaos scramble of watermark, embedding, extraction and reduction four major parts of watermark, and first is the chaos scramble of watermark, comprising: (1) produces chaos sequence X (j) by Logistic Map; (2) according to X (j) scramble is carried out in watermark, obtain the chaos scramble watermark BW (i, j); Second portion is the embedding of watermark, comprising: (3) obtain the proper vector V (j) of a resist geometric attacks by volume data being carried out overall three-dimensional DFT conversion; (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), (i j) is present in the third party with two-valued function sequence Key then; Third part is the extraction of watermark, comprise: (5) obtain volume data to be measured resist geometric attacks proper vector V ' (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) are used Logistic Map and are obtained the chaos sequence X (j) identical with step (1), 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 from small to large 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 DFT conversion, obtain the proper vector V (j) of a resist geometric attacks of this volume data;

Earlier to initial body data F (i, j, k) carry out overall three-dimensional DFT conversion, obtain three-dimensional DFT matrix of coefficients FF (i, j, k), again from three-dimensional DFT matrix of coefficients FF (i, j, k) L Low Medium Frequency coefficient value before middle the taking-up, and by three-dimensional DFT coefficient being carried out the proper vector V (j) that symbolic operation obtains this volume data, because Fourier coefficient be plural number, for the purpose of making things convenient for, here with real part, imaginary part as two numbers, when the real part of Fourier coefficient or imaginary part coefficient value be on the occasion of or during null value we with " 1 " expression, coefficient during for negative value with " 0 " expression.Process prescription is as follows:

FF(i,j,k)=DFT3(F(i,j,k))

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

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

Key(i,j)=V(j)⊕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), through obtaining the method that three-dimensional DFT matrix of coefficients is FF ' (i, j k), press above-mentioned steps 3) after the overall three-dimensional DFT conversion, try to achieve the proper vector V ' of volume data to be measured (j) to volume data to be measured to F ';

FF’(i,j,k)=DFT3(F’(i,j,k))

V’(j)=Sign(FF’(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)

BW’(i,j)=Key(i,j)⊕V’(j)

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 the value among the chaos sequence X (j) according to sorting from small to large, 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 has following advantage:

At first, because the present invention is based on the digital watermark technology of three-dimensional DFT conversion, 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 content of initial body data, is a kind of zero digital watermark, can better must protect medical volume data.This characteristic especially has bigger 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 experimental data:

1) 3 d-dem Fourier transform

Function f (3 d-dem Fourier direct transform formula z) is for x, y:

$F(u,v,w)=\underset{x=0}{\overset{M-1}{\mathrm{\Σ}}}\underset{y=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{z=0}{\overset{P-1}{\mathrm{\Σ}}}f(x,y,z)\·e^{-j2\mathrm{\πxu}/M}{e}^{-j2\mathrm{\πyv}/N}{e}^{-j2\mathrm{\πzw}/P}$

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

The 3 d-dem Fourier inversion formula is:

$f(x,y,z)=\frac{1}{\mathrm{MNP}}\underset{u=0}{\overset{M-1}{\mathrm{\Σ}}}\underset{v=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{w=0}{\overset{P-1}{\mathrm{\Σ}}}F(u,v,w)\·e^{j2\mathrm{\πxu}/M}{e}^{j2\mathrm{\πyv}/N}{e}^{j2\mathrm{\πzw}/P}$

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

In Fourier's positive inverse transform, (x, y z) are three dimensions territory function to f, and (u, v w) are corresponding frequency-domain function to F.

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) the proper vector choosing method of the resist geometric attacks of volume data

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 digital watermarking that will embed and this proper vector then, just can solve the robustness problem of watermark preferably.By being observed, the overall DFT 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, the Low Medium Frequency of three-dimensional DFT is real, some variations may take place the size of imaginary part coefficient value, but its coefficient symbols remains unchanged substantially, and we specify by the experimental data of table 1.What be used as test in the table 1 is that Fig. 1 is seen in the section (getting the tenth here) of volume data, " 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 the corresponding three-dimensional imaging of section." the 2nd row " of table 1 arrive " the 7th row ", are that F (1,1, the 1)-F (1,2,3) that gets in the three-dimensional Fourier coefficient matrix is total to 6x2=12 Low Medium Frequency coefficient (plural number being regarded as real part and two coefficients of imaginary part here).For conventional attack and geometric attack, these Low Medium Frequency coefficient values have bigger variation, but we can find that its symbol does not change substantially.We with Fourier coefficient (plural number is regarded real part and two coefficient values of imaginary part as here) on the occasion of and small incidental expenses " 1 " expression, negative value is represented with " 0 ", so for the primitive medicine volume data, F (1 in the three-dimensional Fourier coefficient matrix, 1,1)-F (1,2,3) coefficient, corresponding coefficient symbols sequence is: " 110000110011 " see Table 1 the 8th row, observing these row can find, no matter conventional attack still is this symbol sebolic addressing of geometric attack keeps similar with the initial body data, and coefficient symbols sequence and initial body data normalization related coefficient are all bigger, see the 9th row (having got 12 coefficient symbols here for the purpose of convenient).

In order to verify that further the proper vector that said method extracts 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 DFT conversion, obtain corresponding Fourier coefficient F (1,1,1)-F (2,2,4), get preceding 16 FFT coefficients (plural number is regarded real part, two coefficients of imaginary part as) here and formed proper vector V (j).And obtain the direct related coefficient of different volume datas, result of calculation is as shown in table 2.

As can be seen from Table 2, at first, the related coefficient maximum between the same volume data self is 1.00; Secondly, the related coefficient between Figure 23 and Figure 24 also more greatly 0.32, and these two figure two similar liver volume datas that are shapes; Facies relationship numerical value between other volume data proper vector is less, this with our eye-observation to be consistent, the volume data proper vector that this explanation adopts this method to extract, basically the better main appearance profile feature that must reflect volume data, this is also consistent with " in two-dimentional DFT, the Low Medium Frequency coefficient mainly reflects the profile of image ".

The low frequency part coefficient of the three-dimensional DFT conversion of the table 1 volume data overall situation and be subjected to different the attack after changing value

* the 1.0e+006 of DFT conversion coefficient unit

The related coefficient of the different volume data proper vectors of table 2 (vector length 32bit)

?	Va	Vb	Vc	Vd	Ve	Vf	Vg	Vh
									Va	1.00	-0.14	-0.08	0.19	-0.33	-0.47	-0.14	0.12
Vb	-0.14	1.00	0.25	-0.44	0.16	-0.14	0.18	-0.22
									Vc	-0.08	0.25	1.00	0.16	-0.30	0.05	-0.29	-0.16
Vd	0.19	-0.44	0.16	1.00	-0.16	0.05	-0.16	-0.02
									Ve	-0.33	0.16	-0.30	-0.16	1.00	0.08	0.20	-0.10
Vf	-0.47	-0.14	0.05	0.05	0.08	1.00	-0.01	-0.14
									Vg	-0.14	0.18	-0.29	-0.16	0.20	-0.01	1.00	0.32
Vh	0.12	-0.22	-0.16	-0.02	-0.10	-0.14	0.32	1.00

In sum, we pass through the analysis to the overall three-dimensional DFT coefficient of volume data, utilize the symbol sebolic addressing of three-dimensional DFT Low Medium Frequency coefficient to obtain the method for the proper vector of a resist geometric attacks of obtaining volume data, utilize this proper vector, Hash function and " third party " concept to realize the method for embed watermark in volume data.Experiment showed, that this method has realized the embedding of watermark, and the embedding of watermark do not influence the content of initial body data, robustness is preferably arranged.

Description of drawings

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

Fig. 2 is to be sectioning image after 3% the Gaussian noise through intensity.

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 [5x5]).

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

Fig. 6 is that to be subjected to intensity be that 3% 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 [5x5], and filter times is 10 times).

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

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

Figure 11 is that vertical direction moves down 8% 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 8% 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 volume data 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 3%) after Gaussian noise is disturbed.

Figure 31 is the three-dimensional reconstruction figure (Gaussian noise intensity 3%) after Gaussian noise is disturbed.

Figure 32 is the watermark (Gaussian noise intensity 3%) that Gaussian noise disturbs the back to extract.

Figure 33 is the sectioning image (the compression quality parameter is 4%) after the JPEG compression.

Figure 34 is the volume data three-dimensional imaging (the compression quality parameter is 4%) after the JPEG compression.

Figure 35 is the watermark (the compression quality parameter is 4%) 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 10 degree.

Figure 40 is the three-dimensional imaging of up time rotation 10 degree back volume datas.

Figure 41 is the watermark that extract up time rotation 10 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 10% sectioning image.

Figure 47 is the three-dimensional imaging that vertically moves down 10% volume data correspondence.

Figure 48 vertically moves down the watermark that extract 10% 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.

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 128 * 128 * 27, sees Fig. 5.The initial body data be expressed as F (i, j, k), 1≤i wherein, j≤128; 1≤k≤27, corresponding 3D-DFT matrix of coefficients be FF (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 by concrete experiment and anti-conventional attack ability and the resist geometric attacks ability of this digital watermark method by each section is attacked, realize the attack to volume data in the 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.Data can be seen from table, when Gaussian noise intensity up to 25% the time, the PSNR of watermark volume data is down to 0.11dB, the watermark of extraction, related coefficient NC=0.70 can accurately must extract watermark.Therefore, this water mark method has anti-Gaussian noise interference performance preferably.

Figure 30 is that Gaussian noise intensity is 3% o'clock watermark section, and is visually very fuzzy;

Figure 31 is that Gaussian noise intensity is 3% o'clock volume data three-dimensional imaging, and volume data is visually very fuzzy, and PSNR=8.03dB is lower;

Figure 32 is the watermark of extracting, and NC=0.93 can accurately extract watermark.

