Biometric‐based efficient medical image watermarking in E‐healthcare application

IET Image ProcessingVolume 13, Issue 3 p. 421-428 Research ArticleFree Access Biometric-based efficient medical image watermarking in E-healthcare application Puvvadi Aparna, Corresponding Author Puvvadi Aparna aparnap0882@gmail.com Department of Electronics and Communication Engineering, K L University, Vijayawadan, AP, IndiaSearch for more papers by this authorPolurie Venkata Vijay Kishore, Polurie Venkata Vijay Kishore Department of Electronics and Communication Engineering, K L University, Vijayawadan, AP, IndiaSearch for more papers by this author Puvvadi Aparna, Corresponding Author Puvvadi Aparna aparnap0882@gmail.com Department of Electronics and Communication Engineering, K L University, Vijayawadan, AP, IndiaSearch for more papers by this authorPolurie Venkata Vijay Kishore, Polurie Venkata Vijay Kishore Department of Electronics and Communication Engineering, K L University, Vijayawadan, AP, IndiaSearch for more papers by this author First published: 01 February 2019 https://doi.org/10.1049/iet-ipr.2018.5288Citations: 20AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Information hiding is particularly used for security applications to protect the secret message from an unauthorised person. Due to the tremendous development of the Internet and its usage, the issue of protection over the internet is increasing. Under such a condition, transforming the information from the transmitter to the receiver requires more security. Accordingly, in my previous research, an efficient medical image watermarking technique in E-healthcare application using a combination of compression and cryptography algorithm was proposed. The system only gives confidentiality and reliability. To overcome the problem, the authors propose a biometric-based on an efficient medical image watermarking in E-healthcare application, which produces a system for authentication, confidentiality, and reliability of the system. The proposed system utilises the fingerprint biometric for authentication, cryptography process for confidentiality, and reversible watermarking for the integrity. Basically, the proposed system consists of two stages such as (i) watermark embedding process and (ii) watermark extraction process. The experiments were carried out on the different medical images with electronic health record and the effectiveness of the proposed algorithm is analysed with the help of peak signal-to-noise ratio and normalised correlation. 1 Introduction Owing to the improvement of most recent innovation in communications and PC systems, trade of medicinal pictures between healing centres has turned into a standard practice nowadays [1]. These therapeutic pictures are traded for a number of reasons. For instance, video chat among clinicians, interdisciplinary trade amongst clinicians, and radiologists for consultative purposes or to discuss diagnostic and restorative measures and for distant learning of medicinal personnel [2]. To give the protected exchange and viability of patients data, and to secure medicinal images from critical issues such as negligence risk, picture maintenance and so on [3, 4]. To avoid the issues, three distinctive research spaces, in particular, unique finger impression biometric for authentication, cryptosystem for confidential data, and reversible watermarking for the integrity [5]. To encourage sharing and remote treatment of medical images in a safe way watermarking ensures alluring properties [6, 7]. Digital watermarking (DWM) is the way towards embedding data into a computerised signal, which might be utilised to confirm its authenticity or the character of its owners, in the same manner as paper bearing a watermark for obvious identification [8]. In DWM, the signal might be audio, pictures, or video. If the signal is copied, then the information also is carried in the copy. A signal may carry several different watermarks at the same time. The DWM can be embedded in the cover medium using spatial domain techniques or the transform domain techniques [9]. In a spatial area procedure, the information is straightforwardly inserted in the pixels by supplanting the bits of the pixel by the information bits [10]. The spatial space installing is not powerful yet can be utilised at whatever point high payload is required. Nonetheless, these methods are simple and easy to realise and are adequate in an assault-free condition and lossless pressure [11]. In a change area procedure, the coefficients of the cover medium are altered. Change space inserting methods offer high heartiness and greater security to assaults [12]. Another classification of watermarking systems is reversible methods and irreversible methods. The original image can be obtained without loss from the