CN102360486A - Medical-image robust multiple-watermark method based on DWT (Discrete Wavelet Transform) and DCT (Discrete Cosine Transform) - Google Patents

Abstract

The invention relates to a medical-image robust multiple-watermark method based on DWT (Discrete Wavelet Transform) and DCT (Discrete Cosine Transform). The method comprises the following steps of: firstly, carrying out embedding of multiple watermarks, wherein the step comprises (1) carrying out DWT on the original medical image, then carrying out whole-image DCT on approximation subimages, and extracting a vector which can represent the important visual feature of the original image in conversion coefficients; (2) utilizing the feature vector and the multiple watermarks to be embedded to obtain a corresponding two-valued logic sequence by virtue of a Hash function, storing the two-valued sequence in a third party and then carrying out extraction of multiple watermarks; (3) carrying out DWT on the medical image to be detected, then carrying out DCT on the approximation subimages, and finding a visual feature vector of the image to be detected; and (4) utilizing the property of the Hash function and the two-valued logic sequence stored in the third party to extract the multiple watermarks. The medical-image robust multiple-watermark method can be used for solving the problems of the geometric attack resistance and conventional attack resistance occurring in the application of the medical image so as to protect the secrecy of information of patients.

Description

A kind of many water mark methods of medical image robust based on DWT and DCT

Technical field

The invention belongs to field of multimedia signal processing, relate to the multiple digital watermark technology of medical image of a kind of wavelet transformation (DWT), discrete cosine transform (DCT) and Image Visual Feature, specifically is a kind of many water mark methods of medical image robust based on DWT and DCT.

Background technology

In recent years, along with developing rapidly of computer science and technology and multimedia communication technology, the digital content management system has brought into play more and more important effect in the medical system in modern times.Progress along with the software and hardware condition; The appearance of particularly high-resolution computerized layer scanning technology (CT) and mr imaging technique (MRI) and advanced person's new equipments such as optical scanner and the exploitation of a series of related softwares, the effect of medical image has had qualitative leap.Along with applying of internet, tele-medicine, remote diagnosis are universal day by day, and the information security issue of the medical image of transmission Network Based comes out gradually.Personal information on medical image is leaked easily; How to solve this difficult problem? Utilize the invisibility and the robustness of digital watermarking can solve this difficult problem preferably, promptly be embedded in the patient's on the medical image personal information in the medical image as digital watermarking.

The research to medical image digital watermarking field at present mainly concentrates on spatial domain and two aspects of transform domain (DCT, DFT and DWT), they respectively the value of some coefficients of gray scale or the transform domain of some pixel through the change spatial domain come embed watermark.At present; Wavelet transformation (DWT); It is the core of Image Compression JPEG2000 of future generation; Many based on the digital watermarking of wavelet transformation research at present, and DCT is the core of the most popular compression of images JPEG now, is one of focuses of frequency field Study of Watermarking now with both combinations.

In view of singularity requirement to medical image focal zone protection, general medical image digital watermark method is normal select with watermark information be embedded into medical image non-area-of-interest (Region of non-interest, RONI).Area-of-interest in the medical image (Region of interest ROI) refers to the focal zone that those comprise important pathological characters or diagnosis and treatment information, if in this zone embed watermark, the diagnosis that then might make the mistake.But often people will spend long time and energy when seeking ROI, and in case select wrongly, then might disturb doctor's diagnosis.

In addition, in medical digital watermark research field, the multiple medical image digital watermarking based on the DWT resist geometric attacks up to now is still a more insoluble problem; Do not see public reported at present as yet; Still belong to blank, and in the practical application, medical image often receives geometric attack.

Summary of the invention

The purpose of this invention is to provide a kind of many water mark methods of medical image robust based on DWT and DCT; Visual feature vector, encryption technology and third-party notion through with medical image combine; Need not carry out choosing of area-of-interest; Thereby solved agility problem and capacity limit property problem that many watermarks embed, extract, had very desirable robustness and invisibility, solved the multiple digital watermarking problem of medical image effectively; The resistance geometric attack and resistance conventional attack problem that occur in the medical image applications have been solved simultaneously, with the crypticity of protection sufferer information.

