Securing DICOM images based on adaptive pixel thresholding approach

This paper presents a novel efficient two-region Selective encryption approach that exploits medical images statistical properties to adaptively segment Digital Imaging and Communications in Medicine (DICOM) images into regions using thresholding technique in the spatial domain. This approach uses adaptive pixel thresholding, in which thresholds for same DICOM modality, anatomy part and pixel intensities' range were extracted off-line. Then, the extracted thresholds were objectively and subjectively evaluated to select the most accurate threshold for the correspondent pixel intensities' range. In the on-line stage, DICOM images were segmented into a Region Of Interest (ROI) and a Region Of Background (ROB) based on their pixels intensities using the adopted thresholds. After that, ROI was encrypted using Advanced Encryption Standard (AES), while ROB was encrypted using XXTEA. The main goal of the proposed approach is to reduce the encryption processing time overhead in comparison with the Naïve approach; where all image pixels are encrypted using AES. The proposed approach aims to achieve a trade-off between processing time and a high level of security. The encryption time of the proposed approach can save up to 60% of the Naïve encryption time for DICOM images with small-medium ROI

Enhanced image encryption scheme with new mapreduce approach for big size images

Achieving a secured image encryption (IES) scheme for sensitive and confidential data communications, especially in a Hadoop environment is challenging. An accurate and secure cryptosystem for colour images requires the generation of intricate secret keys that protect the images from diverse attacks. To attain such a goal, this work proposed an improved shuffled confusion-diffusion based colour IES using a hyper-chaotic plain image. First, five different sequences of random numbers were generated. Then, two of the sequences were used to shuffle the image pixels and bits, while the remaining three were used to XOR the values of the image pixels. Performance of the developed IES was evaluated in terms of various measures such as key space size, correlation coefficient, entropy, mean squared error (MSE), peak signal to noise ratio (PSNR) and differential analysis. Values of correlation coefficient (0.000732), entropy (7.9997), PSNR (7.61), and MSE (11258) were determined to be better (against various attacks) compared to current existing techniques. The IES developed in this study was found to have outperformed other comparable cryptosystems. It is thus asserted that the developed IES can be advantageous for encrypting big data sets on parallel machines. Additionally, the developed IES was also implemented on a Hadoop environment using MapReduce to evaluate its performance against known attacks. In this process, the given image was first divided and characterized in a key-value format. Next, the Map function was invoked for every key-value pair by implementing a mapper. The Map function was used to process data splits, represented in the form of key-value pairs in parallel modes without any communication between other map processes. The Map function processed a series of key/value pairs and subsequently generated zero or more key/value pairs. Furthermore, the Map function also divided the input image into partitions before generating the secret key and XOR matrix. The secret key and XOR matrix were exploited to encrypt the image. The Reduce function merged the resultant images from the Map tasks in producing the final image. Furthermore, the value of PSNR did not exceed 7.61 when the developed IES was evaluated against known attacks for both the standard dataset and big data size images. As can be seen, the correlation coefficient value of the developed IES did not exceed 0.000732. As the handling of big data size images is different from that of standard data size images, findings of this study suggest that the developed IES could be most beneficial for big data and big size images

Automatic Selective Encryption of DICOM Images

Securing DICOM images is essential to protect the privacy of patients, especially in the era of telemedicine and eHealth/mHealth. This increases the demand for rapid security. Nevertheless, a limited amount of research work has been conducted to ensure the security of DICOM images while minimizing the processing time. Hence, this paper introduces a selective encryption approach to reduce the processing time and sustain the robustness of security. The proposed approach selects regions within medical images automatically in the spatial domain using the pixel thresholding segmentation technique, then compresses and encrypts them using different encryption algorithms based on their importance. An adaptive two-region encryption approach is applied to single and multi-frame DICOM images, where the Region of Background (ROB) is encrypted using a light encryption algorithm, while the Region of Interest (ROI) is encrypted using a sophisticated encryption algorithm. For multi-frame DICOM images (Approach I), additional time-saving has been achieved by almost 10,000 times faster than the Naïve encryption approach, and 100 times better compression ratio, using one segmentation map based on a pre-defined reference frame for all the DICOM frames. For single-frame DICOM image (Approach II), a multi-region selective encryption approach is proposed, where the ROI is further split into three regions based on potential security threats, using a mathematical model that guarantees shorter encryption time in comparison with the Naive and the two-region encryption approaches, with almost 47% and 14% saving times, respectively. Based on the estimated processing time, Approach I outperformed Approach II noticeably. Further, cryptanalysis metrics are utilized to evaluate the proposed approaches, which indicate good robustness against a wide variety of attacks.</jats:p