Region Of Non-interest Research Articles

Embedding in medical images with contrast enhancement and tamper detection capability

This paper presents an efficient data hiding technique capable of providing improved visual quality of watermarked images, besides having the ability to detect the tamper, if any. It is a spatial domain approach in which major emphasis is on improving the visual quality rather than increasing the PSNR or the embedding capacity. The medical images have been divided into Region of Interest (ROI) and Non-Region of Interest (NROI). Bringing out details that lie within the low dynamic range is very important in medical images for effective diagnosis. ROI being diagnostically critical region is enhanced using contrast stretching and subsequently, data is reversibly embedded into the peak bins of ROI. Only those peak bins are employed for reversible data embedding that have an adjacent empty bin to overcome the problem of overflow and underflow. In NROI, the uniform intensity and redundant information region, Least Significant Bit (LSB) embedding is employed for increasing the payload. For tamper detection, a fragile watermark has been embedded in the ROI. To evaluate the scheme various parameters like peak signal to noise ratio (PSNR), No-Reference Quality Metric for contrast-distorted images (NR-CDIQA) and Structural Similarity Index Matrix (SSIM) have been calculated. The experimental results show a remarkable increase in visual quality compared to state-of-art.

Multipurpose Watermarking Algorithm for Medical Images

Considering the existing medical image watermarking algorithms, a single function often has limitations, and a multipurpose watermarking algorithm for medical images is proposed. First, medical images are divided into regions of interest (ROIs) and regions of noninterest (RONIs). Then, the authentication watermark produced for each subblock of the ROI is embedded into the corresponding mapping subblock. The visible watermark is embedded into the RONI, and, finally, the watermark information and constructed authentication information in each subblock of the ROI are embedded into the corresponding RONI subblock. Simulation results show that the embedded visible watermark can protect and facilitate medical image management. In addition, the proposed algorithm has strong robustness and very good visual quality. It can simultaneously realize copyright protection and content authentication and also has high tamper localization capability.

Prediction-Based Reversible Watermarking of CT Scan Images for Content Authentication and Copyright Protection

In the application of teleradiology, radiologists located at different geographical locations around the globe, exchange Computed Tomography (CT) scan images along with relevant patient information for diagnosis as well as therapeutic measures. However, when these images are exchanged over public networks like the Internet, two important issues are created: content authentication and copyright protection. In this paper, a novel high capacity reversible watermarking scheme for CT Scan images is presented which is based on the region of interest (ROI) and region of non-interest (RONI) watermarking. The scheme embeds and extracts the secret data into the input CT scan image as well as restores the image to its pristine state at the receiving end. After segmenting the host image into the ROI and RONI regions, the scheme implants a fragile watermark (FW) into the ROI to check the integrity of the ROI and a robust watermark (RW) into the RONI for copyright protection. The novelty of the proposed system is in two fold. First, it takes advantage of the segmentation algorithm to avoid the additional overhead of information about the vertices usually required at the receiving end in order to select the ROI. Secondly, it takes advantage of the prediction-based reversible watermarking to avoid additional overhead of location map, which is usually required for reversibility in most of the reversible medical image watermarking techniques. Experimental results show that the proposed system outperforms medical image watermarking techniques recently reported in the literature in terms of embedding capacity and imperceptibility of watermarked image.

PIXEL-BASED FOREGROUND DETECTION IN REPETITIVE TIME-SERIES REGION

Currently, many state-of-the-art background subtraction techniques cannot deal properly with the area of periodic changing background, while some continue classifying them as foreground at intervals, others simply mask that area as a non-region of interest. To cope with this issue, a novel method of detecting repetitive temporal patterns based on the image sequences was proposed in this paper. The main emphasis of the proposed approach is on classifying those pixels as a background and identifying foreground objects in their relevant areas. As for the foreground detection, a model of time series pattern found in each pixel is individually built first; and then, any changes beyond the allowance of model periodicity are then determined as foreground objects. The proposed method could be used and run in parallel with any state-of-the-art background subtraction technique, allowing more accurate foreground-background segmentation. Experimental results showed that using Y channel, the proposed method of detecting time-series background area could achieve 92.9% of recall rate with less than 1% false positives. The recall of foreground detection in an area of repetitive time-series pattern was about 87%; while F-measure was about 0.73 on average. The false positives of foreground detection were also less than 1%. Accordingly, the proposed time-delay detection technique could significantly help to suppress the foreground error on time series background area, especially during the change from one sub-pattern to another which causes a camera sensor to capture both sub-pattern values in one frame. Performance comparison with state-of-the-art methods showed that our proposed method was able to reduce 80% of the average false alarm and improve F-measure to 28% while the computational efficiency was reduced by only 1%.