watermarked image with reversible watermarking techniques [13, 14], while lossless recovery of the original image is not possible with irreversible watermarking techniques [15]. Reversible watermarking is more suitable for medical images [16]. Electronic health record (EHR) technology has substituted the inefficient paper records paradigm and is existing in numerous forms such as diagnostic reports, images, vital sign signals etc. When embedding the additional electronic patients' record (EPR) or the DWM within the medical image; the image quality must not be affected [13]. DWM methods have the potentiality of becoming an all-in-one solution tool providing alternative and/or complementary results for a wide number of problems associated with medical data management and distribution [14]. During the last year, many medical image watermarking (MIW) schemes are explained [15-18], such as text and graphical signal based watermarking [15, 16], blind reversible MIW with good imperceptibility [18], and discrete cosine transform (DCT)-based watermarking [17]. Additionally, the important image watermarking methods include the DCT [19], discrete wavelet transform (DWT), Z-transform [20], bit-plane compression techniques [21, 22], and LSB-based method [23]. Furthermore, the wavelet-based watermarking system to entrench multiple watermarks in medical images is enlightened. The watermarking methods for protected data hiding in wavelet trampled fingerprint images are elucidated in [24, 25]. Visual cryptography is an additional field that is utilised for content protection. Visual cryptography has been implemented in numerous implementations, including but not limited to data hiding, overall access edifices [26], and visual confirmation and empathy [27]. The presented paper intends to reveal the potential of DWM in medical data management issues and propose a novel method to enforce integrity, authenticity, and confidentiality of the medical information, by embedding information bit to the medical image. In this study, at first, we segment the medical image using a region growing (RG) algorithm. Then we encrypt the region of interest (ROI) part using the SHA-256 algorithm. After that, we consider the electric healthcare recode (EHR), which is having important information about the patient. We have to encrypt the information using the elliptic curve cryptography (ECC) algorithm. For authentication purpose, in this study, we add the fingerprint information. From the fingerprint, we calculate the minutiae point and convert that point into a binary format. Then, we concatenate the image, fingerprint and EHR information and compressed using the arithmetic encoding algorithm. Finally, we embed the compressed bit stream into the original image. The same process is repeated for the extraction process. Moreover, the proposed scheme is highly secure having several layers of security mechanisms. Watermarking by combining lossless data compression and encryption techniques, embedding of the watermark bits in the embedding region, all these aspects make the proposed scheme an effective novel MIW technique. The rest of the paper is organised as follows: brief reviews of some of the literary works in MIW techniques are presented in Section 2. The background of the proposed work is explained in Section 3 and proposed MIW is explained in Section 4. The experimental result is analysed in Section 5 and the conclusion part is given in Section 6. 2 Related work A host of inquiries are accessible in the literature associated with the topic of MIW, some of that is reviewed at this time. Rohit Thanki et al. [28] have explained the fast discrete curvelet transform (FDCuT) and DCT-based blind MIW. This method was mainly used for medical image protection in telemedicine applications. However, using this method noise is always present in the extracted image. This method only implemented in binary watermarking was unable to embed EPR data. Moreover, the robust watermarking outline with a blind abstraction procedure for the high-efficiency video coding encoded video was provided by Tanima Dutta and Hari [29]. Experimental solutions validated that their work confines to the upsurge in video bit rate and dilapidation of perceptual quality. However, this method was highly sensitive and computationally expensive. Similarly, a reversible watermarking technique in transform domain was explained in Selvam et al. [30]. This method was implemented using a combination of integer wavelet transform (IWT) and discrete gould transform (DGT), which was mainly utilised for telemedicine application, banking, and military purpose. This method provides better results, but involves expensive computation. Moreover, Loganathan and Kaliyaperumal [31] have elucidated an effectual rescindable adaptive video watermarking system