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Various fields in medicine require scientific research and computer application. This results in computation time optimization becoming a task that is of increasing importance due to its highly parallel architecture. As is well-known, the graphics processing unit (GPU) is regarded as a powerful engine for application programs that demand fairly high computation capabilities. Our study is based on the deep analysis of the parallelism pertaining to the calculation of the gray level co-occurrence matrix, whereby an algorithm was introduced to optimize the method used to compute the gray-level co-occurrence matrix (GLCM) of an image. Furthermore, strategies (e.g., copying, image partitioning, and so on) were proposed to optimize the parallel algorithm. Our experiments indicate that without losing the computational accuracy, the speed-up ratio of the GLCM computation of images with different resolutions by GPU utilizing compute unified device architecture was at least 50 times faster than that of the GLCM computation by the central processing unit. This manifestation of a significantly improved performance can lead to the development of a very useful computational tool in medical computer vision.

Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application