Incremental shape learning of 3D surfaces of the knee, data from the osteoarthritis initiative

Traditional shape learning of medical image data has been implemented via Principal Component Analysis (PCA). These PCA based statistical shape models batch process all shapes at once to generate a fixed model of shape variation as principal components, which may require significant computation resources for large number of shapes. This paper applies incremental PCA (IPCA) on a dataset of 728 surfaces (derived from magnetic resonance imaging examinations displaying the articulating bones of the knee joint) that can efficiently adapt to changes in training sets. After comparing the compactness and the accuracy of shape reconstruction of both batch PCA and IPCA models, our results show that IPCA produces a model comparable to batch PCA in terms of compactness and applicability to shape reconstruction, while requiring considerably shorter processing time and computer memory for computation.