3D statistical shape modeling

Abstract

Statistical shape models (SSMs) have been firmly established as a robust tool for segmentation of images. Widespread utilization of three-dimensional models appeared only in recent years, primarily made possible by breakthroughs in automatic detection of shape correspondences; while 2D models have been in use since the early 1990s. The objective of this project is to build a 3D statistical shape modeling for a given data; the implemented process goes through those basic steps, first collect the given data then apply the alignment algorithm based on the ICP (iterative closest point) method which in turn relies on Procrustes analysis result as a starting point, next we apply fitting algorithm which is also based on ICP. Finally we obtain the model using PCA (principle component analysis). To achieve this work, we have implemented the above process on two different shape models, one tested with the Basel Face Model (BSF) and the other is the femur model data samples from the SICAS (Swiss Institute for Computer Assissted Surgery) Medical Image Repository which is used by the Basel University (Switzerland) for both samples, where these models allow the generation and the exploration of the possible shape variation.