Disease characterization of active appearance model coefficients

Abstract

We previously reported on 2D and 3D Active Appearance Models (AAM) for automated segmentation of cardiac MR. AAMs are shown useful for such segmentations because they exploit prior knowledge about cardiac shape and image appearance, yet segmentation of object borders might not be the only benefit of AAMs. An AAM represents objects as a linear combination of shape and texture variations applied to a mean object via Principal Component Analysis (PCA) to form an integrated model. This model captures enough shape, texture, and motion variations to accurately synthesize reconstructions of target objects from a finite set of parameters. Because of this, we hypothesize that AAM coefficients may be used for the classification of disease abnormalities. PCA is useful for reducing the dimensionality of vectors, however it does not produce vectors optimal for the separation of classes needed for disease classification. Discriminate analysis such as Linear Discriminate Analysis (LDA) and Kernel Discriminate Analysis (KDA) are dimension reducing techniques with the added benefit of supervised learning for the purpose of classification. Once AAM segmentation is complete, disease probabilities are computed from model coefficients via discriminate analysis. Preliminary results on model coefficients show tendency of disease separation for certain disease classes.