Abstract
Image processing is a very computationally heavy task, especially in the medical field. There are various medical imaging applications where image processing is required in a fast, efficient, and effortless manner to ensure quality for patients as well as doctors. The use of graphics processing units (GPUs) in medical image processing has increased over the past several years due to their high efficiency and parallel capabilities. This chapter explores the various GPU-based image-processing algorithms used for medical imaging with quantitative performance-based comparisons to the respective CPU-based counterparts. To this end, we explore an NVIDIA Clara-powered GPU segmentation extension within the application 3D Slicer to demonstrate the capabilities of GPU segmentation on a brain MRI consisting of a large brain tumor.
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