in the past few decades , there has been a tremendous growth in information and communication technology leading to easier access to any form of digital data including medical data . medical data are those that are generated from medical information system ( mis ) , hospital information system ( his ) , radiology information system ( ris ) , and electronic patient records ( epr ) . most of the medical diagnosis is based on images from ct scans , x - rays , mri scans , mammography , and other forms of image modalities . the time factor is very important to discover the abnormality issues in target images , especially in various cancer tumors such as the breast cancer . researchers at the january 1997 consensus development conference presented data discussing the facts related to breast cancers detected by mammography . their data showed that mammograms have more prognoses than cancers detected by other imaging modalities . though the survival rate of a cancer patient is less , it makes a difference for a breast cancer patient . to increase the survival rate of breast cancer patients , mammography is used for earlier detection and treatment stages with reduced risk . doctors ' advice and treatment are based on the data the doctor analyses from the mammogram . nowadays , most of the patients prefer a second / third opinion on the medical analyses received from their doctor . the second / third opinion may be from a doctor located at a different place through telemedicine . telemedicine applications are those that provide for diagnostics , prescription , consulting , and sometimes conferences to telesurgery . telemedicine plays an important role in today 's world , where the patient 's medical data is transmitted over the internet . the medical data considered may be medical images ( mammogram ) which has to be shared or transmitted over internet . this medical data is crucial data and is at the risk of unauthorized access or manipulation . authenticating as well as protecting it is essential since critical judgment is done on medical images especially mammograms during breast cancer detection . cryptography , steganography , and watermarking are such schemes that are used to protect digital data . digital image watermarking is a technique of embedding a watermark ( say logo ) into the host image for dealing with security issues . medical image watermarking deals with medical data authentication , ownership , security , source identification , and patient identification . when dealing with epr , it is mandatory to consider confidentiality to access the information , availability of the data to be used when required , and reliability of the data . these are maintained through security issues , namely , integrity , availability , authentication , and confidentiality nonrepudiation . medical image watermarking provides for security at the origin reducing piracy of crucial medical data and also protects medical documents or images that can be illegally modified . part of the medical information , the region of interest ( roi ) , is crucial and must not be altered . several watermarking schemes exist , and these schemes adopt both additive and multiplicative approaches in time domain . these schemes adopted ss ( spread spectrum ) , dct ( discrete cosine transform ) , dft ( discrete fourier transform ) , dwt ( discrete wavelet transform ) , svd ( singular value decomposition ) , ridgelets , and contourlets   in frequency domain . proposed a watermarking scheme to verify the integrity and authenticity of ct scan images . here have identified three different schemes to watermark a medical image : embedding the information within the non - region of interest ( nroi ) : this method   generally places the watermark in the gray portions of the image . , this method may seem too annoying to the physician.these schemes deal with reversible watermarking and were better suited for integrity control and data hiding .these methods normally minimize distortion . have proposed a method where the physician selects the maximum power of the watermark under the level of interference with diagnosis . wakatani   proposed a scheme wherein the watermark was embedded in the nroi ( non - region of interest ) by adopting dwt . medical image watermarking schemes must consider computational complexities which may lead to time delays for the physician and are still in the early stages of development . it is difficult to evaluate watermark interferences with diagnosis . embedding the information within the non - region of interest ( nroi ) : this method   generally places the watermark in the gray portions of the image . during application of salt and pepper noise , this method may seem too annoying to the physician . these schemes deal with reversible watermarking and were better suited for integrity control and data hiding . this was achieved by replacing some of the image details . in this paper , a robust medical image watermarking scheme using fractional wavelets is proposed . the proposed scheme is divided into two stages , namely , the embedding stage and the extraction stage . frft ( fractional fourier transform ) has good reconstruction capability compared to fft ( fast fourier transform ) . when frft is combined with wpt ( wave packet transform ) , it has the capability to retain the coefficients after attacks . firstly , arnold transform is applied to the host image and the host image is scrambled . secondly , the value of the transform order  is used as the key . according to this key thirdly , the position of all frequency subbands is changed at each level using some secret rule known only to the user or creator . quality of the extracted watermark to the original watermark is directly proportional to degradation of the image quality . sections 2 and 3 describe fractional wave packet transform and arnold transform , respectively . we conclude the work presenting the future enhancements to obtain more robust medical image watermarking schemes . based on the idea of fractional fourier transform ( frft ) and wave packet transform ( wpt ) , fractional wave packet transform ( frwpt ) was introduced by huang and suter . mathematically , for an input signal x(t ) represented along the time axis and its fourier transform represented along the frequency axis , frft , f(t , u ) , of the input signal , x(t ) , is given by(1)ft , u=+ct , uxtdt , where c(t , u ) is the fourier transformation kernel . the wave packet transform ( wpt ) is a combination of short time fourier transform ( stft ) and continuous wavelet transform ( cwt ) . wpt ( w ) of an input signal , x(t ) , is given by(2)w=12+expjuttxtdt.wpt can also be defined as the ft of a signal windowed by a wavelet , dilated by  and translated by . frwpt , w(u , a , b ) , for a given input signal x(t ) can be defined as(3)wu , a , b=1a+ctbaxtdt.the frwpt is a function of time , frequency , and scale . the computation of frwpt corresponds to the following steps as explained by huang and suter : a product by a wavelet.a product by a chirp.a fourier transform.another product by a chirp.a product by a complex amplitude factor.for computational purposes , a simple implementation of frwpt is used as shown in figure 1 . a product by a wavelet . a product by a chirp . the computational algorithm can be described as follows:(1)the transform order of  is used for the frwpt . if frwpt is applied to the original signal , and the transformed signal is reconstructed from inverse frwpt , then the transform order  is that value which results in minimum mean square error between the original and the reconstructed signal . the process may sometimes be long and cumbersome due to its trial and error approach , but it is limited only to once per signal.(2)frft is performed on the input signal , with user defined transform order  , followed by wavelet packet transform . this phase is the decomposition phase.(3)inverse wavelet packet transform and inverse frft are performed on the transformed signal with transform order . this phase is the reconstruction phase . if frwpt is applied to the original signal , and the transformed signal is reconstructed from inverse frwpt , then the transform order  is that value which results in minimum mean square error between the original and the reconstructed signal . the process may sometimes be long and cumbersome due to its trial and error approach , but it is limited only to once per signal . frft is performed on the input signal , with user defined transform order  , followed by wavelet packet transform . inverse wavelet packet transform and inverse frft are performed on the transformed signal with transform order . this phase is the reconstruction phase . to further improve the security and improve the proposed watermarking scheme , the host image is preprocessed ( scrambled ) by applying arnold transform . consider an image of size n  n ; the arnold transform is defined by ( 4)xy=11121112xymodn , where x , y are the coordinates of the image and x , y are the coordinates after scrambling . the proposed scheme can be divided into two phases : the embedding phase and the extraction phase . in the embedding phase , the host image is converted to a reference image and the watermark is embedded on the reference image . during the extraction phase , the watermark is first extracted and then the host image is obtained from the reference image . the embedded watermark is smaller than the host image by a factor raised to the power of 2 along both the directions . the proposed embedding and extraction scheme is shown in figures 2 and 3 , respectively . assuming that i(x , y ) represents the original image of size p  q , w(x , y ) represents the watermark of size p  q ; the embedding and the extraction phase can be detailed as follows . the proposed watermarking embedding process can be outlined as follows:(1)scramble the host image by applying arnold transform.(2)apply l - level fractional wave packet transform to the arnold transformed image . the subbands obtained are hh , hl , lh , and ll.(3)change the position of the subbands . the pattern to swap / change the image subband is defined by the creator.(4)apply l - level inverse fractional wavelet transform to obtain the reference image iref(x , y ) and then divide the reference image into subblocks.