Region Of Non-interest Research Articles

Quantization based multiple medical information watermarking for secure e-health

In this paper, an improved wavelet based medical image watermarking algorithm is proposed. Initially, the proposed technique decomposes the cover medical image into ROI and NROI regions and embedding three different watermarks into the non-region of interest (NROI) part of the transformed DWT cover image for compact and secure medical data transmission in E-health environment. In addition, the method addressing the problem of channel noise distortion may lead to faulty watermark by applying error correcting codes (ECCs) before embedding them into the cover image. Further, the bit error rates (BER) performance of the proposed method is determined for different kind of attacks including ‘Checkmark’ attacks. Experimental results indicate that the Turbo code performs better than BCH (Bose-Chaudhuri-Hochquenghem) error correction code. Furthermore, the experimental results validate the effectiveness of the proposed framework in terms of BER and embedding capacity compared to other state-of-the-art methods. Therefore, the proposed method finds potential application in prevention of patient identity theft in e-health applications.

Tinnitus alters resting state functional connectivity (RSFC) in human auditory and non-auditory brain regions as measured by functional near-infrared spectroscopy (fNIRS).

Tinnitus, or phantom sound perception, leads to increased spontaneous neural firing rates and enhanced synchrony in central auditory circuits in animal models. These putative physiologic correlates of tinnitus to date have not been well translated in the brain of the human tinnitus sufferer. Using functional near-infrared spectroscopy (fNIRS) we recently showed that tinnitus in humans leads to maintained hemodynamic activity in auditory and adjacent, non-auditory cortices. Here we used fNIRS technology to investigate changes in resting state functional connectivity between human auditory and non-auditory brain regions in normal-hearing, bilateral subjective tinnitus and controls before and after auditory stimulation. Hemodynamic activity was monitored over the region of interest (primary auditory cortex) and non-region of interest (adjacent non-auditory cortices) and functional brain connectivity was measured during a 60-second baseline/period of silence before and after a passive auditory challenge consisting of alternating pure tones (750 and 8000Hz), broadband noise and silence. Functional connectivity was measured between all channel-pairs. Prior to stimulation, connectivity of the region of interest to the temporal and fronto-temporal region was decreased in tinnitus participants compared to controls. Overall, connectivity in tinnitus was differentially altered as compared to controls following sound stimulation. Enhanced connectivity was seen in both auditory and non-auditory regions in the tinnitus brain, while controls showed a decrease in connectivity following sound stimulation. In tinnitus, the strength of connectivity was increased between auditory cortex and fronto-temporal, fronto-parietal, temporal, occipito-temporal and occipital cortices. Together these data suggest that central auditory and non-auditory brain regions are modified in tinnitus and that resting functional connectivity measured by fNIRS technology may contribute to conscious phantom sound perception and potentially serve as an objective measure of central neural pathology.

Region Based Data Hiding in Medical Images

Medical images are generated and transmitted between hospitals for analysis by physicians who are geographically apart. Moreover, as the patient data is also embedded within the medical images, it is very important to maintain the privacy of patient data. For data hiding in images, many techniques are used. Some techniques will hide data but embedding cause some misrepresentation of image, and some techniques will cause misrepresentation during retrieval. In this paper, new approach of data hiding technique is proposed to hide the data in both Region of Interest (ROI) of medical images and Non Region of Interest (NROI) of medical images and recover the data as well. Medical image and hidden data are recoverable without any loss of data. Keywords: Data hiding, Medical images, ROI, NROI, LSB

Enhanced Data Security and Integrity using Contourlet Transform for Medical Images