To achieve these goals; The present invention is performed such: earlier medical image is carried out wavelet transformation, obtain " approximation coefficient " and " detail coefficients ", and can know according to the small echo theory; " approximation coefficient " represented the low frequency characteristic of medical image, reflection be the main profile of medical image; " detail coefficients " represent medical image high frequency characteristics reflection be the detailed information of medical image.Because the resist geometric attacks ability of wavelet transformation itself is relatively poor, for this reason, we carry out wavelet transformation (DWT) to medical image earlier; And then to the reflection low frequency characteristic " approximation coefficient " carry out overall cosine transform (DCT) again; In the DCT coefficient, extract the proper vector of a resist geometric attacks, and the Hash function in digital watermark and the cryptography and " third party's notion " are combined; Realized based on wavelet transformation the embedding of resist geometric attacks large capacity digital watermark.The method that the present invention adopted comprises watermark embedding and watermark extracting two large divisions; First is the multi-watermarking embedding grammar; Comprise: (1) through medical image is carried out wavelet transformation, the pairing approximation coefficient carries out overall dct transform then, obtains a visual feature vector V (j) of image; (2) according to the multi-watermarking W that will embed ^k(j), k=1,2 ..., n; The proper vector V (j) that n representes the watermark number that embeds and in medical image, extracts through the Hash functional operation, generates two-valued function sequence Key ^k(j), then with two-valued function sequence Key ^k(j) there is the third party.Second portion is the multi-watermarking method for distilling, comprising: the visual feature vector V ' that testing image is obtained in (3) (j); (4) utilize there to be third-party two-valued function sequence Key ^k(j) and the proper vector V ' of medical image to be measured (j), extract multi-watermarking W ^k' (j).

Method of the present invention is elaborated as follows at present:

At first use W ^k(j) indicate a plurality of medical experts' electronic signature, the multi-watermarking that promptly will embed, W ^k(j)={ w ^k(j) | w (j)=0,1; 1≤j≤L, 1≤k≤n}, the watermark length that the L representative will embed, n is the number of embed watermark.Original image be designated as F={f (i, j) | f (i, j) ∈ R; 1≤i≤N1,1≤j≤N2) }, wherein, (i, j) grey scale pixel value of expression primitive medicine image is established N1=N2=N to f.

First: watermark embedding method

1) through the primitive medicine image is carried out wavelet transformation, " approximation coefficient " to wavelet transformation carries out overall dct transform more then, in the Low Medium Frequency coefficient of DCT, obtains the proper vector V (j) of a resist geometric attacks of this medical image.

(i j) carries out the DWT wavelet transformation, obtains matrix of coefficients ca_cd (i to former figure F earlier; J), (i j) carries out overall dct transform to wherein " approximation coefficient " ca again;, obtain DCT matrix of coefficients DF (i, j); (i obtains frequency DCT coefficient sequence from low to high in Low Medium Frequency coefficient j), L value before getting from DCT matrix of coefficients DF again; And obtain this visual feature of image vector V (j) through the computing of DCT coefficient symbols, convenient for the purpose of, a plural number is regarded real part, two coefficients of imaginary part (imaginary part is only seen coefficient) as here; We are with " 1 " expression (containing the situation of coefficient value for " 0 ") when " just " when coefficient value, and with " 0 " expression, main process prescription is following when negative for coefficient:

ca_cd(i，j)＝DWT2(F(i，j))

DF(i，j)＝DCT2(ca(i，j))

V(j)＝-Sign(DF(i，j))

2) according to watermark W ^k(j) and visual feature of image vector V (j) generate a two-valued function sequence Key ^k(j).

${\mathrm{Key}}^k\left(j\right)=V\left(j\right)\⊕W^k\left(j\right);k=\mathrm{1,2},...,n$

Key ^k(j) be by visual feature of image vector V (j) and watermark W ^k(j), generate through cryptography Hash function commonly used.Preserve Key ^kNeed use when (j), extracting watermark afterwards.Through with Key ^k(j) apply for to the third party as key,, thereby reach the purpose of protecting medical image with the entitlement of acquisition original image.

Second portion: watermark extracting method

3) the visual feature vector V ' that obtains medical image to be measured (j).

