Medical imaging is a technique used for creating visual representation of the internal body parts for the purpose of clinical analysis & med
Global medical imaging process market is segment based on the techniques into CT scan, X-ray, MRI, Ultrasound, nuclear imaging, and others. CT scan and X-ray technique segment hold the dominant share in the market. The availability of handheld X-ray devices and MRI scanners are fuelling the growth of global medical image processing market. Based on the image type, the market is segmented into 2D image, 3D image and 4D image. #readmore..
Compressing of Magnetic Resonance Images with Cuda
by 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
Direct Link: http://www.ijtsrd.com/computer-science/parallel-computing/20209/ compressing-of-magnetic-resonance-images-with-cuda/mahmut-ünver
indexed journal, conference issue publication, high impact factor
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.
A new innovative technique based on fuzzy deconvolution for scattering centre detection (F-SCD) is proposed together with its implementation in FPGA for real-time deployment in UAV and automotive collision avoidance application.
By Luigi Giubbolini in Image Processing and Medical Image Processing.A new innovative technique based on fuzzy de-convolution for scattering centre detection (F-SCD) is proposed together.