An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine

Maintaining secured patient credentials in telemedicine is becoming a critical task. Image watermarking is one of the solutions to this problem. It is extensively used to protect and block the content alteration. Medical images may acquaint with tampers during transit in telemedicine. Before taking a prior decision about referring for diagnosis, the reliability of region of interest (ROI) of the watermarked medical test image must be tested to avoid faulty diagnosis. In this paper, tamper recognition and authenticity were obtained by concealing the dual watermarks into the region of non-interest (RONI) blocks of the medical image. These blocks are chosen by the characteristics of Human Visual System (HVS) with the integration of Discrete Wavelet Transform (DWT) and Schur transform along with the Particle Swarm Bacterial Foraging Optimization algorithm (PSBFO). The major focus of the PSBFO algorithm is to select the threshold value for obtaining optimum results in terms of imperceptibility and robustness against attacks. The dual watermarks are compressed by Lempel-Ziv-Welch (LZW) lossless compression algorithm to increase the payload capacity. Simulation outcomes conducted on different types of medical images disclose that the proposed scheme demonstrates superior transparency and robustness against signal and compression attacks compared with the related hybrid optimized algorithms. It also recognizes the existence of tampers inside the portion of ROI with 100% precision. The proposed scheme is also able to retrieve the original ROI without losing any information and provides optimum security capability when compared with the state-of-the-art algorithms.

Automated Diagnosis of Cardiovascular Disease Through Measurement of Intima Media Thickness Using Deep Neural Networks.

Ultrasound images(US) of carotid artery aid in the detection and diagnosis of Cardiovascular Diseases (CVD). Traditional methods for analysis of US images employ hand crafted features to classify images, which need expert knowledge for careful design and lack robustness to variations, leading to low sensitivity in clinical applications. Intima Media Thickness (IMT) and elasticity are the predominant markers used for carotid artery (CA) atherosclerotic plaque detection. This paper proposes to address the problem by building Convolutional Neural Network (CNN) for segmentation of intima media complex (ie) Region of Interest (RoI). A dataset consisting of 450 subjects is used to train and validate the proposed CNN. Segmentation is done in the far wall region of the artery from the longitudinal B-mode images enabling atleast 24 RoIs and RoNIs (Region of Non Interest) for each image. The result of 10-fold cross validation shows accuracy of 99.54%. Mean deviation of IMT from manual tracings is found to be 0.06645mm.

MEDICAL IMAGES COMPRESSION BASED ON SPIHT AND BAT INSPIRED ALGORITHMS

There is a significant necessity to compress the medical images for the purposes of communication and storage.Most currently available compression techniques produce an extremely high compression ratio with a high-quality loss. Inmedical applications, the diagnostically significant regions (interest region) should have a high image quality. Therefore, it ispreferable to compress the interest regions by utilizing the Lossless compression techniques, whilst the diagnostically lessersignificant regions (non-interest region) can be compressed by utilizing the Lossy compression techniques. In this paper, a hybridtechnique of Set Partition in Hierarchical Tree (SPIHT) and Bat inspired algorithms have been utilized for Lossless compressionthe interest region, and the non-interest region is loosely compressed with the Discrete Cosine Transform (DCT) technique.The experimental results present that the proposed hybrid technique enhances the compression performance and ratio. Also,the utilization of DCT increases compression performance with low computational complexity.

Secure telemedicine using RONI halftoned visual cryptography without pixel expansion

To provide quality healthcare services worldwide telemedicine is a well-known technique. It delivers healthcare services remotely. For the diagnosis of disease and prescription by the doctor, lots of information is needed to be shared over public and private channels. Medical information like MRI, X-Ray, CT-scan etc. contains very personal information and needs to be secured. Security like confidentiality, privacy, and integrity of medical data is still a challenge. It is observed that the existing security techniques like digital watermarking, encryption are not efficient for real-time use. This paper investigates the problem and provides the solution of security considering major aspects, using Visual Cryptography (VC). The proposed algorithm creates shares for parts of the image which does not have relevant information. All the information which contains data related to the disease is supposed to be relevant and is marked as the region of interest (ROI). The integrity of the image is maintained by inserting some information in the region of non-interest (RONI). All the shares generated are transmitted over different channels and embedded information is decrypted by overlapping (in XOR fashion) shares in θ(1) time. Visual perception of all the results discussed in this article is very clear. The proposed algorithm has performance metrics as PSNR (peak signal-to-noise ratio), SSIM (structure similarity matrix), and Accuracy having values 22.9452, 0.9701, and 99.8740 respectively.