If testing image be F ' (i, j), through wavelet transformation (DWT), the overall Fourier transform (DCT) of again its approximation coefficient being carried out, obtain the DCT matrix of coefficients and be DF ' (i, j), by above-mentioned Step1 method, the visual feature vector V ' that tries to achieve testing image (j);

ca_cd’(i，j)＝DWT2(F’(i，j))

DF’(i，j)＝DCT2(ca’(i，j))

V’(j)＝-Sign(DF’(i，j))

4) in testing image, extract watermark W ^k' (j).

According to the Key that generates when the embed watermark ^k(j) and the visual feature vector V ' of testing image (j), utilize Hash character can extract the watermark W of testing image ^k' (j).

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

Again according to W ^k(j) and W ^k' (j) degree of correlation differentiates the entitlement of testing image and patient's personal information.

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

Because the present invention is based on the digital watermark technology of DWT and dct transform, DWT is the core of Image Compression JPEG2000 of future generation, and DCT is the core of present the most popular compression of images JPEG; Therefore; This algorithm the present and the future's compressed software all has preferably compatible, and the embedding of multi-watermarking and extraction be in frequency domain, to carry out, and the experimental data through the back confirms; This watermark not only has stronger anti-conventional attack ability, and stronger resist geometric attacks ability is arranged; Do not need artificial the choosing of area-of-interest of carrying out, thereby solved the agility problem that multi-watermarking embeds; The multi-watermarking that embeds is a kind of zero watermark, does not influence primitive medicine picture quality, aspect medical, have very high practical value, and this algorithm is applicable to other field; Utilize third-party notion, adapted to the practicability and the standardization of the network promotion now; The watermark number of inserting does not simultaneously receive the restriction of capacity.

Below from the explanation of theoretical foundation and test figure:

1) wavelet transform (DWT)

The wavelet transformation (DWT) that S.Mallat proposed in 1988 is a new signal analysis theory of rise in recent years, its " time one frequently " analytical approach that is a kind of, and its basic thought is with wavelet function Ψ _{A, b}(t) be substrate, signal f (t) is decomposed.

$W_{f(a,b)}={\∫}_{R}f\left(t\right)\stackrel{\‾}{{\mathrm{\ψ}}_{a,b}\left(t\right)}\mathrm{dt}$

Wavelet function ψ wherein _{A, b}(t) be to go out through translation, flexible and one group of function obtaining by same basis function Ψ (t).

Ψ _a，b(t)＝|a| ^-1/2Ψ((t-b)/a)a， b∈R， a≠0

Ψ (t) is called basic small echo, and a is a contraction-expansion factor, and b is a shift factor.

Mallat algorithm decomposition formula is:

$c_{j+1,k}=\underset{n\∈z}{\mathrm{\Σ}}{c}_{j,n}{\stackrel{\‾}{h}}_{n-2k},k\∈z$

$d_{j+1,k}=\underset{n\∈z}{\mathrm{\Σ}}{c}_{j,n}{\stackrel{\‾}{g}}_{n-2k},k\∈z$

Mallat algorithm reconstruction formula is:

$c_{j,k}=\underset{n\∈z}{\mathrm{\Σ}}{c}_{j+1,n}{h}_{k-2n}+\underset{n\∈z}{\mathrm{\Σ}}{d}_{j+1,n}{g}_{k-2n},k\∈z$

After the 2D signal image carried out the one-level wavelet decomposition; Former figure is divided into four sub-graphs, and wherein three high frequency details subloops (level, vertical and diagonal) and a low frequency ll channel are in the low frequency ll channel; The essential information that has comprised image; Receive external action little, therefore be added in watermark in the ll channel, good robustness is arranged.

2) discrete cosine transform

2-D discrete cosine direct transform (DCT) formula is following:

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

u＝0，1，Λ，M-1； v＝0，1，Λ，N-1；

In the formula

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

2-D discrete cosine inverse transformation (IDCT) formula is following:

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

x＝0，1，Λ，M-1； y＝0，1，Λ，N-1

X wherein, y is the spatial domain sampled value; U, v are the frequency field sampled value, and digital picture is represented with the pixel square formation usually, i.e. M=N

Can know that from top formula the coefficient symbols of DCT is relevant with the phase place of component.