for multiple watermarks on the basis of bi-directional associative memory (BAM) neural networks and fuzzy inference system (multi-BAM-FUZ) scheme. This adaptive embedding strength was engendered with the help of the fuzzy inference system that receipts HVS features namely luminance, texture, and edge of each frame as an input in the DWT transform. However, this method attains higher compression as well as expensive computation. In [32] Amit Kumar Singh et al. [32] have explained multiple watermarking techniques for securing online social network contents using the back propagation neural network. This work addresses the issue of ownership identity authentication, multiple watermarks are embedded instead of a single watermark into the same multimedia objects simultaneously, which offer the extra level of security and reduced storage and bandwidth requirements in the important application areas such as E-health, secure multimedia contents on online social network, secured E-voting systems, digital cinema, education and insurance companies, and driver's license/passport. Similarly, Sharma et al. [33] have explained a robust and secure ROI and non-ROI-based watermarking method for medical images. This method has less flexibility against noise attack. Aparna and Kishore [34] have explained an efficient MIW technique in E-healthcare application using hybridisation of compression and cryptography algorithm. This method was mainly used for integrity and confidentiality but this technique consumes more time. The summary of the literature survey is given in Table 1. Table 1. Summary of the literature survey Ref. no. Proposed Technique used advantages Disadvantage [28] blind MIW scheme is proposed in order to increase the security of transfer data FDCuT and DCT this scheme is mainly used for medical image protection in telemedicine application noise is always present in the extracted watermark image [29] compressed video watermarking proposed HEVC video encoding and extraction algorithm for compression, it will increase video resolution highly sensitive and computationally expensive [30] reversible watermarking technique in the transform domain is proposed IWT and DGT telemedicine application, military, and banking computation complexity [31] an reversible adaptive video watermarking is proposed BAM neural networks and fuzzy inference system it will reduce illegitimate user higher compression ratio as well as expensive computation [32] multiple watermarking techniques for securing online social network contents is proposed back propagation neural network This method provides the extra level of security and reduces storage/bandwidth computational complexity [33] a robust MIW system for E-healthcare is proposed DCT used for tele-diagnosis and e-health care application less flexibility against noise attack [34] proposed the MIW technique in E-healthcare application ECC and arithmetic encoding this method focuses on integrity and confidentiality time complexity is more 3 Methods used in watermarking In this medical image, EHR, biometric-based MIW has different approaches. The different approaches are explained below. 3.1 Fingerprint biometric In this study, we introduce fingerprint-based MIW. This section explains how the minutiae point is extracted from the fingerprint. The step by step process of minutiae point extraction is explained below. 3.1.1 Fingerprint image enhancement Minutiae point is an important feature of a fingerprint. The main purpose this section is to extract the minutiae point from fingerprint image Fin. Before extracting the minutiae point, firstly, we have to enhance the fingerprint image quality because the fingerprint images obtained from sensors or other media do not possess perfect quality. The enhancing process improves the contrast between ridges and furrows, and connects the false broken points of ridges with sufficient amount of ink, which are very useful in providing a higher accuracy to fingerprint recognition [7]. A significant characteristic is the quality of the ridge structures in a fingerprint image, as the ridges carry the information of characteristic features needed for minutiae extraction. In a well-defined fingerprint image, the ridges and valleys should alternately flow ideally in the locally constant direction. However, in practice, a fingerprint image may not always be well defined due to the elements of noise that corrupt the clarity of the ridge structures. This corruption occurs because of variations in skin and impression conditions, e.g. non-uniform contact, scars, and soil with the fingerprint capture device. Therefore, this enhancement process reduces the noise. 