ROI-based reversible watermarking scheme for ensuring the integrity and authenticity of DICOM MR images

Reversible and imperceptible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back. In this paper, a novel blind reversible watermarking approach is presented to detect intentional and unintentional changes within brain Magnetic Resonance (MR) images. The scheme segments images into two parts; the Region of Interest (ROI) and the Region of Non Interest (RONI). Watermark data is encoded into the ROI using reversible watermarking based on the Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realize a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by concealing the data into ‘smooth’ regions inside the ROI and through the elimination of the large location map required for extracting the watermark and retrieving the original image. Our scheme delivers highly imperceptible watermarked images, at 92.18–99.94 dB Peak Signal to Noise Ratio (PSNR) evaluated through implementing a clinical trial based on relative Visual Grading Analysis (relative VGA). This trial defines the level of modification that can be applied to medical images without perceptual distortion. This compares favorably to outcomes reported under current state-of-art techniques. Integrity and authenticity of medical images are also ensured through detecting subsequent changes enacted on the watermarked images. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible approach, that may establish increased trust in the digital medical workflow.

A High-Capacity Reversible Watermarking Scheme Based on Shape Decomposition for Medical Images

We present a high-capacity reversible, fragile, and blind watermarking scheme for medical images in this paper. A bottom-up saliency detection algorithm is applied to automatically locate the multiple arbitrarily-shaped regions of interest (ROIs). The iterative square-production algorithm is developed to generate different sizes of squares for shape decomposition on the regions of noninterest (RONIs). This scheme of combining the frequency-domain watermarking and arbitrarily-shaped ROI methods can significantly increase the watermarking capacity, whereas the embedded image fidelity is preserved. Extensive experiments were carried out on the OASIS medical image dataset, which consists of a cross-sectional collection of 416 subjects, aged from 18 to 96 years old. The results show that the proposed scheme outperforms six existing state-of-the-art schemes in terms of watermarking capacity and embedded image fidelity.

A novel approach to medical image watermarking for tamper detection and recovery of region of interest using block compression and checksum

Effective use of telecommunication and information technology in telemedicine increases the medical services to the patients who are from far away locations. The doctors provide these services by evaluating the patient details & scans like CT Scan, MRI and Ultra Sound. The patient information is exchanged between doctors and patients on a public network which is not safe. In medical image, specific regions are very important to diagnosis known as Region of Interest (ROI) and the rest of the regions are not of much importance known as Region of Non-Interest (RONI). Providing security to the ROI is an important issue hence medical image watermarking is used to transmit the medical images by embedding the ROI into RONI. At the destination, if tampering is found in ROI then recovery of ROI is possible by extracting the ROI from RONI. In the proposed method, the medical image is divided into three parts: BORDER, ROI and RONI. Further the ROI and RONI are divided into blocks and each ROI block is mapped to RONI block by applying division hash function. Lossless block compression technique is applied to each ROI block and embedded the compressed ROI block into mapped RONI block. To provide authenticity to ROI, checksum is calculated for ROI and embed this checksum in BORDER. Again checksum is calculated for each ROI block and placed in mapped RONI blocks. Whether ROI is tampered or not, is to be identified by extracting the checksum from BORDER and if it is tampered then recover the ROI by mapped RONI. The efficiency of the proposed algorithm is estimated by the performance measures mainly Peak Signal to Noise Ratio (PSNR). The proposed method gives good results on average 55 dB of PSNR compared to the previous methods [21] by efficiently compressing the ROI and by checking the authenticity.

Networked medical data sharing on secure medium - A web publishing mode for DICOM viewer with three layer authentication.

Growing demand for e-healthcare across the globe has raised concerns towards the secure and authentication enhanced medical image sharing. One of the services offered by health informatics in hospitals include an user interface through the Local Area Network (LAN) for enabling storage and access of medical records. In this paper, a security enhanced DICOM image sharing over a LAN addressing confidentiality, integrity and authentication has been proposed. Initially, the AES encrypted patient history was combined along with the thumb impression and Quick Response (QR) code of patient ID as watermark. This watermark was encrypted employing Integer Wavelet Transform (IWT), chaotic map and attractors with confusion-diffusion operations. Further, the encrypted watermark was embedded in the selected Region Of Non-Interest (RONI) pixels of DICOM image. Username & unique password credentials, Face identification and FPGA generated One Time Password (OTP) form the three layer authentication scheme for secure DICOM image access through the LAN. Web publishing medium of storing secured DICOM images in cloud has also been addressed in this work. To validate the proposed hybrid crypto-watermarking system, parameters such as key sensitivity, key space, correlation, entropy, histogram, cropping attack, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) were performed and the results obtained have proved the strength of the proposed algorithm against brute force, statistical and cropping attacks.

