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

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

The Impact of Innovation in Jordanian Chemical and Pharmaceutical Industries on Export Performance

Innovation has long been considered an important factor for creating and maintaining the competitiveness of the firms. Common knowledge stands that innovation is the cause of the increase of exports. However, contradicting empirical evidences are reported in the literature on the relationship between innovation and export performance. In this research we examine whether innovation performed by Jordanian chemical and pharmaceutical industries enhances their export performance. Based on research objectives, a structured questionnaire was developed to collect the needed data to test the developed hypotheses. Data were collected from twenty two companies, representing a sixty-five percent response rate. Data were analyzed and hypotheses were tested using various analytical methods. Research findings indicate that there is a statistical significant relationship between innovation and export performance for the sample under study; mainly for research and development, marketing data base, management (atmosphere conductive to innovation), promotion and product (quality). Based on the results, several recommendations are suggested

Letrozole before TESE in Non-Obstructive Azoospermia, Does It Affect Sperm Retrieval Rate, A Retrospective Study

Objective: This study was designed to evaluate the effect of letrozole 2.5 mg, an aromatase inhibitor, on the sperm retrieval rate (SRR) by the testicular sperm extraction (TESE) procedures that was done for the treatment of males with non-obstructive azoospermia (NOA).Materials and methods: Data was collected retrospectively from males diagnosed with non-obstructive azoospermia who underwent TESE procedure in the duration between May 2010 until June, 2018. The collected data includes the age of the patient, body mass index (BMI), testicular volume, hormonal profile (FSH LH, prolactin, testosterone), and the use of letrozole preoperatively. Logistic regression was done to address the association of these parameters to the sperm’s retrieval rate.Results: The study screaned 145 patients. Eighty patients fit the inclusion criteria and thus they were statistically analyzed. The use of letrozole was associated with negative TESE outcome (p=0.006), odd (0.154) CI 0.04-0.579. The other factors had no significant correlation to the TESE results.Conclusion: The evidence in this study showed an adverse effect of letrozole use on TESE results of those with high FSH

The Influence of Social Marketing Drives on Brand Loyalty via the Customer Satisfaction as a Mediating Factor in Travel and Tourism Offices

This study analyzed the impact of various aspects of social media marketing (beneficial promotions, relevant content, popular content, and presence on multiple platforms) on brand loyalty through the mediating factor of customer satisfaction in travel and tourism offices in Jordan. The study’s sample consisted of 350 followers of at least one travel and tourism office on social media, with a response rate of 86% obtained via a self-administered questionnaire. The results supported the significance of social media marketing drives on brand loyalty, with customer satisfaction playing a crucial mediating role. All the factors that engage customers in social media marketing (i.e., beneficial promotions, relevant content, popular content, and presence on multiple platforms) were found to have a simultaneous impact on brand loyalty. This study is the first of its kind in the Jordanian business setting to examine the effect of social media marketing on brand loyalty through customer satisfaction. Most prior research in this field has been conducted in Western countries

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

Investigating the impact of ECRM success factors on business performance: Jordanian commercial banks

Purpose: The purpose of this paper is to develop an integrated framework to explore the influences of electronic customer relationship management (ECRM) success factors (process fit, customer information quality and system support) on customer satisfaction, customer trust and customer retention, which, in turn, impact upon the business financial performance of Jordanian commercial banks in Amman city. Design/methodology/approach: Using a sample of 343 branch managers, assistant branch managers and heads of departments in Jordanian commercial banks, who answered a self-administrated questionnaire, data were collected and analysed using structural equation modelling (AMOS 17.0). Findings: The results showed that the ECRM success factors (process fit, customer information quality and system support) positively affected customer satisfaction, customer trust and customer retention. Furthermore, the authors discovered that customer satisfaction and customer trust positively influenced customer retention. It was determined that customer satisfaction, customer trust and customer retention positively impact on a business's financial performance. Originality/value: Previous research lacks the link between ECRM success factors and business performance (financial and non-financial).Scopu

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