Objective: The objective of this framework is to analyze the security, compression ratio and integrity by using segmentation algorithm and transformation to extract the tumor region from MRI brain image and to observe its performance metrics. Methods/Statistical Analysis: This paper offers a futuristic healthcare solution to encompass of segmenting the ROI using Bhattacharya coefficient algorithms and successively applying the modified EMD steganography method centered on contourlet transform. This framework also challenges to verify the integrity of ROI using SHA-1, precisely senses any variation in ROI, furthermore, it ensures robustness of the entrenched data in non-region of interest and mends ROI perfectly for investigation. Lastly the whole image is encrypted with modified Logistic map encryption in order to afford overall security. Findings: This work recapitulates the contrast of distinct embedding algorithms viz. Least Significant Bit - Discrete Cosine Transform (LSB-DCT) and no-shrinkage F5-Integer Wavelet Transform (nsF5-IWT) with Improved Exploiting Modification Direction-Contourlet Transform (IEMD-CT) for embedding processes. The performance analysis of integrity check during transmission is verified using Secure Hash Algorithm 1 (SHA-1). Experimental endings deliberate Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE), Bit Error Rate (BER), Signal to Noise Ratio (SNR) as performance metrics and found the effectiveness of the proposed framework over conventional methods. Application/Improvements: This framework can be exploited in telemedicine applications in order to obtained with meticulousness in tumor location for effective healthcare services. Keywords: Brain Tumor, Confidentiality, Contourlet Transform, Integrity, MRI Image, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Tumor Extraction

Region of Interest-Based Tamper Detection and Lossless Recovery Watermarking Scheme (ROI-DR) on Ultrasound Medical Images.

Tampering on medical image will lead to wrong diagnosis and treatment, which is life-threatening; therefore, digital watermarking on medical image was introduced to protect medical image from tampering. Medical images are divided into region of interest (ROI) and region of non-interest (RONI). ROI is an area that has a significant impact on diagnosis, whereas RONI has less or no significance in diagnosis. This paper has proposed ROI-based tamper detection and recovery watermarking scheme (ROI-DR) that embeds ROI bit information into RONI least significant bits, which will be extracted later for authentication and recovery process. The experiment result has shown that the ROI-DR has achieved a good result in imperceptibility with peak signal-to-noise ratio (PSNR) values approximately 48dB, it is robust against various kinds of tampering, and the tampered ROI was able to recover to its original form. Lastly, a comparative table with the previous research (TALLOR and TALLOR-RS watermarking schemes) has been derived, where these three watermarking schemes were tested under the same testing conditions and environment. The experiment result has shown that ROI-DR has achieved speed-up factors of 22.55 and 26.65 in relative to TALLOR and TALLOR-RS watermarking schemes, respectively.

SVM based robust watermarking for enhanced medical image security

Medical images are more typical than any other ordinary images, since it stores patient’s information for diagnosis purpose. Such images need more security and confidentiality as total diagnosis depends on it. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data. The embedded data is scrambled using a unique key and then a transform domain based hybrid watermarking technique is used to embed the scrambled data into the transform coefficients of the host image. The data embedding in medical images involves more attention, so that the diagnosis part must not be affected by any modification. Therefore, Support Vector Machine (SVM) is used as a classifier, which classify a medical image into two regions i.e. Non Region of Interest (NROI) and Region of Interest (ROI) to embed watermark data into the NROI part of the medical image, using the proposed embedding algorithm. The objective of the proposed model is to avoid any quality degradation to the medical image. The simulation is performed to measure the Peak Signal to Noise Ratio (PSNR) for imperceptibility and Structural Similarity Index (SSIM) to test the robustness. The experimented result shows, robustness and imperceptibility with SSIM of more than 0.50 and PSNR of more than 35 dB for proposed watermarking model.

Medical Image Compression Using Roi and Nroi Images

Advanced medical imaging requires storage space of huge quantities of digitized clinical data. Due to the inhibited bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, transform coding is one of the most effective strategies. Image data in spatial domain will be changed into spectral domain after the transformation to attain more compression gains. In our proposed system, separation of the original image in to Region of Interest (ROI) and Non-Region of Interest (NROI) parts is done. Then Lossless compression with Set Partition in Hierarchical Trees (SPIHT) algorithm is used to compress the ROI part and Lossy compression with Haar wavelet transform is used to compress the NON-ROI part. This algorithm gives better Peak Signal Noise Ratio (PSNR)and Bits per pixel (BPP) value for medical images.

Novel Variants of a Histogram Shift-Based Reversible Watermarking Technique for Medical Images to Improve Hiding Capacity.