Robust and Fragile Medical Image Watermarking: A Joint Venture of Coding and Chaos Theories.

A secure spatial domain, hybrid watermarking technique for obtaining watermark (authentication information) robustness and fragility of the host medical image (content integrity) using product codes, chaos theory, and residue number system (RNS) is proposed. The proposed scheme is highly fragile and unrecoverable in terms of the host image, but it is significantly robust and recoverable in terms of the watermark. Altering the medical image may result in misdiagnosis, hence the watermark that may contain patient information and organization logo must be protected against certain attacks. The host medical image is separated into two parts, namely, the region of interest (ROI) and region of noninterest (RONI) using a rectangular region. The RONI part is used to embed the watermark information. Moreover, two watermarks are used: one to achieve authenticity of image and the other to achieve the robustness against both incidental and malicious attacks. Effectiveness in terms of security, robustness, and fragility of the proposed scheme is demonstrated by the simulations and comparison with the other state-of-the-art techniques.

Patient Data Hiding and Integrity Control Using Prediction-Based Watermarking for Brain MRI and CT Scan Images

The increase of using e-Health services enables remote access, communication, and analysis of medical images and data to facilitate medical diagnostics. This has introduced potential security threats to the medical images and data. Therefore, the security of private patient information and medical images becomes an important issue that should be considered. In such a framework, reversible watermarking has been presented to enhance medical image security. Reversible watermarking is an image watermarking category, that has the capability of completely restoring the original image after the hidden data is extracted. In this paper, a reversible watermarking scheme based on adaptive prediction error is introduced, in order to ensure the confidentiality of the patient's information, verify the authenticity of the host image, and localize and recover tampered areas if the image has been modified. This scheme segments the medical image into two segments namely: Region of Interest (ROI) and Region of Non-Interest (RONI). The Patient data and the hash value of the ROI are hidden in ROI. The hash value of the RONI, tamper detection and tamper recovery data are hidden in RONI. To reduce the distortion of ROI, the most diagnostically significant region, each embeddable pixel of ROI is able to conceal one bit and each embeddable pixel of RONI could conceal two bits. From the experimental results conducted on brain MRI and CT scan images, the proposed scheme is capable to accurately locate tempered regions and recover them. Moreover, the reversibility and the high hiding capacity with very good perceptibility are proven.

Medical image region based watermarking for secured telemedicine

Exchange of Medical images over public networks entail a methodology to offer secrecy for the image along with confirmation for image integrity. In this paper, a region-based Firefly optimized algorithm and hybridization of DWT and Schur transforms in conjunction with multiple watermarking is recommended to endow with security requisites such as authenticity of the ownership of medical image besides origin of source for interchange of medical images in telemedicine applications. Secrecy and authenticity are offered by inserting robust multiple watermarks in the region-of-noninterest (RONI) of the medical image by means of a blind method in the discrete wavelet transform and Schur transform (DWT-Schur). The capability of imperceptibility, robustness and payload are the main parameters for the assessment of watermarking algorithm, with MRI, Ultrasound plus X-ray gray-scale medical image modalities. Simulation results make obvious the efficacy of the projected algorithm in offering the essential security benefits for applications allied to telemedicine.

Hybrid Lempel–Ziv–Welch and clipped histogram equalization based medical image compression

