An Efficient Implementation of a Census-Based Stereo Matching and Its Applications in Medical Imaging

Compared to most conventional efficient stereo matching algorithms that based on NCC (Normalized Cross-Correlation) or SAD (Sum of Absolute Difference), stereo matching based on census transform is robust to radiometric distortion. Thus, in the paper we propose a census-based efficient implementation stereo algorithm for medical imaging. Firstly, census-based stereo matching is investigated, and its specific implementation process is analyzed in detail. Secondly, in order to simplify the calculation process and improve the efficiency, the moving window and memory organization optimized techniques are used. The program runs on standard PC hardware utilizing various SSE2 instructions. Finally, stereo matching of four standard image pairs on the Middlebury image datasets and a paired cervical images obtained from clinical colposcope are implemented in an efficient way. The experimental results on simulated and real medical images prove the effectiveness of the method for the computational cost.