Sheli Sinha Chaudhuri

Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search

Authentication is very important in validating a medical content in the domain of telemedicine; however, there are many challenges. Accurate verification is paramount, and any misuse of personal information may have serious consequences. Many authentication processes tried to design various methods to minimise such discrepancies. In this current work, we propose a new approach to design a robust biomedical content authentication system by embedding logo of the hospital within the electrocardiogram signal by means of both discrete wavelet transformation and cuckoo search CS. An adaptive meta-heuristic cuckoo search is used to find the optimal scaling factor settings for logo embedding. Results show that the proposed method can serve as a secure and accurate authentication system.

A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution

Abstract We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding techniques are widely popular for segmenting grayscale images. Color image segmentation is still a challenging field as it involves 3-D histogram unlike the 1-D histogram of grayscale images. Effectiveness of entropy based multi-level thresholding for color image is yet to be explored and this paper presents a humble contribution in this context. We have used differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The proposed method is evaluated by comparing it with seven other prominent algorithms both qualitatively and quantitatively using a well known benchmark suite – the Barkley Segmentation Dataset (BSDS300) with 300 distinct images. Such comparison reflects the efficiency of our algorithm

Particle Swarm Optimization based parameter optimization technique in medical information hiding

In this era of globalization, use of technology has influenced medical science as well. Now-a-days, exchanging medical information using communication technologies like network devices or telecommunication to provide health care services for medical case studies amongst various diagnostic centers or hospitals is a very common practice. In this paper, a Discrete Wavelet Transformation (DWT) based method is proposed for embedding a Hospital Logo or Electronic Patient Record (EPR), where the embedding factors/scaling factors are optimized by Particle Swarm Optimization (PSO).

Hyper-spectral image segmentation using Rényi entropy based multi-level thresholding aided with differential evolution

Unsupervised classification of land cover study of hyper-spectral satellite images.A multi-level Renyi entropy based image thresholding scheme is presented.Multi-level thresholding is formulated as optimization problem and solved with DE.Composite kernel based classification approach using Support Vector Machine (SVM).Very competitive performance on popular hyper-spectral imagery like ROSIS and AVRIS. This article presents a novel approach for unsupervised classification of land cover study of hyper-spectral satellite images to improve separation between objects and background by using multi-level thresholding based on the maximum Renyi entropy (MRE). Multi-level thresholding, which partitions a gray-level image into several distinct homogeneous regions, is a widely popular tool for segmentation. However, utility of multi-level thresholding is yet to be investigated in challenging applications like hyper-spectral image analysis. Differential Evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques. In addition, the outcomes of the MRE-based thresholding are employed to train a Support Vector Machine (SVM) classifier via the composite kernel approach to improve the classification accuracy. The final outcomes are tested on popular hyper-spectral imagery like ROSIS and AVRIS sensors. The effectiveness of the proposed algorithm is evaluated through qualitative and quantitative comparison with other state-of-the-art global optimizers.

Feature analysis for the blind-watermarked electroencephalogram signal in wireless telemonitoring using Alattar's method

The present medical era has seen quite a considerable amount of work been done in tele-monitoring that involves transmission of biomedical signals through wireless media. Exchange of information amongst various hospital systems and medical centers require high level of reliability and security. Signal integrity can be verified, authenticity and achieved control over the copy process can be proved by adding watermark in the original information as multimedia content. Electroencephalography (EEG) is a medical test that records the electrical activity originating from the brain. In this present work, Alattars Method is used for watermark insertion and extraction in an EEG signal without devalorizing its diagnostic parameters. In the second part of the paper, different features in the time domain and the spatial domain are obtained from the original EEG signal, watermarked EEG signal, and the recovered EEG signal.

Feature analysis for the reversible watermarked electrooculography signal using Low distortion Prediction-error Expansion

At present, most of the hospitals and diagnostic centers globally, have started using wireless media for exchange of biomedical information (Electronic Patient Report or hospital logo) for mutual availability of therapeutic case studies. Exchange of information amongst various hospital and medical centers require high level of reliability and security. Signal integrity can be verified, authenticity and achieved control over the copy process can be proved by adding watermark in the original information as multimedia content. Electrooculography (EOG) is a medical test that records the movements and position of the eyes. In this present work, Low distortion Prediction-error Expansion technique is used for watermark insertion and extraction in an EOG signal without devalorizing its diagnostic parameters. It can be seen that in this approach the correlation value of the original watermark and the extracted watermark is quite high. The Signal-to-Noise ratio (SNR) between the original EOG signal and the recovered EOG signal markedly improves which claims the robustness of the method. In the second part of the present work different features of the original EOG signal, watermarked EOG signal and recovered EOG signal are analysed.

DWT-DCT-SVD based blind watermarking technique of gray image in electrooculogram signal

At present most of the hospitals and diagnostic centers globally, use wireless media to exchange biomedical information for mutual availability of therapeutic case studies. The required level of security and authenticity for transmitting biomedical information through the internet is quite high. Level of security can be increased; authenticity of the information can be verified and control over the copy process can be ascertained by adding watermark as “ownership” information in multimedia content. In this proposed method different types of gray scale biomedical images can be used as added ownership (watermark) data. Electrooculography is a medical test used by the ophthalmologists for monitoring eyeball movement in Rapid Eye Movement (REM) and non-REM sleep, to detect the disorders of human eyes and to measure the resting potential of the eye. In this present work 1-D EOG signal is transformed into 2-D signal. DWT, DCT, SVD are applied on the transformed 2D signal to embed watermark in it. Extraction of watermark image is done by applying inverse DWT, inverse DCT and SVD. The Peak Signal to Noise Ratio (PSNR) of the original EOG signal vs. watermarked signal and the correlation value between the original and extracted watermark image are calculated to prove the efficacy of the proposed method.

Tamper detection of electrocardiographic signal using watermarked bio–hash code in wireless cardiology

The current globalised era is marked with a rapid increase in the use of wireless media to exchange information over globally distributed locations. This advancement and growth of technologically mediated information helps to provide medical care from a distant location by exchanging biomedical information amongst various hospitals and diagnostic centres across the world. However, while transmitting, the medical information becomes highly vulnerable to miscellaneous attacks like tampering and hacking. A watermark is added in the Electrocardiographic (ECG) signal to increase the level of security to help protect the integrity of the data and decrease the chances of wrong diagnosis. In this current work, a technique is proposed to detect undesirable modifications, if present, in a transmitted biomedical ECG signal. The proposed method is based on bio–hashing and reversible watermarking techniques.

Interfacial magnetism and exchange coupling in BiFeO3–CuO nanocomposite

Ferromagnetic BiFeO3 nanocrystals of average size 9 nm were used to form a composite with antiferromagnetic CuO nanosheets, with the composition (x)BiFeO3/(100-x)CuO, x = 0, 20, 40, 50, 60, 80 and 100. The dispersion of BiFeO3 nanocrystals into the CuO matrix was confirmed by x-ray diffraction and transmission electron microscopy. The ferromagnetic ordering as observed in pure BiFeO3 occurs mainly due to the reduction in the particle size as compared to the wavelength (62 nm) of the spiral modulated spin structure of the bulk BiFeO3. Surface spin disorder of BiFeO3 nanocrystals gives rise to an exponential behavior of magnetization with temperature. Strong magnetic exchange coupling between the BiFeO3 nanocrystal and the CuO matrix induces an interfacial superparamagnetic phase with a blocking temperature of about 80 K. Zero field and field cooled magnetizations are analyzed by a ferromagnetic core and disordered spin shell model. The temperature dependence of the calculated saturation magnetization exhibits three magnetic contributions in three temperature regimes. The BiFeO3/CuO nanocomposites reveal an exchange bias effect below 170 K. The maximum exchange bias field HEB is 1841 Oe for x = 50 at 5 K under field cooling of 50 kOe. The exchange bias coupling results in an increase of coercivity of 1934 Oe at 5 K. Blocked spins within an interfacial region give rise to a remarkable exchange bias effect in the nanocomposite due to strong magnetic exchange coupling between the BiFeO3 nanocrystals and the CuO nanosheets.

A hybrid reversible watermarking technique for color biomedical images

In the field of medical diagnosis, exchange of information amongst various hospitals and diagnostic centres for mutual availability of diagnostic and therapeutic case studies is quite common. During digital image transportation, some information needs to be added or hidden in order to identify the owner of the data in multimedia content. EPR (electronic patient record) or hospital logo can be hidden within a bio medical image for high security instead of transferring the EPR/logo through the internet. In this present work a reversible watermarking method (Odd-Even Method) is used for watermark insertion and extraction in a bio medical image with large data hiding capacity, security as well as high watermarked quality. It can be seen that in this approach the correlation value of the original watermark and the extracted watermark is 1 and the experimental results demonstrate that, no matter how much secret data is embedded, Peak Signal-to-Noise Ratio (PSNR) is high enough which clams the robustness of the method.