Objective

2

Diffusion weight imaging (DWI)

as a new medical imaging technique

transforms the diffusion motion of water molecules in tissues into grayscale or other parameter information of an image by applying multi-directional diffusion magnetic sensitive gradients under each diffusion sensitive gradient. This technique can be used for the auxiliary diagnosis of living heart myocardial fiber modeling

brain fiber

lesions of the central nervous system

liver fibrosis

and other diseases. With the popularization of telemedicine diagnostic technology

an increasing amount of DWI data are being used for remote diagnosis and scientific research. DWI images

which are originally stored and used on a single machine in a hospital

must be transmitted and used over the network. Many scholars have proposed many watermarking algorithms for protecting medical images

such as the reversible watermarking algorithm

robust reversible watermarking algorithm

and zero-watermarking algorithm. The advantage of the reversible algorithm is that it can be completely used for nondestructive image recovery. The robustness of the reversible watermarking algorithm is too weak to guarantee the existence of the reversible watermark when embedded images are attacked intentionally or unintentionally. Therefore

some researchers propose the robust reversible watermarking algorithm. The robust reversible watermarking algorithm could restore an original picture when no attack occurs and could draw embedded watermarking. It ensures that its robust reversible performance should carry additional information. Thus

it must consume a large amount of transmission bandwidth. Some robust reversible watermarks are constructed by dual watermarking

and they depend on one another's information to extract the watermarks. To protect medical images by other methods

some researchers use the zero-watermarking algorithm

which is different from the traditional method that embeds information into images. The zero-watermarking algorithm can retrieve internal features from data to build binary watermarking and then save it in a third-party application. When the image is used by other people without the license

we could use zero-watermarking to prove copyright. Thus

the zero-watermarking algorithm

as a non-embedded algorithm

cannot actively complete the protection of property information. The robust watermarking algorithm plays an irreplaceable role in ensuring that the medical image watermarking information has certain robustness. To prevent unauthorized DWI images from being used or tampered with

this study proposes a robust watermarking algorithm based on DWI images.

Method

2

The algorithm initially obtains specified slips by the maximum inter-class variance segmentation algorithm and area control threshold to ensure that the selected slice has a sufficient embedding area because the tip and the bottom of the heart are unsuitable for embedding. The foreground region with a diffusion gradient direction image is prepared for embedding. We obtain the low-frequency sub-band coefficient by using integer wavelet transform in the default region. Then

we count the low frequency and analyze the low sub-frequency coefficient by using the fixed step length; the low sub-frequency coefficient follows the characteristics of the coefficient of DWI images. The ratio relation of adjacent clusters in the histogram subject area is adjusted for watermark embedding. Finally

we propose to design the quantitative reversible relationship between apparent DWI coefficients

with diffusion tensor imaging (DTI) as the key. We use this key to encrypt a DWI image after embedding the watermark to effectively protect the copyright information of the DWI image.

Result

2

The algorithm can maintain its robustness and reduce the change in the DTI parameters in the experiment on robustness and changes in the parameters of DTI after embedding. The proposed algorithm also has excellent robustness in attack experiments

such as those involving Gaussian noise

contrast expansion

and small angle rotation. In the experiment on parameter change measurement before and after embedding

the algorithm is greatly reduced the volume of change in isotropic and fiber main direction of the myocardial fiber. In our proposed method

the main direction of the fiber is reduced by more than 100

and the average change of the mean diffusivity is reduced by more than 30 in the same database. In the visual quality of the algorithm

the peak signal-to-noise ratio is approximately 8 dB higher than that specified in the comparative literature.

Conclusion

2

An embedded selection feedback mechanism is proposed to carry out the selection of watermark embedding according to actual embedding demands. Then

the statistical histogram of the sub-band coefficient is constructed by specifying the fixed step length according to the characteristics of the wavelet transform coefficient of the DWI image. Finally

the reversible key algorithm based on the quantitative relationship between DWI and DTI is constructed. Experiments show that this algorithm can be applied to the watermark embedding of dispersion weighted imaging and can satisfy fiber direction as little as possible.