Nowadays, the medical images upsurge because of numerous major disease predictions. The medical image size needed vast volumes of memory and taking additional bandwidth for storage as well as transmission. With the aim of decreasing the size of the storage and as well for greater transmission image compression is needed. The prior research presented a completely automatic technique for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks (CNNs), which is skilled end to-end and does not depend on past acquittance of the data. On the other hand it contains problem with storage for the period of the medical image transmission and transmission speed. With the aim of resolving this issue the presented system developed a compression method. In this research, magnetic resonance imaging are pre-processed by means of median filter. The preprocessed image is split into region of interest (ROI) and non region of interest by means of deep fully convolutional networks with Jaccard distance. Subsequent to the ROI segmentation, the ROI edge is taken out and encrypted with Freeman chain coding. At that point the ROI part is compressed by hybrid Lempel–Ziv–Welch and clipped histogram equalization (CHE). In CHE, ideal threshold value is chosen by means of particle swarm optimization technique for enhancing the brightness maintenance. The Non ROI part is compressed by means of enhanced zero tree wavelet (EZW). In this EZW technique, a preliminary threshold is chosen by making use of firefly algorithm. Lastly the decompression is carried out at the receiving end. The experimentation outcomes prove that the presented method attains greater performance when matched up with the previous technique in regard to compression ratio, peak signal to noise ratio and mean square error.

Machine Learning-Based Robust Watermarking Technique for Medical Image Transmitted Over LTE Network

Abstract This paper discusses a safe and secure watermarking technique using a machine learning algorithm. In this paper, the propagation of a watermarked image is simulated over the third-generation partnership project (3GPP)/long-term evolution (LTE) downlink physical layer. The watermark data are scrambled and a transform domain-based hybrid watermarking technique is used to embed this watermark into the transform coefficients of the host image and transmitted over the orthogonal frequency division multiplexing (OFDM) downlink physical layer. Support vector machine (SVM) is used as a classifier for the classification of non-region of interest (NROI) and region of interest (ROI) in a medical image. The result achieved in this experiment revealed that a 10−6 bit error rate (BER) value is realizable for a greater value of signal-to-noise ratio (SNR; i.e. more than 10.4 dB of SNR). The peak SNR (PSNR) of the received cover image is more than 35 dB, which is acceptable for clinical applications.

Underwater Acoustic Image Encoding Based on Interest Region and Correlation Coefficient

It is difficult for the conventional image compression method to achieve good compression effect in the underwater acoustic image (UWAI), because the UWAI has large amount of noise and low correlation between pixel points. In this paper, fractal coding is introduced into UWAI compression, and a fractal coding algorithm based on interest region is proposed according to the importance of different regions in the image. The application problems of traditional quadtree segmentation in UWAIs was solved by the range block segmentation method in the coding process which segmented the interest region into small size and the noninterest region into large size and balanced the compression ratio and the decoded image quality. This paper applies the classification, reduction codebook, and correlation coefficient matching strategy to narrow the search range of the range block in order to solve the problem of the long encoding time and the calculation amount of encoding process is greatly reduced. The experimental results show that the proposed algorithm improves the compression ratio and encoding speed while ensuring the image quality of important regions in the UWAI.

A robust zero-watermarking scheme for tele-ophthalmological applications

Medical images are watermarked with patient details to impose patient identification in tele-medicine applications. Fundus images comprise of fine features like blood vessels, macula and optic disks. Conventional watermarking methods may tamper crucial information in this case. Even minutest change can distort fine features and affect final decision. ROI (Region of Interest) selection approach also fails here as fine features of fundus images are evenly distributed and it’s almost impossible to properly define Region of Interest (ROI) and Region of Non-Interest (RONI) for medical practitioner. Hence arises an imperative necessity of a watermarking system which does not tamper the fundus images and results in no loss of medical information. This paper proposes a zero-watermarking system which uses singular value coefficients in Wavelet domain for generation of unique identification code from fundus images which is strategically combined with patient ID to generate watermark. Proposed technique suits to the specific requirements of fundus images and optimizes the conflicting requirements of traditional watermarking. The technique aims at unique identification, authentication and integrity verification of medical images. Experimental results reveal the robustness of the system against some common image processing attacks and indicate its suitability for secure exchange of medical images for tele-ophthalmological applications and for storage in large distributed medical databases.

A RONI Based Visible Watermarking Approach for Medical Image Authentication.

Nowadays medical data in terms of image files are often exchanged between different hospitals for use in telemedicine and diagnosis. Visible watermarking being extensively used for Intellectual Property identification of such medical images, leads to serious issues if failed to identify proper regions for watermark insertion. In this paper, the Region of Non-Interest (RONI) based visible watermarking for medical image authentication is proposed. In this technique, to RONI of the cover medical image is first identified using Human Visual System (HVS) model. Later, watermark logo is visibly inserted into RONI of the cover medical image to get watermarked medical image. Finally, the watermarked medical image is compared with the original medical image for measurement of imperceptibility and authenticity of proposed scheme. The experimental results showed that this proposed scheme reduces the computational complexity and improves the PSNR when compared to many existing schemes.