Author(s)

Ying-Chih Lin^4*, Chien-Liang Huang⁴, Chin-Sheng Chen⁴, Wen-Chung Chang¹, Yu-Jen Chen³, Chia-Yuan Liu²

¹Department of Electrical Engineering, National Taipei University of Technology, Chinese Taipei.
²Department of Internal Medicine, Mackay Memorial Hospital, Taiwan, China.
³Department of Radiation Oncology, Mackay Memorial Hospital, Taiwan, China.
⁴Graduate Institute of Automation Technology, National Taipei University of Technology, Chinese Taipei.

Image registration is wildly used in the biomedical image, but there are too many textures and noises in the biomedical image to get a precise image registration. In order to get the excellent registration performance, it needs more complex image processing, and it will spend expensive computation cost. For the real time issue, this paper proposes edge gradient direction image registration applied to Computer Tomography(CT) image and Ultrasonography (US) image based on the cooperation of Graphic Processor Unit (GPU) and Central Processor Unit (CPU). GPU can significantly reduce the computation time. First, the CT image slice is extracted from the CT volume by the region growing and the interpolation algorithm. Secondly, the image pre-processing is employed to reduce the image noises and enhance the image features. There are two kinds of the image pre-processing algorithms invoked in this paper: 1) median filtering and 2) anisotropic diffusion. Last but not least, the image edge gradient information is obtained by Canny operator, and the similarity measurement based on gradient direction is employed to evaluate the similarity between the CT and the US images. The experimental results show that the proposed architecture can distinctively improve the efficiency and are more suitably applied to the real world.

Image Registration; Graphic Processor; Computer Tomography; Ultrasonography; Canny Operator

Y. Lin, C. Huang, C. Chen, W. Chang, Y. Chen and C. Liu, "Computer Tomography and Ultrasonography Image Registration Based on the Cooperation of GPU and CPU," Journal of Signal and Information Processing, Vol. 4 No. 3B, 2013, pp. 80-85. doi: 10.4236/jsip.2013.43B014.