3) choosing method of medical image vision principal character vector

The main cause of present most of medical image watermarking algorithm resist geometric attacks ability is: people are embedded in digital watermarking in pixel or the conversion coefficient, and the slight geometric transformation of medical image usually causes the bigger variation of having of pixel value or transform coefficient values.The watermark that is embedded in like this in the medical image is just attacked easily.If can find the proper vector of a reflection medical image geometrical feature; And when little geometric transformation takes place in medical image; Tangible sudden change can not take place in this proper vector value; And be associated the multi-watermarking that will embed and this proper vector, just can solve the robustness problem of watermark preferably.The ability of the resistance geometric attack of wavelet transformation is relatively poor, through experimental data, finds the wavelet transformation and the cosine transform of medical image are combined; Can find the proper vector of a resist geometric attacks; When a medical image was carried out common geometric transformation, some variations possibly take place in the size of DCT Low Medium Frequency coefficient value, but its coefficient symbols remains unchanged basically; According to so rule of finding; We carry out wavelet transformation (selecting one deck here for use) to medical image earlier, then its approximation coefficient are carried out overall dct transform again, and we explain through some experimental datas of table 1.The primitive medicine image that is used as test in the table 1 is Fig. 1 (a), is the sectioning image (128x128) of a width of cloth brain.What the 1st row showed in the table is medical image type under attack, and the medical image that receives behind the conventional attack is seen Fig. 1 (b)-(d), and the medical image that receives behind the geometric attack is seen Fig. 2 (a)-(d).The 3rd is listed as the 11st row, and this is DF (1,1)-DF (3,3) of in the DWT-DCT matrix of coefficients, getting totally 9 Low Medium Frequency coefficients.Wherein coefficient DF (1,1) representes the DC component value of medical image.For conventional attack, these Low Medium Frequency coefficient values F (1,1)-F (3,3) remains unchanged and primitive medicine image value approximately equal basically; For geometric attack, the part coefficient has bigger variation, but we can find that medical image is when receiving geometric attack, and the size of part DWT-DCT Low Medium Frequency coefficient has taken place to change but its symbol does not change basically.We with the DWT-DCT coefficient on the occasion of and small incidental expenses " 1 " expression, negative value with " 0 " expression, so for the primitive medicine image; F in the DWT-DCT matrix of coefficients (1,1)-F (3,3) coefficient; Corresponding coefficient symbols sequence is: " 1,100 01001 "; See the 12nd row of table 1, we observe these row and can find, no matter conventional attack still is this symbol sebolic addressing of geometric attack keeps similar with the primitive medicine image energy; With the normalized correlation coefficient all big (seeing the 13rd row) of primitive medicine image, (having got 9 DWT-DCT coefficient symbols here for the purpose of convenient).

Table 1 medical image full figure DWT-DCT conversion Low Medium Frequency part coefficient and receive different the attack after changing value

^*The 1.0e+002 of dct transform coefficient unit

In order to prove that further the proper vector of extracting as stated above is a key character of this medical image, we see Fig. 3 (a)-(h) again different test patterns; Carry out full figure DWT-DCT conversion according to the method described above; Obtain corresponding DWT-DCT coefficient F (1,1)-F (4,8); And obtain the related coefficient with the symbol sebolic addressing of former figure, result of calculation is as shown in table 2.

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

	Pa	Pb	Pc	Pd	Pe	Pf	Pg	Ph
									Pa	1.00	0.34	0.00	0.31	-0.17	-0.24	0.18	0.38
Pb	0.34	1.00	0.32	0.01	-0.04	0.30	-0.25	0.32
									Pc	0.00	0.32	1.00	-0.19	0.19	0.25	0.31	0.00
Pd	0.31	0.01	-0.19	1.00	-0.01	-0.05	0.00	0.31
									Pe	-0.17	-0.04	0.19	-0.01	1.00	-0.09	0.01	0.06
Pf	-0.24	0.30	0.25	-0.05	-0.09	1.00	-0.18	-0.13
									Pg	0.18	-0.25	0.31	0.00	0.01	-0.18	1.00	-0.06
Ph	0.38	0.32	0.00	0.31	0.06	-0.13	-0.06	1.00

Can find out that from table 2 between the different medical images, it is bigger that symbol sebolic addressing differs, the degree of correlation is less, less than 0.5.

This explains that more the symbol sebolic addressing of DWT-DCT coefficient can reflect the main visual signature of this medical image.After watermarking images received conventional attack and geometric attack to a certain degree, this vector was constant basically, and this also meets the DWT-DCT ability that " very strong extraction characteristics of image arranged ".

