A medical image steganography method based on integer wavelet transform and overlapping edge detection

© Springer International Publishing Switzerland 2015. Recently, there has been an increased interest in the transmission of digital medical images for e-health services. However, existing implementations of this service do not pay much attention to the confidentiality and protection of patients’ information. In this paper, we present a new medical image steganography technique for protecting patients’ confidential information through the embedding of this information in the image itself while maintaining high quality of the image as well as high embedding capacity. This technique divides the cover image into two areas, the Region of Interest (ROI) and the Region of Non- Interest (RONI), by performing Otsu’s method and then encloses ROI pixels in a rectangular shape according to the binary pixel intensities. In order to improve the security, the Electronic Patient Records (EPR) is embedded in the high frequency sub-bands of the wavelet transform domain of the RONI pixels. An edge detection method is proposed using overlapping blocks to identify and classify the edge regions. Then, it embeds two secret bits into three coefficient bits by performing an XOR operation to minimize the difference between the cover and stego images. The experimental results indicate that the proposed method provides a good compromise between security, embedding capacity and visual quality of the stego images

The impact of the implementations of the Sysrust’s framework upon the quality of financial reporting: structural equation modelling approach

The purpose of this research is to examine empirically, validate, and predict the reliability of the proposed relationship between the reliability of AIS process in the context of SysTrust' framework (principles and criteria) and the quality of financial reporting in shareholdings companies in Jordan. For this purpose, a primary data was used that was collected through a self- structured questionnaire from 239 of shareholdings companies. The extent of SysTrust's framework (principles and criteria) and the quality of financial reporting were also measured. The data were analyzed using structure equation modeling. The results showed that the magnitude and significance of the loading estimate and they indicated that all of the main five principles of SysTrust's framework are relevant in predicating the quality of financial reporting. Moreover, the reliability of AIS by the implementation of these five principles of SysTrust's framework were positively impact the quality of financial reporting, as the structural coefficient for these paths are significant

An Empirical Examination of InterOrganizational Factors Influence on Green Marketing Adoption in Jordanian Industrial Sector

The aim of this research is to examine the factors affecting the adoption of green marketing concept among the industrial manufactures in Jordan. Data were collected from 92 industrial manufactures. Hypotheses were tested using multiple regression. The results indicated that social and environment responsibility have positive relationships with green marketing adoption. Lacks of significant relationships were found between managerial attitude, innovative management and green marketing adoption. These results provide significant managerial implications on how to build and foster the green marketing as an organizational culture and determine what factors should be considered in that regard

Accumulation, Source Identification, and Cancer Risk Assessment of Polycyclic Aromatic Hydrocarbons (PAHs) in Different Jordanian Vegetables

The accumulation of polyaromatic hydrocarbons in plants is considered one of the most serious threats faced by mankind because of their persistence in the environment and their carcinogenic and teratogenic effect on human health. The concentrations of sixteen priority polycyclic aromatic hydrocarbons (16 PAHs) were determined in four types of edible vegetables (tomatoes, zucchini, eggplants, and cucumbers), irrigation water, and agriculture soil, where samples were collected from the Jordan Valley, Jordan. The mean total concentration of 16 PAHs (∑16PAHs) ranged from 10.649 to 21.774 µg kg−1 in vegetables, 28.72 µg kg−1 in soil, and 0.218 µg L−1 in the water samples. The tomato samples posed the highest ∑16PAH concentration level in the vegetables, whereas the zucchini samples had the lowest. Generally, the PAHs with a high molecular weight and four or more benzene rings prevailed among the studied samples. The diagnostic ratios and the principal component analysis (PCA) revealed that the PAH contamination sources in soil and vegetables mainly originated from a pyrogenic origin, traffic emission sources, and biomass combustion. The bioconcentration factors (BCF) for ∑16PAHs have been observed in the order of tomatoes > cucumbers and eggplants > zucchini. A potential cancer risk related to lifetime consumption was revealed based on calculating the incremental lifetime cancer risk of PAHs (ILCR). Therefore, sustainable agricultural practices and avoiding biomass combusting would greatly help in minimizing the potential health risk from dietary exposure to PAHs

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

© 2016 Elsevier Ireland Ltd Objectives: The 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. Methods: The 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. Results: The 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. Conclusion: The 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

MR Brain Image Segmentation Based on Unsupervised and Semi-Supervised Fuzzy Clustering Methods

© 2016 IEEE. In medical imaging applications, the segmentation of Magnetic Resonance (MR) brain images plays a crucial role for measuring and visualizing the anatomical structures of interest. In general, the brain image segmentation aims to divide the image pixels into non-overlapping homogeneous regions for analyzing the changes in the brain for surgical planning. Several supervised and unsupervised clustering methods have been developed over the years to segment the magnetic resonance brain image. However, most of these methods have certain limitations such as requiring user interaction and high computational complexity. In this context, this paper proposes a methodology that combines semi-supervised and unsupervised classification techniques for achieving efficient and fully-automatic segmentation of brain images. Firstly, the algorithm applies a median filter to remove the noise inherent in MR images prior to the clustering step. Secondly, the background of the MR image is removed by using a global thresholding technique. Thirdly, we utilize the subtractive clustering method to overcome the deficiency of randomly initialized Fuzzy C-Means (FCM) parameters. This method is used for estimating the clustering number and to generate the initial centers, which is used as initialization parameter for FCM clustering. Finally, a semi-supervised algorithm with Standard Fuzzy Clustering is selected to divide the brain MR image into different classes based on the generated membership function from FCM. The efficiency of the proposed method is demonstrated on various MR brain images and compared with some of the well-known clustering techniques

A clustering fusion technique for MR brain tissue segmentation

© 2017 Elsevier B.V. In recent decades, a large number of segmentation methods have been introduced and applied to magnetic resonance (MR) brain image analysis to measure and visualize the anatomical structures of interest. In this paper, an efficient fully-automatic brain tissue segmentation algorithm based on a clustering fusion technique is presented. In the training phase of this algorithm, the pixel intensity value is scaled to enhance the contrast of the image. The brain image pixels that have similar intensity are then grouped into objects using a superpixel algorithm. Further, three clustering techniques are utilized to segment each object. For each clustering technique, a neural network (NN) model is fed with features extracted from the image objects and is trained using the labels produced by that clustering technique. In the testing phase, pre-processing step includes scaling and resizing the brain image are applied then the superpixel algorithm partitions the image into multiple objects (similar to the training phase). The three trained neural network models are then used to predict the respective class of each object and the obtained classes are combined using majority voting. The efficiency of the proposed method is demonstrated on various MR brain images and compared with the three base clustering techniques

An efficient steganography method for hiding patient confidential information

© 2014 IEEE. This paper deals with the important issue of security and confidentiality of patient information when exchanging or storing medical images. Steganography has recently been viewed as an alternative or complement to cryptography, as existing cryptographic systems are not perfect due to their vulnerability to certain types of attack. We propose in this paper a new steganography algorithm for hiding patient confidential information. It utilizes Pixel Value Differencing (PVD) to identify contrast regions in the image and a Hamming code that embeds 3 secret message bits into 4 bits of the cover image. In order to preserve the content of the region of interest (ROI), the embedding is only performed using the Region of Non-Interest (RONI)