Home > Computer Science > Parallel Computing > Volume-3 > Issue-1 > Compressing of Magnetic Resonance Images with Cuda

Compressing of Magnetic Resonance Images with Cuda

Call for Papers

Volume-8 | Issue-6

Last date : 27-Dec-2024

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area


Compressing of Magnetic Resonance Images with Cuda


Mahmut Ünver | Atilla Ergüzen

https://doi.org/10.31142/ijtsrd20209



Mahmut Ünver | Atilla Ergüzen "Compressing of Magnetic Resonance Images with Cuda" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1, December 2018, pp.1140-1145, URL: https://www.ijtsrd.com/papers/ijtsrd20209.pdf

One of the most important areas that use image processing is the health sector. In order to detect some diseases, the need to visualize a certain part of the patient's body using medical imaging devices has emerged. This field in the health sector is the Radiology department. MR, Tomography, Ultrasound, X-ray, Echocardiography. Because of the importance of time in the health sector, GPU technologies are a technology that should be used in hospitals. Medical MRI images showed that the unused areas (NON-ROI) occupy a large area and this unnecessary area in the image could reduce the image size significantly. In this method developed with CUDA, the ROI (Region of Interest) region within the Medical MR images is determined by sending a 3X3 Kirsch filter matrix to the CUDA cores, and the NON-ROI region is extracted with CUDA from the image. It is then compressed with a new compression method developed. As a result of this method; The parallel application with CUDA solves the problem 34 times faster than the sequential application for each image, while the compressed image takes up 90% less space than the original image size; it takes 40% less space than the compressed size of the original image.

CUDA, Medical Image Processing, Image Compression, Parallel Programming


IJTSRD20209
Volume-3 | Issue-1, December 2018
1140-1145
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin