ROI Determination and Compression in MRI Using Gradient Method with CUDA
Due to the large use of MRI in hospitals, large storage areas are needed to store these images. Also, if you want to access these images over the system repeatedly, a large bandwidth is required. To solve this problem, it will be necessary to compress and store the medical imaging system quickly and without disruption. It has been seen that in the studies made on MRIs, the non used regions NON ROI occupy a large space and the image size can be reduced significantly when the unnecessary area in the image is cleaned. In this method developed with CUDA, the region of interest ROI in the MRI is detected by sending a 3x3 Kirsch filter matrix to the CUDA cores and the NON ROI region is extracted from the image with CUDA. These operations are first executed by the serial application on CPU, then by a parallel application on GPU. As a result, the application running on the GPU produced 34 times faster results than the application on the CPU. When images are compressed with this new improved method, it takes up 89 less than the original image size and 15 less than the original compressed image.
Medical Image, Parallel Programming, Medical Image Processing, CUDA, ROI.