In telemedicine systems, critical medical data is shared on a public communication channel. This increases the risk of unauthorised access to patient's information. This underlines the importance of secrecy and authentication for the medical data. This paper presents two innovative variations of classical histogram shift methods to increase the hiding capacity. The first technique divides the image into nonoverlapping blocks and embeds the watermark individually using the histogram method. The second method separates the region of interest and embeds the watermark only in the region of noninterest. This approach preserves the medical information intact. This method finds its use in critical medical cases. The high PSNR (above 45 dB) obtained for both techniques indicates imperceptibility of the approaches. Experimental results illustrate superiority of the proposed approaches when compared with other methods based on histogram shifting techniques. These techniques improve embedding capacity by 5–15% depending on the image type, without affecting the quality of the watermarked image. Both techniques also enable lossless reconstruction of the watermark and the host medical image. A higher embedding capacity makes the proposed approaches attractive for medical image watermarking applications without compromising the quality of the image.

Squint Pixel Steganography

Technology is playing a major role in the rapid growth of Techno media in relation to information security. Tampers are a major handicap while transferring medical images. In order to circumvent these issues used Steganography to hide the information inside a cover medium with different carrier formats. In this paper, the author proposes a novel squint pixel based medical image steganography technique to avoid distortion by an attacker. In this method, Original medical image itself acts as a carrier image. A Medical image segmented into two sets of pixels, Region of interest (ROI) and squint pixels of region of non-interest (RONI). The authentic data and information of ROI of a medical image embedded in penultimate and least significant bits (PLSB) of squint pixels of RONI. Results of experiments on various medical images show that the proposed method produces high quality stego medical images with high accuracy and recovery of ROI data without loss.

Region-based hybrid medical image watermarking for secure telemedicine applications

In this paper we propose a novel region based hybrid medical image watermarking (MIW) scheme to ensure authenticity, integrity and confidentiality of medical images. In this scheme a digital medical image is partitioned into region of interest (ROI) and the region of non interest (RONI). To detect and localize ROI tampering with high accuracy pixel wise positional and relational bits are calculated. Positional bit is calculated with respect to MSBs, row and column of the pixel. Relational bit shows the relation between MSBs. Two original LSBs of each ROI pixel are replace by their corresponding positional and relational bits. Original LSBs of ROI pixels are concatenated and embedded in RONI for ROI recovery in the case of tampering. Multiple watermarks i.e. electronic patient record (EPR), hospitals logo and LSBs of ROI are embedded simultaneously as a robust watermark in RONI using IWT-SVD hybrid transform. The proposed scheme is blind and free from false positive detection. Various experiments have been carried out on different medical imaging modalities to evaluate the performance of the proposed scheme in terms of imperceptibility, robustness, tamper detection, localization, recovery and computation time. ROI tampering is detected and recovered with high accuracy. Thus, the proposed scheme is effective in telemedicine applications.

A new hybrid watermarking algorithm for MRI medical images using DWT and hash functions.

This paper deals with a blind hybrid digital watermarking algorithm for the copyright protection and authentication of magnetic resonance tomography images. Medical image watermarking requires extreme care when embedding watermark information in the medical images, to protect the image quality from being violated and to avoid the wrong diagnosis that might occur. The proposed algorithm contains robust watermark for the ownership protection and fragile watermarks for checking the authenticity. In the proposed algorithm, the medical image is divided into two regions, called the Region of Interest and Region of Non-Interest using histogram technique and the watermarks are embedded in both wavelet domain and spatial domain in the Region of Non-Interest Area. The proposed hybrid watermarking technique was successfully tested on a variety of MRI medical images and offerd high peak signal to noise ratio and similarity structure index measure values.

High-capacity multiple regions of interest watermarking for medical images

Watermarking techniques have been widely used for copyright protection, broadcast monitoring, and data authentication in medical images to protect patient's private information against tampering by unauthorized persons. A region of interest (ROI) in a medical image contains valuable diagnostic information, and thus must be preserved to avoid medical misdiagnosis. Current state-of-the-art medical image watermarking techniques only allow single-ROI data preservation. Aiming for high embedding capacity and high image fidelity, we present a novel medical image watermarking scheme which allows multiple ROIs to be selected and kept lossless, whereas regions of non-interest (RONIs) are collated for watermark embedding. The watermark is compressed, and the embedding is conducted in frequency domain to avoid piracy. Experimental results show that the proposed technique has significantly achieved high-capacity and high-PSNR (peak signal-to-noise ratio) as compared against eight existing similar techniques. In addition, our technique allows flexible adjustment on a variable that controls the tradeoff between image fidelity and embedding capacity.

