Artificial humeral head modeling using Kmeans++ clustering and PCA

Abstract

Currently, artificial humeral heads are mainly designed by scaling the average shape of anatomical data, and the humeral head shape is often designed to approximate a sphere or ellipse. It causes a problem that the range of motion (ROM) of the shoulder is limited with the artificial shoulder joint. Improvement of similarity of artificial shoulder joint with actual one may increase the ROM. For the purpose, we previously proposed a method for constructing a statistical shape model (SSM) of the humeral head that represents the inter-individual variation of the humeral head shape using principal component analysis (PCA). In this study, we propose a method to design the artificial humeral head model using Kmeans++ clustering and PCA. First, PCA is applied to the humeral head shape data of the subjects that are aligned and scaled. Next, Kmeans++ clustering is applied to the obtained PC score distribution map, and they are classified into four clusters. The mean shapes were obtained for each cluster and the models were constructed by changing their scale. From the experimental results, it was shown that the artificial humeral head model more similar to the actual humeral head shape was designed by the proposed method.