3.1.2 Histogram equalisation (HE) After the enhancement, we perform HE for a fingerprint image. To increase the perceptional information of the image we have to perform HE, which is expanding the pixel value distribution of an image. After the histogram process, all the image pixels are within the range of 0–255. 3.1.3 Fingerprint image binarisation After the equalisation process, we convert the fingerprint image into binary image Bin because minutiae extraction algorithms work based on binary images. Binarisation is the process that changes a grey level image into a binary image. This improves the contrast between the ridges and valleys in a fingerprint image and consequently facilitates the extraction of minutiae. The binary image consists of 0‱s (black pixels) and 1‱s (white pixels). Here, the black pixels represent ridges and the white pixels represent valleys. 3.1.4 Thinning After the binary image conversion, we are going to reduce the thickness of the image using the thinning process. Diminishing is a morphological operation that progressively removes the foreground pixels until the point that they are one pixel wide. After the thing procedure, we acquired the fingerprint images having a single pixel width without any discontinuities. Utilising the thinning procedure, we lessen the noise of singular pixels and each ride is diminished with its middle pixel. This algorithm is accessible in MATLAB through the 'thin' operation under the 'bwmorph' function. 3.1.5 Minutiae points extraction Thinning eradicates the redundant pixels of ridges until they are just one pixel wide. Ridge thinning algorithm is applied and in each scan of the full fingerprint image, the algorithm marks down redundant pixels in each small frame and finally removes all those marked pixels after several scans. Then fingerprint ridge thinning minutiae points are focused. After calculating the minutiae point, we convert each point into eight-bit binary value. In this study, we take the 12-minutiae point. The final binary bit is used for further processing. 3.2 Elliptical curve cryptography (ECC)-based encryption–decryption process ECC is a cryptographic scheme that utilises the possession of elliptic curves to yield cryptographic algorithms. It fits public-key cryptography in that each user frequently has a pair of keys, a public key and a private key and also a set of operations associated with the keys to perform the cryptographic operations [18]. The private and public keys produced by the ECC method make the encrypted data safer. The general equation of the elliptic curve is given below (1)To create both public key and private key, we need the key generation part. To encrypt the message, the sender will be using the receiver's public key and the receiver will decrypt its private key. Key generation by ECC: The operations of ECC are explained over two predetermined fields: prime field and binary field. For cryptographic operations, the suitable field is selected with a finitely massive number of points. The prime field operations choose a prime number, and finitely large numbers of basic points are produced on the elliptic curve (2)where r is the random values, P is the point of the curve, and is the public key. Encryption and decryption: Here, the input message is given to the ECC algorithm, which has encrypted the message. It represents in EM (encrypted message) in ECC. Here the input message is encrypted and the output is split into two cipher text as C1 and C2(3) (4)In (3), symbolises the key value. This encrypted messages C1 and C2 are sent to the receiver. After receiving the ciphertext, the receiver decrypts the message using the following equations: (5) 4 Proposed medical image watermarking methodology The novelty of the proposed approach is to design a lossless MIW based on the hybridisation approach and this method enforces integrity, authenticity, and confidentiality of the medical information. Transformation of the medical image and the related information about the patient from one hospital to another hospital is the main purpose. Therefore, in this study, we propose a watermarking system. Here, we present fingerprint biometric for authentication, cryptosystem for confidential data, and reversible watermarking for the integrity. Mainly, the proposed approach consists of two modules such as (i) embedding and (ii) extraction. The overall process of the proposed system is given in Fig. 1. The embedding and extraction process is explained in the below section. Fig. 1Open in figure viewerPowerPoint Overall concept of the proposed watermarking 4.1 Watermark embedding procedure Watermarking is the process by which imperceptibly embeds information (watermark) within a cover image. This process is for securing information while transferring through networks. This study proposes a novel method to enforce integrity, authenticity, and confidentiality of the medical information, by embedding two different watermarks in the medical