主催: 一般社団法人 日本機械学会
会議名: ロボティクス・メカトロニクス 講演会2016
開催日: 2016/06/08 - 2016/06/11
This paper presents a method to quantify the 3D alignment variances of hindfoot by Iterative Closest Point (ICP) and the principal component analysis (PCA). A general method is hard to calculate 3D geometric variances by reason of evaluating progression of the disorder from 2D X-ray measurement. The proposed method is able to consider 3D geometric variances using displacement fields calculated by the volume registration that compares with two 2D tomography images. The PCA for the bones alignment quantifies 3D alignment variances. The numerical results of PCA demonstrates the principal features of imaging posture, translation of fibula and rotation of navicular.