In sum; Through analysis to the overall DWT-DCT coefficient of medical image; Utilize the symbol sebolic addressing of DWT-DCT Low Medium Frequency coefficient to obtain a kind of method of proper vector of a resist geometric attacks obtaining medical image, utilize this proper vector and Hash function, " third party " notion to realize in medical image, embedding the method for many watermarks.Through experiment showed, that this method has realized the embedding of many watermarks, and the embedding of watermark do not influence the content of medical image, and robustness is preferably arranged.

Description of drawings

Fig. 1 (a) is the primitive medicine image.

Fig. 1 (b) is the image that disturbs through Gauss.

Fig. 1 (c) is the image of attacking through JPEG.

Fig. 1 (d) is the image through medium filtering.

Fig. 2 (a) is the image through rotational transform.

Fig. 2 (b) is the image through convergent-divergent 2.0.

Fig. 2 (c) is the image through convergent-divergent 0.5.

Fig. 2 (d) is the image through vertical moving.

Fig. 3 (a) is standardized test chart MRI_1.

Fig. 3 (b) is standardized test chart MRI_2.

Fig. 3 (c) is standardized test chart MRI_3.

Fig. 3 (d) is standardized test chart Engine.

Fig. 3 (e) is standardized test chart Head.

Fig. 3 (f) is standardized test chart Teddy bear.

Fig. 3 (g) is standardized test chart Mri_1back1.

Fig. 3 (h) is standardized test chart Mri_1back2.

The watermarking images of Fig. 4 (a) when not disturbing.

The watermark detection of Fig. 4 (b) when not disturbing.

Watermarking images when Fig. 5 (a) has Gauss to disturb (Gauss's interference strength is 3%).

Watermark detection when Fig. 5 (b) has Gauss to disturb.

Watermarking images (compression quality is 4%) after Fig. 6 (a) JPEG compression.

Watermark detection after Fig. 6 (b) JPEG compression.

Watermarking images behind Fig. 7 (a) medium filtering (through 20 filtering of [3x3]).

Watermark detection behind Fig. 7 (b) medium filtering.

Watermarking images behind Fig. 8 (a) rotation 20 degree.

Watermark detection behind Fig. 8 (b) rotation 20 degree.

Fig. 9 (a) zoom factor is 4.0 watermarking images.

Fig. 9 (b) zoom factor is 4.0 image watermark detection.

Figure 10 (a) zoom factor is 0.5 watermarking images.

Figure 10 (b) zoom factor is 0.5 image watermark detection.

Image after Figure 11 (a) vertical moving 8%.

Watermark detection after Figure 11 (b) vertical moving 8%.

Figure 12 (a) shears 10% watermarking images.

Figure 12 (b) shears 10% image watermark detection.

Embodiment

Below in conjunction with accompanying drawing the present invention is described further and uses 1000 groups of independently binary pseudo-random (value is+1 or 0); Every group of sequence length is 32bit; In these 1000 groups of data; Appoint to extract three groups (selecting the 300th group, the 500th group, the 700th group here), as three watermark sequences that embed, promptly we to have embedded total length be the watermark sequence of 32x3=96bit.Testing used primitive medicine image, is the brain three-dimensional imaging behind the width of cloth process CT scan, chooses the image (128x128) of its tenth section and sees Fig. 4 (a).If former figure be expressed as F (i, j), 1≤i≤128,1≤j≤128 wherein; Corresponding full figure DWT-DCT matrix of coefficients is that (i j), selects 32 Low Medium Frequency coefficient Y (j) to DF; 1≤j≤L, the DC component of first value Y (1) representative image, from low to high frequency order is arranged then.Consider the capacity of robustness and disposable embed watermark, we select 4x8=32 coefficient of medium and low frequency to do proper vector, i.e. L=32.The many watermarks W that embeds is by k sub-watermark W ^k(j) form, the number k of this routine neutron watermark gets 3, and this lining watermark is designated as W ^k(j), 1≤j≤32,1≤k≤3; The DWT-DCT matrix of coefficients of choosing be F (i, j), 1≤i≤4,1≤j≤8.Detect W through watermarking algorithm ^k' (j) after, again through calculating W ^k(j) and W ^k' (j) normalized correlation coefficient NC ^k(Normalized Cross Correlation) for the purpose of making things convenient for, representes three related coefficients corresponding with three watermarks that extract with NC1, NC2 and NC3, is used to judge whether that watermark embeds.

