Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Quality of Service aware Medical CT Image Transmission Anti-collision Mechanism Based on Big Data Autonomous Anti-collision Control

Author(s): Yong Jin*

Volume 14, Issue 7, 2019

Page: [676 - 683] Pages: 8

DOI: 10.2174/1574893613666180502111320

Price: $65

Abstract

Background: At present, due to the limitation of hardware, software and network transmission performance, the medical diagnosis of medical CT image equipment is easy to be carried out based on the wrong image. In addition, due to the complex structure of human organs and unpredictable lesion location, it is difficult to judge the reliability of medical CT images, spatial localization of the lesion, two-dimensional slice images and shape based on stereotypes. Therefore, how to improve the efficiency of medical CT terminal and the image quality has become the key technology to improve the satisfaction of medical diagnosis and treatment.

Objective: To improve the work efficiency of medical CT terminal and medical image transmission quality, with the medical CT terminal state and service quality.

Methods: Firstly, from the view of throughput, packet loss rate, delay and so on, a QoS aware model for medical CT image transmission has been established. Then, with throughput, packet length, path loss, service area size, access point location, and the number of medical CT terminals, the performance change regulation of the medical CT image transmission is completed and the optimal quality of service guarantee parameters sequence is obtained. Next, the medical CT image big data autonomous collision control scheme is proposed.

Results: The experimental and mathematical results verify the real-time performance, reliability, effectiveness and feasibility of the proposed medical CT image transmission anti-collision mechanism.

Conclusion: The proposed scheme can satisfy the high-quality high demand for data transmission at the same time, according to a variety of user experience demand and real-time adjustment of medical CT terminal working state, which provides effective data quality assurance and optimization of the network source distribution, and also enhances the quality of medical image data transmission service.

Keywords: Medical computed tomography, medical image transmission, quality of service aware, big data network, autonomous anti-collision control, stereotypes.

« Previous
Graphical Abstract

[1]
Singh S, Gupta D, Anand RS, et al. Non subsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network. Biomed Signal Process Control 2015; 18: 91-101.
[2]
Dubey SR, Singh SK, Singh RK. Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases. IEEE Trans Image Process 2015; 24: 5892-903.
[3]
Savva AD, Economopoulos TL, Matsopoulos GK. Geometry-based vs. intensity-based medical image registration: A comparative study on 3D CT data. Comput Biol Med 2016; 69: 120-33.
[4]
Kobayashi H, Yamagata H. Medical image processing apparatus, X-ray CT apparatus, MRI apparatus, ultrasound diagnostic imaging apparatus, and medical image processing method. J Oral Rehabil 2015; 2(5): 1-18.
[5]
Murala S, Wu QMJ. Local ternary co-occurrence patterns: A new feature descriptor for MRI and CT image retrieval. Neurocomputing 2013; 119: 399-412.
[6]
Duan X, Chen D, Wang J, et al. Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images. Int J Digit Multimed Broadcast 2017; 2017: 1-8.
[7]
Hanaoka S, Shimizu A, Nemoto M, et al. Automatic detection of over 100 anatomical landmarks in medical CT images: A framework with independent detectors and combinatorial optimization. Med Image Anal 2017; 35: 192-214.
[8]
Taguchi H, Maeda T. X-ray CT apparatus, image processing apparatus, and image processing method. Med Imaging Technol 2017; 14: 207-11.
[9]
Agrawal S, Raidu B, Agrawal K, et al. Prediction of Cancerous Cell by Cluster Based Biomedical CT Image and Analysis. Iosr J Dental Med Sci 2017; 16: 67-73.
[10]
Hashemi SM, Beheshti S, Cobbold RSC, Paul NS. Subband-dependent compressed sensing in local CT reconstruction. Signal Image Video Process 2016; 10: 1009-15.
[11]
Stille M, Kleine M, Hägele J, et al. Augmented Likelihood Image Reconstruction. IEEE Trans Med Imaging 2016; 35: 158.
[12]
Rister B, Horowitz MA, Rubin DL. Volumetric image registration from invariant keypoints. IEEE Trans Image Process 2017; 26: 4900-10.
[13]
Šuligoj F, Švaco M, Jerbić B, et al. Automated Marker Localization in the Planning Phase of Robotic Neurosurgery. IEEE Access 2017; 5: 12265-74.
[14]
Qi D, Hao C, Yu L, et al. Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection. IEEE Trans Biomed Eng 2017; 64: 1558-67.

Rights & Permissions Print Cite
Article Metrics
37
3
1
1
© 2024 Bentham Science Publishers | Privacy Policy