Statistical shape models generally use Principal Component Analysis (PCA) to describe the main directions of shape variation in a training set of ex- ample shapes. However, PCA has the restriction that the input data must be drawn from a Gaussian distribution and is only able to describe global shape variations. In this paper we evaluate the use of an alternative shape decomposi- tion, Independent Component Analysis (ICA), for two reasons. ICA does not require a Gaussian distribution of the input data and is able to describe localized shape variations. With ICA however, the resulting vectors are not ordered, therefore a method for ordering the Independent Components is presented in this paper. To evaluate ICA-based Active Appearance Models (AAMs), 10 leave-15-out models were trained on a set of 150 short-axis cardiac MR Images with PCA-based as well as ICA-based AAMs. The median values for the aver- age and maximal point-to-point distances between the expert drawn and auto- matically segmented contours for the PCA-based AAM were 2.95 and 8.39 pix- els. For the ICA-based AAM these distances were 1.86 and 5.01 pixels respec- tively. From this, we conclude that the use of ICA results in a substantial im- provement in border localization accuracy over a PCA-based model.
['Mehmet Üzümcü', 'Alejandro F. Frangi', 'Milan Sonka', 'Johan H. C. Reiber', 'Boudewijn P. F. Lelieveldt']
ICA vs. PCA Active Appearance Models: Application to Cardiac MR Segmentation