The anti-Gaussian noise interference experiment of table 3 watermark data

Noise intensity (%)	3	5	10	15	20	25
							PSNR(dB)	8.03	6.03	3.32	1.79	0.80	0.11
NC	0.93	0.93	0.88	0.84	0.71	0.70

(2) the JPEG compression is handled

Adopt image compression quality percentage as parameter watermarking images to be carried out the JPEG compression;

Table 4 is experimental datas of the anti-JPEG compression of watermark.When compression quality only was 2%, compression quality was lower, PSNR=16.57dB, and at this moment NC=0.73 still can accurately must extract watermark.

Figure 33 is that compression quality is 4% sectioning image, and blocking artifact has appearred in this figure;

Figure 34 is that compression quality is the three-dimensional imaging of 4% volume data correspondence, and three-dimensional blocking artifact has appearred in volume data;

Figure 35 is the watermark of extracting, and NC=0.71 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	0.73	0.71	0.77	0.77	0.77	0.77	0.85	0.93

(3) medium filtering is handled

Table 5 is the anti-medium filtering experimental datas of watermark, from table data as can be seen, when the medium filtering parameter is [5x5], the filtering multiplicity is 20 o'clock, PSNR=18.07dB is lower; NC=0.94 still can accurately extract watermark.

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 PSNR=18.69dB is worth lowlyer, and at this moment profile such as ear is not too clearly demarcated;

Figure 38 is the watermark of extracting, and NC=0.94 can accurately extract watermark.

The anti-medium filtering experimental data of table 5 watermark

Watermarking algorithm resist geometric attacks ability

(1) rotational transform

Table 6 is that experimental data is attacked in the anti-rotation of watermark.From table, can see when watermark volume data up time rotation 20 is spent, still can extract watermark, at this moment NC=0.62.And the resist geometric attacks ability of the watermarking algorithm that Y.H.WU provides is relatively poor, and when rotation only is 1.50 when spending, the value of normalized correlation coefficient is just very low, and NC=0.24 can't accurately extract watermark.

Figure 39 is the watermark section of up time rotation 10 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 very low, PSNR=13.97dB;

Figure 41 is the watermark of extracting, and NC=0.80 can accurately must extract watermark.

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

The up time rotation	3 degree	5 degree	8 degree	10 degree	13 degree	15 degree	18 degree	20 degree
									PSNR(dB)	18.94	16.54	14.68	13.97	13.28	12.98	12.63	12.44
NC	0.94	0.80	0.80	0.80	0.80	0.69	0.69	0.62

(2) scale transformation

Table 7 is the nonshrink attack experimental datas of putting of watermark volume data, as can be seen from Table 7 when watermark volume data zoom factor little to 0.3 the time, NC=0.53 still can 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 that the watermark of extraction had NC=0.79 after convergent-divergent was attacked.

The nonshrink attack experimental data of putting of table 7 watermark

Zoom factor	0.3	0.5	0.8	1.0	1.2	2.0	4.0
								NC	0.53	0.79	0.85	1.00	0.92	0.85	0.79

(3) translation transformation

Table 8 is that experimental data is attacked in the anti-translation of watermark.Data learn that NC=0.67 still can accurately extract watermark, so this digital watermarking has stronger anti-translation transformation ability when horizontal left 10% from table.

Figure 46 is the sectioning image of volume data horizontal left 5%;

Figure 47 is each section horizontal left 5% of volume data, corresponding volume data three-dimensional imaging.At this moment, PSNR=11.14dB, signal to noise ratio (S/N ratio) is lower;

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

Experimental data is attacked in the anti-translation of table 8 watermark

Horizontal left (%)	4	6	8	10	12	14
							PSNR(dB)	11.38	10.90	10.21	9.80	9.27	8.94
NC	0.94	0.94	0.76	0.67	0.67	0.54

(4) shearing attack

Table 9 is the anti-shearing attack experimental datas of watermark, can see, when shearing 20% from Z-direction, still can extract watermark, and NC=0.93 is arranged, and 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 volume data three-dimensional imaging in 20% back by Z-direction, can find that the effect of shearing attack is obvious, and the three-dimensional imaging of the former relatively figure in top has cut very big one;

Figure 51 is the watermark of extracting, and NC=0.93 can accurately extract watermark.

The anti-shearing attack experimental data of table 9 watermark

The Z axle is sheared (%)	4	6	8	10	14	16	18	20
									NC	0.85	0.85	0.85	0.85	0.93	0.93	0.93	0.93

Claims (1)

1. volume data watermark implementing method based on three-dimensional DFT and chaos scramble, it is characterized in that: based on the three-dimensional DFT conversion 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 the locus of watermark pixel 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 DFT conversion, in Fourier 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), Key (i, j)=V (j) ⊕ 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 DFT conversion, in Fourier 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) utilize Hash function character and be present in third-party Key (i j), extracts watermark: BW ' (i, j)=Key (i, j) ⊕ V ' (j);

The 4th part is the reduction of watermark: utilize the character of Logistic Map 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 locus of the watermark pixel extracted 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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