GPU based brain segmentation method

As Graphical Processing Units (GPU) develops fast and becomes suitable for general purpose usage, GPUs are used to improve speed performance of processing and analysis of medical images. With the high parallel computation capabilities of GPUs, a large number of pixel computation can be done in parallel. Especially volumetric MR or CT scans may contain more than 40 slices. In this type of data, parallel processing of image slices will speed up the medical processes. In this paper, we propose a brain segmentation method which uses our parallel implementation of active contours and K-means clustering algorithm on CUDA environment. GPU and CPU implementations of the method are compared and the advantages and disadvantages of using CUDA are explained.