image. The embedding process steps are explained below. Input: Medical image (IC), a fingerprint image (F), EHR. Output: Watermarked image (IW) Step 1: Consider the input image IC size of 256 × 256. The image consists of ROI part and non-ROI part. Here, at first, we segment the ROI region (IROI) from the input image using RG algorithm [1]. Step 2: Then, we calculate the hash function of the ROI region using the SHA-256 algorithm. In this section, we obtain the hash image (ROIHash). Step 3: After that, we calculate the binary value of ROIHash. Then, we change the binary value ROIBi into hexadecimal value ROIHex. Step 4: Then, we consider another part of the input, which is an EHR. Here, we encrypt the EHR using elliptical cryptography (ECC) algorithm. The EEC consist of the patient reference number, the name of the doctor, name of the patient, age of the patient, and date of admission. Step 5: Then, we consider the third part of the input which is fingerprint (F). In this fingerprint image, we calculate the minutiae points refer (Section 3.1). This point is in the form of binary FBin. Step 6: Then, we concatenate the image information, EHR, and fingerprint information. Here, concatenate ROIHex, EHRHEX, and Fbin, we obtain Icon. Step 7: To increase the efficiency of the embedding process, we compress the Icon using the arithmetic coding method. In this step, we obtain the compressed bit stream of BSCom. Step 8: Then, we generate the initial random matrix R using the following steps. The pixel values in the input image 'IC' is summed and denoted as ISeed(6)A RM R is generated with the size of the input image with the aid of the pseudo-RM generation with ISeed(7) Step 9: Then, we generate the final RM R is generated by using the following steps: (i) 0.5 is subtracted from the generated RM R and the resultant matrix is multiplied by 2. The final resultant matrix is denoted as . (8) (ii) Finally, the intended RM is generated using the pseudo-RM generator with the matrix as a seed value. (9) Step 10: Then, we embed the BSCon into original image IC. Finally, we obtain the watermarked image IW. The embedding process is given below. (a) If the watermark bit is 0 means the RM is multiplied by the embedding strength and the resultant matrix is added to the original image I(10) (b) If the watermark bit is 1 no operation is performed. Step 11: Repeat steps 9 and 10 until all the watermark pixels are embedded. The initial RM R for every iteration is generated from the PRMG initiated with seed . 4.2 Watermark extraction process During the extraction process, an operation reverse to the embedding operation is performed, to extract the compressed bit CBS from a suspicious watermarked image IW and compare it to the original image I and information EHR. To discover the original information, in the beginning; the extraction algorithm performs the same operation as the embedding algorithm. Firstly, the embedded watermark image IW is given as the input of the extraction operation. The extraction process is given in Fig. 2 and step by step process is explained below. Step 1: In the extraction process, firstly, we generate a RM using steps (9 and 10) cited in the previous sub-section. In accordance with the previous sub-section, the initial RM R for every iteration is generated from the initiated with seed . Fig. 2Open in figure viewerPowerPoint Watermark embedding process Step 2: Then, compute the correlation coefficient RCor between the watermarked image IW and the generated RM using the following equation: (11)where represent the watermarked image, represents the RM, represents the mean value of IW and the mean value of B. Step 3: Divide the calculated correlation coefficient value RCor by two and denote the resultant value is as (12) Step 4: Repeat steps 1 to 3 for the size of the watermark image and stores the resultant values in a vector . Step 5: Then calculate the mean value of the vector . (13) Step 6: The elements of the vector are compared against the mean value for the extraction of watermark image pixels. If the element's value is greater than the mean value, the extracted watermark image pixel is '0' otherwise the pixel value is '1'. The above process is described as follows: (14) Step 7: Then, we decompress the bit stream EBS using the arithmetic decoder method and obtain the original bit stream DBS. Step 8: From the original bit stream DBS, we extract HROI, HEHR, and Hfin. Step 9: Convert HEHR into their corresponding bit stream representation as BEHR. Then change BEHR into original representation as Eencrypt. Step 10: Decrypt Eencrypt using K using the ECC method to get EHR. Step 11: Similarly, we calculate the hash for HROI to obtain the original ROI image. 