A robust medical image watermarking against salt and pepper noise for brain MRI images

The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. During the transmission of medical images between hospitals or specialists through the network, the main priority is to protect a patient's documents against any act of tampering by unauthorised individuals. Because of this, there is a need for medical image authentication scheme to enable proper diagnosis on patient. In addition, medical images are also susceptible to salt and pepper impulse noise through the transmission in communication channels. This noise may also be intentionally used by the invaders to corrupt the embedded watermarks inside the medical images. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this work addresses the issue of designing a new watermarking method that can withstand high density of salt and pepper noise for brain MRI images. For this purpose, combination of a spatial domain watermarking method, channel coding and noise filtering schemes are used. The region of non-interest (RONI) of MRI images from five different databases are used as embedding area and electronic patient record (EPR) is considered as embedded data. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER).

A ROI-based reversible data hiding scheme in encrypted medical images

A novel ROI-based reversible data hiding scheme in encrypted medical images is proposed. Firstly, a content owner partitions an original medical image into the region of interest (ROI) and the region of noninterest (RONI), and then encrypts the image using an encryption key. A data-hider concatenates the least significant bits (LSB) of the encrypted ROI and Electronic Patient Record (EPR), and then embeds the concatenated data into the encrypted image by LSB substitution algorithm. With the encrypted medical image containing the embedded data, the receiver can extract the embedded data with the data-hiding key; if the receiver has the encryption key, a medical image similar to the original image can be obtained by directly decrypting the encrypted medical image; if the receiver has both the data-hiding key and the encryption key, the embedded data can be extracted without any error and ROI can be losslessly recovered after extracting the embedded data.

Sensitive Patient Data Hiding using a ROI Reversible Steganography Scheme for DICOM Images.

The exchange of medical images over the Internet has evoked significant interest over the past few years due to the introduction of web and cloud based medical information systems. The protection of sensitive data has always been a key indicator in the performance of such systems. In this context, this work presents an algorithm developed for Digital Imaging and Communications in Medicine (DICOM) medical images, which applies secret-sharing steganography methods for ensuring the integrity of sensitive patient data as well as the important parts of the image. In the proposed algorithm, images are divided into two parts: the region of interest (ROI) and the region of non interest (RONI). Patient data and integrity hashes are positioned inside the ROI while the information (map) needed to recover the ROI before insertion is positioned in the RONI. Security of the extraction process is assured through the use of cryptography. The experimental results prove that the original (cover) images and the stego images provide an excellent visual equality result in terms of PSNR. Furthermore, they prove that the proposed scheme can be efficiently used as a steganography scheme in DICOM images with limited smooth areas.

Iris based secure NROI multiple eye image watermarking for teleophthalmology

This paper presents a new secure multiple text and image watermarking scheme on cover eye image using fusion of discrete wavelet transforms (DWT) and singular value decomposition (SVD) for Teleophthalmology. Secure Hash Algorithm (SHA-512) is used for generating hash corresponding to iris part of the cover digital eye image and this unique hash parameter is used for enhancing the security feature of the proposed watermarking technique. Simultaneous embedding of four different watermarks (i.e. Signature, index, caption and reference watermark) in form of image and text using fusion of discrete wavelet transforms (DWT) and singular value decomposition (SVD) is achieved in this paper. The suggested technique initially divides the digital eye image into Region of interest (ROI) containing iris and Non-Region of interest (NROI) part where the text and image watermarks are embedded into the Non-Region of interest (NROI) part of the DWT cover image. The selection of DWT decomposition level for embedding the text and image watermarks depends on size, different characteristics and robustness requirements of medical watermark. The performance in terms of Normalized Correlation (NC) and bit error rate (BER) of the developed scheme is evaluated and analyzed against known signal processing attacks and `Checkmark' attacks. The method is found to be robust against all the considered attacks. The proposed multilevel watermarking method correctly extracts the embedded watermarks without error and is robust against the all considered attacks without significant degradation of the medical image quality of the watermarked image. Therefore the proposed method may find potential application in secure and compact medical data transmission for teleophthalmology applications.

