Accelerated detection of intracranial space-occupying lesions with CUDA based on statistical texture atlas in brain HRCT

In this paper, we present a method that detects intracranial space-occupying lesions in two-dimensional (2D) brain high-resolution CT images. Use of statistical texture atlas technique localizes anatomy variation in the gray level distribution of brain images, and in turn, identifies the regions with lesions. The statistical texture atlas involves 147 HRCT slices of normal individuals and its construction is extremely time-consuming. To improve the performance of atlas construction, we have implemented the pixel-wise texture extraction procedure on Nvidia 8800GTX GPU with compute unified device architecture (CUDA) platform. Experimental results indicate that the extracted texture feature is distinctive and robust enough, and is suitable for detecting uniform and mixed density space-occupying lesions. In addition, a significant speedup against straight forward CPU version was achieved with CUDA.