Compressive Sensing of Medical Images Based on HSV Color Space.

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV-SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV-SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of 256×256 pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV-SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

A Systematic Review on Recent Trends, Challenges, Privacy and Security Issues of Underwater Internet of Things.

Owing to the hasty growth of communication technologies in the Underwater Internet of Things (UIoT), many researchers and industries focus on enhancing the existing technologies of UIoT systems for developing numerous applications such as oceanography, diver networks monitoring, deep-sea exploration and early warning systems. In a constrained UIoT environment, communication media such as acoustic, infrared (IR), visible light, radiofrequency (RF) and magnet induction (MI) are generally used to transmit information via digitally linked underwater devices. However, each medium has its technical limitations: for example, the acoustic medium has challenges such as narrow-channel bandwidth, low data rate, high cost, etc., and optical medium has challenges such as high absorption, scattering, long-distance data transmission, etc. Moreover, the malicious node can steal the underwater data by employing blackhole attacks, routing attacks, Sybil attacks, etc. Furthermore, due to heavyweight, the existing privacy and security mechanism of the terrestrial internet of things (IoT) cannot be applied directly to UIoT environment. Hence, this paper aims to provide a systematic review of recent trends, applications, communication technologies, challenges, security threats and privacy issues of UIoT system. Additionally, this paper highlights the methods of preventing the technical challenges and security attacks of the UIoT environment. Finally, this systematic review contributes much to the profit of researchers to analyze and improve the performance of services in UIoT applications.

On the Performance Evaluations of Cooperative Retransmission Scheme for Cell-Edge Users of URLLC in Multi-Carrier Downlink NOMA Systems.

Non-orthogonal multiple access (NOMA) has a key feature that the cell-center user (CCU) has prior information about the messages of the cell-edge user (CEU) in the same user-pair. It means that CCU can be used for retransmission when the CEU requests retransmission. As ultra-reliability and low-latency communication (URLLC) requires high-reliability constraints (e.g., 99.999%), using CCU for retransmission can be useful to satisfy the reliability constraint. In this study, to ensure the reliability of CEU, cooperative retransmission (CR) scheme for downlink NOMA systems is proposed. And the CR scheme is evaluated with Block error rate (BLER) considering reliability and with packet loss rate (PLR) in terms of reliability and latency constraints. And the evaluation results showed that the proposed CR scheme can satisfy the target BLER for URLLC low SNR compared to the conventional retransmission scheme, and showed the improved PLR compared to the conventional retransmission scheme in low SNRs.

The Relationship Between Breast Density Change During Menopause and the Risk of Breast Cancer in Korean Women.

BACKGROUND: The aim of this study was to investigate the relationship between changes in breast density during menopause and breast cancer risk. METHODS: This study was a retrospective, longitudinal cohort study for women over 30 years of age who had undergone breast mammography serially at baseline and postmenopause during regular health checkups at Samsung Medical Center. None of the participants had been diagnosed with breast cancer at baseline. Mammographic breast density was measured using the American College of Radiology Breast Imaging Reporting and Data System. RESULTS: During 18,615 person-years of follow-up (median follow-up 4.8 years; interquartile range 2.8-7.5 years), 45 participants were diagnosed with breast cancer. The prevalence of dense breasts was higher in those who were younger, underweight, had low parity or using contraceptives. The cumulative incidence of breast cancer increased 4 years after menopause in participants, and the consistently extremely dense group had a significantly higher cumulative incidence (CI) of breast cancer compared with other groups [CI of extremely dense vs. others (incidence rate per 100,000 person-years): 375 vs. 203, P < 0.01]. CONCLUSION: Korean women whose breast density was extremely dense before menopause and who maintained this density after menopause were at two-fold greater risk of breast cancer. PREVENTION RELEVANCE: Extremely dense breast density that is maintained persistently from premenopause to postmenopause increases risk of breast cancer two fold in Korean women. Therefore, women having risk factors should receive mammography frequently and if persistently extremely dense breast had been detected, additional modalities of BC screening could be considered.

Compressed medical imaging based on average sparsity model and reweighted analysis of multiple basis pursuit.

In medical imaging and applications, efficient image sampling and transfer are some of the key fields of research. The compressed sensing (CS) theory has shown that such compression can be performed during the data retrieval process and that the uncompressed image can be retrieved using a computationally flexible optimization method. The objective of this study is to propose compressed medical imaging for a different type of medical images, based on the combination of the average sparsity model and reweighted analysis of multiple basis pursuit (M-BP) reconstruction methods, referred to as multiple basis reweighted analysis (M-BRA). The proposed algorithm includes the joint multiple sparsity averaging to improves the signal sparsity in M-BP. In this study, four types of medical images are opted to fill the gap of lacking a detailed analysis of M-BRA in medical images. The medical dataset consists of magnetic resonance imaging (MRI) data, computed tomography (CT) data, colonoscopy data, and endoscopy data. Employing the proposed approach, a signal-to-noise ratio (SNR) of 30 dB was achieved for MRI data on a sampling ratio of M/N=0.3. SNR of 34, 30, and 34 dB are corresponding to CT, colonoscopy, and endoscopy data on the same sampling ratio of M/N=0.15. The proposed M-BRA performance indicates the potential for compressed medical imaging analysis with high reconstruction image quality.

A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging.

