Abstract

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 patients 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. 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, URL: http://www.ijtsrd.com/papers/ijtsrd20209.pdf http://www.ijtsrd.com/computer-science/parallel-computing/20209/compressing-of-magnetic-resonance-images-with-cuda/mahmut-ünver

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