Medical Image Encryption

The healthcare industry has been facing a lot of challenges in securing electronic health records (EHR). Medical images have found a noteworthy position for diagnosis leading to therapeutic requirements. Millions of medical images of various modalities are generally safeguarded through software-based encryption. DICOM format is a widely used medical image type. In this chapter, DICOM image encryption implemented on cyclone FPGA and ARM microcontroller platforms is discussed. The methodology includes logistic map, DNA coding, and LFSR towards a balanced confusion – diffusion processes for encrypting 8-bit depth 256 × 256 resolution of DICOM images. For FPGA realization of this algorithm, the concurrency feature has been utilized by simultaneous processing of 128 × 128 pixel blocks which yielded a throughput of 79.4375 Mbps. Noticeably, the ARM controller which replicated this approach through sequential embedded “C” code took 1248 bytes in flash code memory and Cyclone IV FPGA consumed 21,870 logic elements for implementing the proposed encryption scheme with 50 MHz operating clock.

Bernoulli's Chaotic Map-Based 2D ECG Image Steganography

Signal processing technology comprehends fundamental theory and implementations for processing data. The processed data is stored in different formats. The mechanism of electrocardiogram (ECG) steganography hides the secret information in the spatial or transformed domain. Patient information is embedded into the ECG signal without sacrificing the significant ECG signal quality. The chapter contributes to ECG steganography by investigating the Bernoulli's chaotic map for 2D ECG image steganography. The methodology adopted is 1) convert ECG signal into the 2D cover image, 2) the cover image is loaded to steganography encoder, and 3) secret key is shared with the steganography decoder. The proposed ECG steganography technique stores 1.5KB data inside ECG signal of 60 seconds at 360 samples/s, with percentage root mean square difference of less than 1%. This advanced 2D ECG steganography finds applications in real-world use which includes telemedicine or telecardiology.

Optimization Techniques for the Multilevel Thresholding of the Medical Images

Multilevel thresholding is segmenting the image into several distinct regions. Medical data like magnetic resonance images (MRI) contain important clinical information that is crucial for diagnosis. Hence, automatic segregation of tissue constituents is of key interest to clinician. In the chapter, standard entropies (i.e., Kapur and Tsallis) are explored for thresholding of brain MR images. The optimal thresholds are obtained by the maximization of these entropies using the particle swarm optimization (PSO) and the BAT optimization approach. The techniques are implemented for the segregation of various tissue constituents (i.e., cerebral spinal fluid [CSF], white matter [WM], and gray matter [GM]) from simulated images obtained from the brain web database. The efficacy of the thresholding technique is evaluated by the Dice coefficient (Dice). The results demonstrate that Tsallis' entropy is superior to the Kapur's entropy for the segmentation CSF and WM. Moreover, entropy maximization using BAT algorithm attains a higher Dice in contrast to PSO.

Medical Data Are Safe

In this internet era, transferring and preservation of medical diagnostic reports and images across the globe have become inevitable for the collaborative tele-diagnosis and tele-surgery. Consequently, it is of prime importance to protect it from unauthorized users and to confirm integrity and privacy of the user. Quantum image processing (QIP) paves a way by integrating security algorithms in protecting and safeguarding medical images. This chapter proposes a quantum-assisted encryption scheme by making use of quantum gates, chaotic maps, and hash function to provide reversibility, ergodicity, and integrity, respectively. The first step in any quantum-related image communication is the representation of the classical image into quantum. It has been carried out using novel enhanced quantum representation (NEQR) format, where it uses two entangled qubit sequences to hoard the location and its pixel values of an image. The second step is performing transformations like confusion, diffusion, and permutation to provide an uncorrelated encrypted image.

Electrocardiogram Beat Classification Using BAT-Optimized Fuzzy KNN Classifier

In this chapter, the BAT-optimized fuzzy k-nearest neighbor (FKNN-BAT) algorithm is proposed for discrimination of the electrocardiogram (ECG) beats. The five types of beats (i.e., normal [N], right bundle block branch [RBBB], left bundle block branch [LBBB], atrial premature contraction [APC], and premature ventricular contraction [PVC]) are taken from MIT-BIH arrhythmia database for the experimentation. Thereafter, the features are extracted from five type of beats and fed to the proposed BAT-tuned fuzzy KNN classifier. The proposed classifier achieves the overall accuracy of 99.88%.

