Real Time Medical Image Consultation System Through Internet

Prasad, Deepak; Ray, S. Saha; Majumdar, A. K.; Mukherjee, Jayanta; Majumdar, B. C.; Paul, Soubhik; Verma, Amit

doi:10.1260/2040-2295.1.1.141

Cited by 7 publications

(3 citation statements)

References 6 publications

(15 reference statements)

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“…However, it cannot be ignored that in such a convenient environment, stored data may be susceptible to attacks, tampering, or misplacement during transmission or storage for diagnosis and treatment. This consideration is especially important in the application of telemedicine [1][2][3]. Therefore, the problem of ensuring the security and copyright of data when they are disseminated in open channels must be addressed.…”

Section: Introductionmentioning

confidence: 99%

WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication

2021

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Traditional watermarking techniques extract the watermark from a suspected image, allowing the copyright information regarding the image owner to be identified by the naked eye or by similarity estimation methods such as bit error rate and normalized correlation. However, this process should be more objective. In this paper, we implemented a model based on deep learning technology that can accurately identify the watermark copyright, known as WMNet. In the past, when establishing deep learning models, a large amount of training data needed to be collected. While constructing WMNet, we implemented a simulated process to generate a large number of distorted watermarks, and then collected them to form a training dataset. However, not all watermarks in the training dataset could properly provide copyright information. Therefore, according to the set restrictions, we divided the watermarks in the training dataset into two categories; consequently, WMNet could learn and identify the copyright information that the watermarks contained, so as to assist in the copyright verification process. Even if the retrieved watermark information was incomplete, the copyright information it contained could still be interpreted objectively and accurately. The results show that the method proposed by this study is relatively effective.

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Section: Introductionmentioning

confidence: 99%

WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication

2021

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“…Web applications have been used in a wide range of areas, including business, media, education, and the medical community. Many radiologists recently participated in the development of these web applications for radiological purposes [ 1 ]. Examples of web-based medical imaging applications are presented briefly in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of HTML5 and WebGL for Medical Imaging Applications

Min

Wang

Liu

2018

Journal of Healthcare Engineering

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Despite the fact that a large number of web applications are used in the medical community, there are still certain technological challenges that need to be addressed, for example, browser plug-ins and efficient 3D visualization. These problems make it necessary for a specific browser plug-in to be preinstalled on the client side when launching applications. Otherwise, the applications fail to run due to the lack of the required software. This paper presents the latest techniques in hypertext markup language 5 (HTML5) and web graphics library (WebGL) for solving these problems and an evaluation of the suitability of the combination of HTML5 and WebGL for the development of web-based medical imaging applications. In this study, a comprehensive medical imaging application was developed using HTML5 and WebGL. This application connects to the medical image server, runs on a standard personal computer (PC), and is easily accessible via a standard web browser. The several functions required for radiological interpretation were implemented, for example, navigation, magnification, windowing, and fly-through. The HTML5-based medical imaging application was tested on major browsers and different operating systems over a local area network (LAN) and a wide area network (WAN). The experimental results revealed that this application successfully performed two-dimensional (2D) and three-dimensional (3D) functions on different PCs over the LAN and WAN. Moreover, it demonstrated an excellent performance for remote access users, especially over a short time period for 3D visualization and a real-time fly-through navigation. The results of the study demonstrate that HTML5 and WebGL combination is suitable for the development of medical imaging applications. Moreover, the advantages and limitations of these technologies are discussed in this paper.

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“…Some examples are presented in [3][4][5][6]. Prasad et al [3] provided a tool for doctors to view medical images online and collaborate over the Internet. Marthinsen et al [4] tried to integrate online PACS systems and multichannel video-conferencing to run multiple remote magnetic resonance (MR) laboratories.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Method to Improve Medical Image Transmission

2011

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The staggering number of images acquired by modern modalities requires new approaches for medical data transmission. There have been several attempts to improve data transmission time between medical imaging systems. These attempts were mostly based on compression. Although the compression methods can help in many cases, they are sometimes ineffectual in high-speed networks. This paper introduces parallelism to provide an effective method of medical data transmission over both local area network (LAN) and wide area network (WAN). It is based on the Digital Imaging and Communications in Medicine (DICOM) protocol and uses parallel TCP connections in storage services within the protocol. Using the proposed interface in our method, current medical imaging applications can take advantage of parallelism without any modification. Experimental results show a speedup of about 1.3 to 1.5 for CT images and relatively high speedup of about 2.2 to 3.5 times for magnetic resonance (MR) images over LAN. The transmission time is improved drastically over WAN. The speedup is about 16.1 for CT images and about 5.6 to 11.5 for MR images.

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Real Time Medical Image Consultation System Through Internet

Cited by 7 publications

References 6 publications

WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication

WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication

An Evaluation of HTML5 and WebGL for Medical Imaging Applications

A Parallel Method to Improve Medical Image Transmission

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