Fig. 4 (a) is the watermarking images that does not add when disturbing;

Fig. 4 (b) does not add when disturbing, and the output of watermark detector can be seen NC1=1.00, NC2=1.00, and NC3=1.00 obviously detects the existence of watermark.

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

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.

Fig. 5 (a) is for the watermarking images when Gaussian noise intensity is 3%, and is visually very fuzzy;

The output of Fig. 5 (b) watermark detector can clearly detect the existence of watermark, NC1=0.87, NC2=0.90, NC3=0.88.

Table 3 is the anti-Gauss of watermark detection data when disturbing.Can see from experimental data, when Gaussian noise intensity when being 25%, watermarking images PSNR reduces to 0.13dB; At this moment detect watermark, related coefficient NC1=0.72, NC2=0.68; NC3=0.70 still can detect the existence of watermark. and this explanation adopts this invention that good anti-Gaussian noise ability is arranged.

The anti-Gaussian noise interfering data of table 3 watermark

Noise intensity (%)	1	3	5	10	15	20	25
								PSNR(dB)	12.33	7.87	5.85	3.30	1.80	0.82	0.13
NC1	0.93	0.87	0.81	0.82	0.75	0.69	0.72
								NC2	0.93	0.90	0.86	0.81	0.77	0.68	0.68
NC3	0.95	0.88	0.81	0.82	0.78	0.70	0.70

(2) JPEG processed compressed

Adopt image compression quality percentage watermarking images to be carried out the JPEG compression as parameter; Fig. 6 (a) is that compression quality is 4% image, and blocking artifact has appearred in this figure;

Fig. 6 (b) is the response of watermark detector, NC1=0.82, and NC2=0.82, NC3=0.81, it is obvious to detect effect.

Table 4 is the test figure of the anti-JPEG of watermarking images.When compression quality is very poor, compression quality is 4% o'clock, still can record the existence of watermark.

The experimental data of the anti-JPEG compression of table 4 watermark

Compression quality (%)	4	8	10	20	40	60	80
								PSNR(dB)	17.61	19.99	20.98	23.04	25.06	26.52	29.27
NC1	0.82	0.95	0.63	0.77	1.00	1.00	1.00
								NC2	0.82	0.93	0.63	0.77	1.00	1.00	1.00
NC3	0.81	0.93	0.62	0.78	1.00	1.00	1.00

(3) medium filtering is handled

Table 5 is the anti-medium filtering ability of watermarking images, and it can be seen from the table, when the medium filtering parameter is [7x7], the filtering multiplicity is 20 o'clock, still can record the existence of watermark, NC1=0.69, NC2=0.70, NC3=0.70.

Fig. 7 (a) is that the medium filtering parameter is [3x3], and the filtering multiplicity is 20 medical image, and bluring has appearred in image;

Fig. 7 (b) is the response of watermark detector, NC1=0.90, and NC2=0.87, NC3=0.88, it is obvious to detect effect.

Letter filtering experimental data during table 5 watermark is anti-

Watermark resist geometric attacks ability

(1) rotational transform

Fig. 8 (a) is 20 ° of watermarking images rotations, the PSNR=12.38dB of watermarking images at this moment, and signal to noise ratio (S/N ratio) is very low;

Fig. 8 (b) is the watermarking images of detection, can obviously detect the NC1=0.81 that exists of watermark, NC2=0.81, NC3=0.81.

Table 6 is the anti-rotation of watermark challenge trial data.Can see in the table when watermarking images (up time) rotates 40 °, NC1=0.75, NC2=0.77, NC3=0.75 still can detect watermark and exist; And Y.H.WU is in paper in 2000, the volume data watermarking algorithm that provides, and when rotation is merely 1.50 when spending, normalized correlation coefficient just lower, NC=0.24 can't detect the existence of watermark.

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

(2) scale transformation

Fig. 9 (a) is the watermarking images when zoom factor 4.0, at this moment center image big than former figure;

Fig. 9 (b) is a watermarking detecting results, can detect the existence of watermark, NC1=1.00, NC2=1.00, NC3=1.00.