Shielding the Confidentiality, Privacy, and Data Security of Bio-Medical Information in India

The framers of Indian Constitution were very much cognizant about the significance of human nobility and worthiness and hence they incorporated the “right to life and personal liberty” in the Constitution of India. Right to life is considered as one of the primordial fundamental rights. There is no doubt that Indian Judiciary has lived up to the expectations of the Constitution framers, both in interpreting and implementing Article 21 initially, but there are still a few complications left as to the viability of Article 21 in modern times. Looking at the wider arena of right to life, it can be articulated that broader connotation of “right to life” aims at achieving the norms of “privacy” as well.

Medical Data Security Tools and Techniques in E-Health Applications

The primary objective of this chapter is to analyze the existing tools and techniques for medical data security. Typically, medical data includes either medical signals such as electrocardiogram, electroencephalogram, electromyography, or medical imaging like digital imaging and communications in medicine, joint photographic experts group format. The medical data are sensitive, subject to privacy preservation, and data access rights. Security in e-health field is an integrated concept which includes robust combination of confidentiality, integrity, and availability of medical data. Confidentiality ensures the data is inaccessible to unauthorized access. Integrity restricts the alteration in data by the unauthorized user. Whereas availability provides the readiness of the data when needed by the authorized user. Additionally, confidentiality, integrity and availability, accountability parameter records the back action list which answers the why, when, what, and whom data is accessed. The selected tools and techniques used in medical data security in e-health applications is discussed.

Healthcare Informatics Using Modern Image Processing Approaches

Medical image segmentation is the first venture for abnormal state image analysis, significantly lessening the multifaceted nature of substance investigation of pictures. The local region-based active contour may have a few burdens. Segmentation comes about to intensely rely on the underlying shape choice which is an exceptionally capable errand. In a few circumstances, manual collaborations are infeasible. To defeat these deficiencies, the proposed method for unsupervised segmentation of viewer's consideration object of medical images given the technique with the help of the shading boosting Harris finder and the center saliency map. Investigated distinctive techniques to consider the image data and present a formerly utilized energy-based active contour method dependent on the choice of high certainty forecasts to allocate pseudo-names consequently with the point of diminishing the manual explanations.

Advancements in Data Security and Privacy Techniques Used in IoT-Based Hospital Applications

The risk of encountering new diseases is on the rise in medical centers globally. By employing advancements in medical sensors technology, new health monitoring programs are being developed for continuous monitoring of physiological parameters in patients. Since the stored medical data is personal health record of an individual, it requires delicate and secure handling. In wireless transmission networks, medical data is disposed of to avoid loss due to alteration, eavesdropping, etc. Hence, privacy and security of the medical data are the major considerations during wireless transfer through Medical Sensor Network of MSNs. This chapter delves upon understanding the working of a secure monitoring system wherein the data could be continuously observed with the support of MSNs. Process of sanctioning secure data to authorized users such as physician, clinician, or patient through the key provided to access the file are also explained. Comparative analysis of the encryption techniques such as paillier, RSA, and ELGamal has been included to make the reader aware in selecting a useful technique for a particular hospital application.

Implementation and Performance Assessment of Biomedical Image Compression and Reconstruction Algorithms for Telemedicine Applications

Compression serves as a significant feature for efficient storage and transmission of medical, satellite, and natural images. Transmission speed is a key challenge in transmitting a large amount of data especially for magnetic resonance imaging and computed tomography scan images. Compressive sensing is an optimization-based option to acquire sparse signal using sub-Nyquist criteria exploiting only the signal of interest. This chapter explores compressive sensing for correct sensing, acquisition, and reconstruction of clinical images. In this chapter, distinctive overall performance metrics like peak signal to noise ratio, root mean square error, structural similarity index, compression ratio, etc. are assessed for medical image evaluation by utilizing best three reconstruction algorithms: basic pursuit, least square, and orthogonal matching pursuit. Basic pursuit establishes a well-renowned reconstruction method among the examined recovery techniques. At distinct measurement samples, on increasing the number of measurement samples, PSNR increases significantly and RMSE decreases.