Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT

Kumar, S. N.; Fred, A. Lenin; Miriam, L. R. Jonisha; Gulyás, Balázs; Ajay, Kumar H.

doi:10.1002/9781119761884.ch8

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…In the future, with the potential emergence of new compression or new segmentation techniques, especially with the ongoing development of artificial intelligence (AI) based methods [ 36 , 37 ], the concept of a multi-level compression approach could still be used, with automatic segmentation of any pre-defined PROIs, SROIs, and background, to achieve even greater results in terms of compression ratio, while maintaining satisfactory image quality. Although the reduced image size in our proposed approach finds its main application in data storage optimization (i.e., image archiving), it could also benefit other domains in radiology, for example in the growing field of teleradiology [ 38 , 39 ], where it could lead to faster data transfers and reduced bandwidth requirements.…”

Section: Discussionmentioning

confidence: 99%

Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries

Fahrni

Rotzinger

Nakajo

et al. 2022

JCDD

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Advances in computed tomography (CT) have resulted in a substantial increase in the size of datasets. We built a new concept of medical image compression that provides the best compromise between compression rate and image quality. The method is based on multiple contexts and regions-of-interest (ROI) defined according to the degree of clinical interest. High priority areas (primary ROIs) are assigned a lossless compression. Other areas (secondary ROIs and background) are compressed with moderate or heavy losses. The method is applied to a whole dataset of CT angiography (CTA) of the lower extremity vasculature. It is compared to standard lossy compression techniques in terms of quantitative and qualitative image quality. It is also compared to standard lossless compression techniques in terms of image size reduction and compression ratio. The proposed compression method met quantitative criteria for high-quality encoding. It obtained the highest qualitative image quality rating score, with a statistically significant difference compared to other methods. The average compressed image size was up to 61% lower compared to standard compression techniques, with a 9:1 compression ratio compared with original non-compressed images. Our new adaptive 3D compression method for CT images can save data storage space while preserving clinically relevant information.

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Section: Discussionmentioning

confidence: 99%

Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries

Fahrni

Rotzinger

Nakajo

et al. 2022

JCDD

View full text Add to dashboard Cite

show abstract

“…Many researchers have offered cloud-based security algorithms for smart applications, increasing data scalability with efficient information retrieval [13][14][15]. Big data in the context of e-health are transported from one site to another by utilizing a wireless communication and cloud network in an IoT-based teleradiology system [16][17][18], as shown in Figure 1. This allows hospitals to obtain quick input from radiologists, who perform the same responsibilities as if they were on-site.…”

Section: Introductionmentioning

confidence: 99%

Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services

et al. 2022

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In wireless multimedia networks, the Internet of Things (IoT) and visual sensors are used to interpret and exchange vast data in the form of images. The digital images are subsequently delivered to cloud systems via a sink node, where they are interacted with by smart communication systems using physical devices. Visual sensors are becoming a more significant part of digital systems and can help us live in a more intelligent world. However, for IoT-based data analytics, optimizing communications overhead by balancing the usage of energy and bandwidth resources is a new research challenge. Furthermore, protecting the IoT network’s data from anonymous attackers is critical. As a result, utilizing machine learning, this study proposes a mobile edge computing model with a secured cloud (MEC-Seccloud) for a sustainable Internet of Health Things (IoHT), providing real-time quality of service (QoS) for big data analytics while maintaining the integrity of green technologies. We investigate a reinforcement learning optimization technique to enable sensor interaction by examining metaheuristic methods and optimally transferring health-related information with the interaction of mobile edges. Furthermore, two-phase encryptions are used to guarantee data concealment and to provide secured wireless connectivity with cloud networks. The proposed model has shown considerable performance for various network metrics compared with earlier studies.

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“…In the age of the internet of things (IoT) and Wireless Sensor Networks (WSN), a large number of linked objects and sensor devices are devoted to collecting, transferring, and generating huge amounts of data for a range of disciplines and applications (Kumar et al, 2021; Marjani et al, 2017; Messaoud et al, 2020). IoT allows physical devices, sensors, appliances, and other items to interact without human involvement.…”

Section: Introductionmentioning

confidence: 99%

A multi‐parametric machine learning approach using authentication trees for the healthcare industry

et al. 2022

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The Internet of Health Things (IoHT) has grown in importance for developing medical applications with the support of wireless communication systems. IoHT is integrated with many sensors to capture the patients' records and transmits them to hospital centres for analysis and reporting. Controlling and managing health records has been addressed in several ways, however, it is noted that two key research problems for vital communication systems are reliability and reducing data loss. To enhance the sustainability of health applications and effectively use the network infrastructure when transferring sensitive data, this research provides a machine learning approach. Moreover, data collected from the IoHTs are protected and can be securely received for physical process in hospitals using authentication trees. Firstly, the undirected graphs are explored based on the multi‐parametric machine learning approach to minimize the computation overheads and traffic congestion. Secondly, it evaluates the nodes' level behaviour over the heterogeneous traffic load with efficient identification of redundant links. Finally, in‐depth analysis and simulation results have shown that the proposed protocol is more effective than existing approaches for data accuracy and security analysis.

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Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT

Cited by 3 publications

References 18 publications

Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries

Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries

Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services

A multi‐parametric machine learning approach using authentication trees for the healthcare industry

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