Figure 10 (a) is 0.5 watermarking images for zoom factor, at this moment center image little a lot of than former figure;

Figure 10 (b) is a watermarking detecting results, can obviously detect the NC1=1.00 that exists of watermark, NC2=1.00, NC3=1.00.

Table 7 is watermark convergent-divergent challenge trial data, from table 8 can see when the watermarking images zoom factor little to 0.2 the time, related coefficient NC1=0.95, NC2=0.93, NC3=0.93 still can record watermark.The method of in DFT, inserting template of employings such as Pereira can only be resisted zoom factor and be not less than 0.65 convergent-divergent, explains that this invention has stronger nonshrink exoergic power.

Table 7 watermark convergent-divergent is attacked experimental data

Zoom factor	0.2	0.5	0.8	1.00	1.2	2.0	4.0
								NC1	0.95	1.00	0.87	1.00	1.00	1.00	1.00
NC2	0.93	1.00	0.88	1.00	1.00	1.00	1.00
								NC3	0.93	1.00	0.88	1.00	1.00	1.00	1.00

(3) translation transformation

Figure 11 (a) moves down 8% situation for image level, PSNR=11.96dB at this moment, and signal to noise ratio (S/N ratio) is very low;

Figure 11 (b) is watermark detector output, can obviously detect the NC1=0.87 that exists of watermark, NC2=0.87, NC3=0.88.

Table 8 is the anti-translation challenge trial of watermark data.From table, learn, still can detect the existence of watermark, so this digital watermarking has stronger anti-translation capability when level or vertical moving 10%.

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

(4) shear test

Figure 12 (a) is for to shear 10% situation to watermarking images by Y direction, and at this moment the top has been sheared greatly with respect to the primitive medicine image;

Figure 12 (b) is its watermark detection situation, can obviously detect the existence of watermark, NC1=1.00, NC2=1.00, NC3=1.00.

Table 9 is watermark cut-through resistance test data, and test figure can learn that this algorithm has certain anti-shear ability from table.

The anti-shearing attack experimental data of table 9 watermark (shearing) by Y direction

Through above description of test, the embedding grammar of this watermark has stronger anti-conventional attack ability and geometric attack ability, and the embedding of watermark do not influence the value of medical image, is a kind of zero watermark.

Claims (1)

1. many water mark methods of medical image robust based on DWT and DCT; It is characterized in that: based on the extraction of the proper vector of small echo, cosine transform and resist geometric attacks; And the Hash function characteristic in digital watermark, the cryptography and " third party " notion combined; Realized in medical image, embedding the method for multiple digital watermarking, this method is divided into two parts, amounts to four steps:

First is that multi-watermarking embeds: through the embedding operation to multi-watermarking, obtain corresponding two-valued function sequence Key ^k(j);

1) the primitive medicine image is carried out wavelet transformation, the pairing approximation coefficient carries out overall cosine transform again, in cosine transform coefficient, obtains the proper vector V (j) of a resist geometric attacks of this medical image according to the symbol sebolic addressing of Low Medium Frequency coefficient;

2) utilize Hash function and the multi-watermarking W that will embed ^k(j), k=0,1,2 ..., n; Obtain two-valued function sequence Key ^k(j), ${\mathrm{Key}}^k\left(j\right)=V\left(j\right)\⊕W^k\left(j\right);$

Preserve Keyk (j), will use when extracting watermark below, through Key ^k(j) apply for to the third party as key, to obtain entitlement to the primitive medicine image;

Second portion is that multi-watermarking extracts: through two-valued function sequence Key ^k(j) and the proper vector V ' of the resist geometric attacks of medical image to be measured (j), extract multi-watermarking W ^k(j);

3) medical image to be measured is carried out wavelet transformation and the pairing approximation coefficient carries out overall dct transform; In conversion coefficient, go out according to the symbol extraction of Low Medium Frequency coefficient medical image to be measured a resist geometric attacks proper vector V ' (j);

4) utilize Hash function character and have third-party Key ^k(j), extract watermark, $W^k\left(j\right)={\mathrm{Key}}^k\left(j\right)\⊕V^{,}\left(j\right);$

With W ^k(j) and W ^k' (j) carry out normalized correlation coefficient calculating, confirm the entitlement of medical image.

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