A Hashing Algorithm of Depth Image Matching for Liver Surgery

As we have been developing a liver surgical navigation system that uses a scanned 3D liver model of the patient, we have pointed the necessity of the efficient posture estimation from depth images to be matched with the posture of a virtual liver rendered on a computer. We have previously used the technique of calculating the sum of squared errors using whole pixels contained in the depth images, and have compared over nine images simultaneously using the technique for the posture estimation. However, it requires a massive number of calculation repetition and that could be a barrier to the real time navigation through the surgery. In this paper, we propose a new method for comparing depth images and estimating postures by hashing the depth images vertically and horizontally. The liver postures can be identified by calculating 25 dimensional integer hashed values with the accuracy around 90%. By combining the previous posture estimation techniques with this method, the efficiency of the navigation system should be increased for the practical use in the real time surgical environment.