Security in Telemedicine using DWT-CDCS

protection of digital media is the very first application that comes to mind for digital watermarking. In the past, duplicating artwork was quite complicated and requires great efforts to create the work looks just like the original. However, in present digital world it is very simple for anyone to duplicate or manipulate digital data. The digital image watermarking allows the watermark to be embedded visibly or invisibly in the original image for identification of the owner. This concept can also be used for other media, such as digital video and audio. Telemedicine is a well-known application of digital watermarking. In this application security & authentication for medical data is very important. Hiding the data into the medical image provide the security over the public network. Authentication verifies whether the image certainly belongs to the right patient Authentication of medical data used for further diagnosis and reference. This paper focused on the methods of medical image data hiding for security and authentication. High capacity data hiding is achieved with CDCS (Class Dependent Coding Scheme). As well as data is effectively hided with discrete wavelet transform. Embedding watermarks in RONI (region of non interest) protects the ROI (region of interest) of medical image, which is diagnostically important part of medical images. Segmentation plays an important role in medical image processing for separating the ROI from medical image.

Quality optimized medical image information hiding algorithm that employs edge detection and data coding

ObjectivesThe present work has the goal of developing a secure medical imaging information system based on a combined steganography and cryptography technique. It attempts to securely embed patient's confidential information into his/her medical images. MethodsThe proposed information security scheme conceals coded Electronic Patient Records (EPRs) into medical images in order to protect the EPRs’ confidentiality without affecting the image quality and particularly the Region of Interest (ROI), which is essential for diagnosis. The secret EPR data is converted into ciphertext using private symmetric encryption method. Since the Human Visual System (HVS) is less sensitive to alterations in sharp regions compared to uniform regions, a simple edge detection method has been introduced to identify and embed in edge pixels, which will lead to an improved stego image quality. In order to increase the embedding capacity, the algorithm embeds variable number of bits (up to 3) in edge pixels based on the strength of edges. Moreover, to increase the efficiency, two message coding mechanisms have been utilized to enhance the ±1 steganography. The first one, which is based on Hamming code, is simple and fast, while the other which is known as the Syndrome Trellis Code (STC), is more sophisticated as it attempts to find a stego image that is close to the cover image through minimizing the embedding impact. The proposed steganography algorithm embeds the secret data bits into the Region of Non Interest (RONI), where due to its importance; the ROI is preserved from modifications. ResultsThe experimental results demonstrate that the proposed method can embed large amount of secret data without leaving a noticeable distortion in the output image. The effectiveness of the proposed algorithm is also proven using one of the efficient steganalysis techniques. ConclusionThe proposed medical imaging information system proved to be capable of concealing EPR data and producing imperceptible stego images with minimal embedding distortions compared to other existing methods. In order to refrain from introducing any modifications to the ROI, the proposed system only utilizes the Region of Non Interest (RONI) in embedding the EPR data.

Watermarking of ultrasound medical images in teleradiology using compressed watermark

The open accessibility of Internet-based medical images in teleradialogy face security threats due to the nonsecured communication media. This paper discusses the spatial domain watermarking of ultrasound medical images for content authentication, tamper detection, and lossless recovery. For this purpose, the image is divided into two main parts, the region of interest (ROI) and region of noninterest (RONI). The defined ROI and its hash value are combined as watermark, lossless compressed, and embedded into the RONI part of images at pixel's least significant bits (LSBs). The watermark lossless compression and embedding at pixel's LSBs preserve image diagnostic and perceptual qualities. Different lossless compression techniques including Lempel-Ziv-Welch (LZW) were tested for watermark compression. The performances of these techniques were compared based on more bit reduction and compression ratio. LZW was found better than others and used in tamper detection and recovery watermarking of medical images (TDARWMI) scheme development to be used for ROI authentication, tamper detection, localization, and lossless recovery. TDARWMI performance was compared and found to be better than other watermarking schemes.

Client Server Architecture for Embedding Patient Information on X-Ray Images

Now a day’s data is increasing exponentially, maintaining the data is very important part of daily processing. In almost every sector i.e. medical, educational, Government sector and so on. In the proposed paper we are focusing on the medical sector data reduction. Objective : To develop a technique for embedding patient information on the X-Ray image by using image segmentation techniques. Methods : The image segmentation is done by dividing the two regions named as Region Of Interest (ROI) and Region Of Non Interest (RONI), and then applying Least Significant Bits (LSB) algorithm for information embedding. Results: The patient information is embedded into the X-Ray image itself and no separate records are maintained for the same. Conclusion: The proposed technique is efficient in reducing the size and maintenance of large databases in Hospital and Clinical Laboratories.