Abstract
As the amount of information contained in 3-dimensional medical images increases, the computational cost of computer-aided diagnosis (CAD) becomes higher. The calculations must therefore be performed faster. The authors have focused on parallel computing methods using the Compute Unified Device Architecture (CUDA) as one of the techniques for general-purpose computing on graphics processing units (GPGPU), which is a technology for diverting the computing resources of GPUs to perform general-purpose computations. In order to use CUDA effectively, programmers must carefully consider the various requirements of parallel programming and CUDA. The Insight Segmentation and Registration Toolkit (ITK) is widely used as a standard medical image processing library in the field of medical image processing. However, because ITK has a unique data structure, it cannot be used in programs with CUDA as is. To address these issues, the authors propose a method for developing parallel ITK programs with CUDA by means of sequential ITK programs. The authors have conducted comparative evaluations of computational speed and usability, and the results confirm the usefulness of the proposed method.