5 Result and discussion We present the results of our proposed methodology and examine ourpresentation in this part. The proposed MIW scheme discussed in this study haseffectively embedded the information bit into the medical image and extracted itback from the information bit to the embedding image. The proposed videowatermarking system is executed in a windows machine containing configurations Intel(R) Core i5 processor, 3.20 GHz, 4 GB RAM, and the operating system platform isMicrosoft Window 7 Professional. The software used for implementation is MATLAB. Inour experiment, we used five types of brain tumour images, which have a size of256 × 256. The input images are shown in Fig. 3and the sample EHR used in the experiment is given in Table 2. The sample fingerprint image is given in Fig. 4. Table 2. Sample electronic healthcare record The Heart Foundation: Kolkata PatientReference number: 019181918 Name of the doctor: Dr Pratap SinghName of the patient: Ms. Rakhi Age in the year: 45 Date ofadmission: 12.09.2008 Fig. 3Open in figure viewerPowerPoint Experimental used images Fig. 4Open in figure viewerPowerPoint Sample fingerprint image 5.1 Evaluation metrics The evaluation of the proposed technique is carried out using the following metrics as suggested by the below equations: Peak signal to noise ratio (PSNR): It is used to measure the quality of the watermarked image. The PSNR is the ratio between the source image and the watermarked image. The PSNR is identified using the mean square error (MSE). The MSE gives the cumulative squared error between the corrupting noise and the maximum power of the signal. Higher the PSNR and lower the MSE values are improving the quality of watermarking where are original image and are watermarked images NC: It measures the similarity between the original watermark image and the watermark extracted from the attached image, where is the pixel value of the original image and is the pixel value of the embedded image Second derivative-like measure of enhancement (SDME): The SDME is an enhancement measure based on the concept of the second derivative. For image quality assessment, this measure is mainly used. The second derivative-like measurement is calculated using the following equation: where K1, K2 are two blocks of an image, IMAX represents the maximum value of the pixel, Imin represents the minimum value of the pixels and Icen represents the centre pixel value of each block. 5.2 Experimental results The performance of the proposed MIW technique using the hybrid lossless data compression and the encryption algorithm is tested in this section. The main objective of this study is to generate a watermark bit from the medical information (watermark bit) and embed the bit into the input medical image. Then extract the original information without any information loss. In this study, we encrypt three types of information on the patient such as ROI part, EHR, and fingerprint. Here, we have given the three types of input such as medical image, EHR, and fingerprint to the system. Here, at first, we segment the ROI part from the image using RG algorithm. Then, we encrypt the ROI part using the SHA-256 algorithm and also encrypt the EHR using the EEC algorithm. Then we extract the minutiae point from the fingerprint. After that, we concatenate the information bit. The fingerprint is biometric, which is used to increase the security of the watermarking system. Then, we compress the information bit using arithmetic encoding. After that, the compressed bit is embedding into the original input image. Finally, we obtain the watermarked image (Fig. 5). Fig. 5Open in figure viewerPowerPoint Experimental results of fingerprint (a) Image of the sample fingerprint, (b) Image after binarisation, (c) Minutiae points In this work, we proposed a novel biometric-based MIW in E-healthcare application. Here, withoutfingerprint, no one can extract the watermark image. Basically, in medicalimages, only the ROI part is very important. Therefore, at first, we separatethe ROI part from the input image using the RG algorithm. The visualrepresentation of the segmentation process is given in Figs. 6 and 7.The step by step output of the RG algorithm is given in Fig. 8. After the segmentation process, we encryptthe ROI part using the SHA-256 algorithm. The hash value of the ROI image isshown in Table 3. After that, we encryptthe patient EHR, which includes personal information (name, age etc.), clinicinformation (name, doctor name etc.) and diagnosis and some managementinformation (record number, date etc.). Then, again we consider the fingerprintimage and extract the minutiae point from the image. The extracted minutiaepoint is given in Table 4. Table 5 shows the watermarking attack resultsfor different images. In this study, we used five types of tumour images forexperimentation. Here, we apply the different attacks such as speckle noise,Gaussian noise