(5)apply watermark to the subblocks to obtain the modified reference watermarked image . the modified subblocks are represented by(5)wref = irefx , y+wp , q ,  where  denotes the strength of the watermark . integrate the subblocks to obtain the reference watermarked image , wref(x , y).(6)perform l - level fractional wavelet transform.(7)change the position of subbands . perform l - level inverse fractional wavelet transform.(8)apply arnold transform to scramble back the image to obtain the watermarked image , w(x , y ) . apply l - level inverse fractional wavelet transform to obtain the reference image iref(x , y ) and then divide the reference image into subblocks . the modified subblocks are represented by(5)wref = irefx , y+wp , q , where  denotes the strength of the watermark . apply arnold transform to scramble back the image to obtain the watermarked image , w(x , y ) . the watermark extraction process can be outlined as follows:(1)apply arnold transform on the watermarked image w(x , y).(2)perform l - level fractional wavelet transform on the arnold transformed image.(3)change the positions of all the subbands.(4)perform l - level inverse fractional wavelet transform to obtain the watermarked reference image , wref(x , y).(5)extract the watermark , w(x , y).(6)perform l - level fractional wavelet transform on the watermarked image , w(x , y).(7)change the positions of all the subbands.(8)perform l - level inverse fractional wavelet transform to obtain the host image , i(x , y ) . perform l - level inverse fractional wavelet transform to obtain the watermarked reference image , wref(x , y ) . perform l - level fractional wavelet transform on the watermarked image , w(x , y ) . the watermark used is the bmw logo ( any grey scale image can be used ) ; reference image is obtained using 2-level decomposition of frwpt . since 2-level decomposition gives a block size of 64  64 , the watermark is embedded 16 times into the modified reference image to get the watermarked reference image . table 1 shows the values of psnr and ssim values of the watermarked image with reference to the original image without any attack . the watermarked image is subjected to common attacks to investigate the robustness of the proposed scheme . the attacks considered are average and median filtering , gaussian noise , salt and pepper noise , cropping , resizing , rotation , and sharpening attacks . the performance of the watermarking algorithm under these attacks is tabulated in tables 2 and 3 . the extracted watermark quality is also degraded . when the image is subjected to weiner filtering the image quality this is due to addition of noise which degrades the quality of the image and hence the watermark extracted is degraded . similar effects are also seen when gaussian noise and salt and pepper noise are added . cropping is done by deleting certain number of rows and columns . since a large part of the image was cropped ( 200  200 pixels towards upper left corner of the image ) , most of the medical information was removed . but this can be further modified to crop only the non - region of interest , preserving important medical data . the image was rotated by an angle of 5 degrees towards the left and right . the proposed algorithm was also tested for sharpening and contrast adjustments and results show that the proposed scheme could withstand these results . graphical analysis of the proposed scheme under various common attacks discussed above is depicted in figures 6 and 7 . the graph shows the relation between psnr and correlation coefficient for different values of  , where  denotes the strength of the watermark . it is observed that the proposed watermarking scheme is not image variant and maintains a linear relationship for most of the mammogram images used . in this work , a robust nonblind medical image watermarking scheme for mammograms was proposed applying frwpt . experimental results show that the scheme is robust to common attacks like the compression attacks , interference attacks , and signal processing attacks . it is vital to adopt imperceptible watermarks to ensure security issues of integrity and authenticity .
exchanging of medical data requires efficient authentication and protection of medical data that can be illegally modified . 
 watermarking plays an important role in protecting , sharing , and securing medical data . in this work 
 , a robust nonblind medical image watermarking scheme is proposed . 
 the process involves two steps : the embedding and the extraction phase . during the embedding phase 
 , l - level frwpt is performed on the host image and the watermark is embedded into the modified reference image . in the second phase , inverse frwpt is performed on the watermarked image to extract the watermark from the watermarked image . 
 the proposed scheme is tested on mammograms images and is subjected to common attacks like gaussian filtering , median filtering , compression , sharpening , and contrast adjustments . 
 experimental results show that the proposed scheme is robust .