Probabilistic Appearance Models for Segmentation and Classification

Statistical shape and appearance models are often based on the accurate identification of one-to-one correspondences in a training data set. At the same time, the determination of these corresponding landmarks is the most challenging part of such methods. Hufnagel etal developed an alternative method using correspondence probabilities for a statistical shape model. We propose the use of probabilistic correspondences for statistical appearance models by incorporating appearance information into the framework. A point-based representation is employed representing the image by a set of vectors assembling position and appearances. Using probabilistic correspondences between these multi-dimensional feature vectors eliminates the need for extensive preprocessing to find corresponding landmarks and reduces the dependence of the generated model on the landmark positions. Then, a maximum a-posteriori approach is used to derive a single global optimization criterion with respect to model parameters and observation dependent parameters, that directly affects shape and appearance information of the considered structures. Model generation and fitting can be expressed by optimizing the same criterion. The developed framework describes the modeling process in a concise and flexible mathematical way and allows for additional constraints as topological regularity in the modeling process. Furthermore, it eliminates the demand for costly correspondence determination. We apply the model for segmentation and landmark identification in hand X-ray images, where segmentation information is modeled as further features in the vectorial image representation. The results demonstrate the feasibility of the model to reconstruct contours and landmarks for unseen test images. Furthermore, we apply the model for tissue classification, where a model is generated for healthy brain tissue using 2D MRI slices. Applying the model to images of stroke patients the probabilistic correspondences are used to classify between healthy and pathological structures. The results demonstrate the ability of the probabilistic model to recognize healthy and pathological tissue automatically.