Wireless capsule endoscopy (WCE) is a process in which a patient swallows a camera-embedded pill-shaped device that passes through the gastrointestinal (GI) tract, captures and transmits images to an external receiver. WCE devices are considered as a replacement of conventional endoscopy methods which are usually painful and distressful for the patients. WCE devices produce over 60,000 images typically during their course of operation inside the GI tract. These images need to be examined by expert physicians who attempt to identify frames that contain inflammation/disease. It can be hectic for a physician to go through such a large number of frames, hence computer-aided detection methods are considered an efficient alternative. Various anomalies can take place in the GI tract of a human being but the most important and common ones and the aim of this survey are ulcers, polyps, and tumors. In this paper, we have presented a survey of contemporary computer-aided detection methods that take WCE images as input and classify those images in a diseased/abnormal or disease-free/normal image. We have considered methods that detect tumors, polyps and ulcers, as these three diseases lie in the same category. Furthermore, general abnormalities and bleeding inside the GI tract may be the symptoms of these diseases; so an attempt is also made to enlighten the research work done for abnormalities and bleeding detection inside WCE images. Several studies have been included with in-depth detail of their methodologies, findings, and conclusions. Also, we have attempted to classify these methods based on their technical aspects. A formal discussion and comparison of recent review articles are also provided to have a benchmark for the presented survey mentioning their limitations. This paper also includes a proposed classification approach where a cascade approach of neural networks is presented for the classification of tumor, polyp, and ulcer jointly along with data set specifications and results.

Quality assessment for wireless capsule endoscopy videos compressed via HEVC: From diagnostic quality to visual perception.

Maintaining the quality of medical images and videos is an essential part of the e-services provided by the healthcare sector. The convergence of modern communication systems and the healthcare industry necessitates the provision of better quality of service and experience by the service provider. Recent inclusion and standardization of the high efficiency video coder (HEVC) has made it possible for medical data to be compressed and transmitted over wireless networks with minimal compromise of the quality. Quality evaluation and assessment of these medical videos transmitted over wireless networks is another important research area that requires further exploration and attention. In this paper, we have conducted an in-depth study of video quality assessment for compressed wireless capsule endoscopy (WCE) videos. Our study includes the performance evaluation of several state-of-the-art objective video quality metrics in terms of determining the quality of compressed WCE videos. Subjective video quality experiments were conducted with the assistance of experts and non-experts in order to predict the diagnostic and visual quality of these medical videos, respectively. The evaluation of the metrics is based on three major performance metrics that include, correlation between the subjective and objective scores, relative statistical performance and computation time. Results show that the metrics information fidelity criterion (IFC), visual information fidelity-(VIF) and especially pixel based VIF stand out as best performing metrics. Furthermore, our paper reports the performance of HEVC compression on medical videos and according to the results, it performs optimally in preserving the diagnostic and visual quality of WCE videos at Quantization Parameter (QP) values of up to 35 and 37 respectively.

Detection of small colon bleeding in wireless capsule endoscopy videos.

In the recent years, wireless capsule endoscopy (WCE) technology has played a very important role in diagnosing diseases within the gastro intestinal (GI) tract of human beings. The WCE device captures images of the GI tract of patient with a certain frame rate. Physicians examine these images in order to find abnormalities in the GI tract. This examination process is very time consuming and hectic for the physician as a WCE device captures around 60,000 images on the average. At present, there are no standards defined for the WCE image classification. Computer aided methods help reducing the burden on the physicians by automatically detecting the abnormalities in the GI tract such as small colon bleeding. In this paper, a pixel based approach to detect bleeding regions in the WCE videos by using a support vector classifier is proposed. Threshold analysis in HSV color space is performed to compute the features for training an optimal support vector machine. The HSV features of the WCE images are fed to the trained support vector classifier for classification. Also, our method includes image enhancement and edge removal in WCE images, which is done prior to classification, for robust results. The method offers high sensitivity, specificity and accuracy in terms of correctly classifying images that contain bleeding regions as compared to another contemporary method. A detailed experimental analysis is also provided for the purpose of method evaluation.

A cost effective block framing scheme for underwater communication.

In this paper, the Selective Multiple Acknowledgement (SMA) method, based on Multiple Acknowledgement (MA), is proposed to efficiently reduce the amount of data transmission by redesigning the transmission frame structure and taking into consideration underwater transmission characteristics. The method is suited to integrated underwater system models, as the proposed method can handle the same amount of data in a much more compact frame structure without any appreciable loss of reliability. Herein, the performance of the proposed SMA method was analyzed and compared to those of the conventional Automatic Repeat-reQuest (ARQ), Block Acknowledgement (BA), block response, and MA methods. The efficiency of the underwater sensor network, which forms a large cluster and mostly contains uplink data, is expected to be improved by the proposed method.

SBMAC: smart blocking MAC mechanism for variable UW-ASN (Underwater Acoustic Sensor Network) environment.

In this paper, several MAC scheduling methods applicable to an underwater environment are proposed. Besides, a new marine communication system model was proposed to improve the reliability of the proposed SBMAC method. The scheme minimizes transmission of control frames except for data transmission and various transmission methods and ACK methods can be used together. Simulation models are set indices and analysis of the underwater environment is established to conduct reliable simulations. Consequently, the performance improvement of the proposed method is verified with respect to delay time, data transmission rate, memory utilization, energy efficiency, etc.