A framework for image segmentation using shape models and kernel space shape priors

Segmentation involves separating an object from the background in a given image. The use of image information alone often leads to poor segmentation results due to the presence of noise, clutter, or occlusion. The introduction of shape priors in the geometric active contour (GAC) framework has proven to be an effective way to ameliorate some of these problems. In this work, we propose a novel segmentation method combining image information with prior shape knowledge using level sets. Following the work of Leventon et al., we propose revisiting the use of principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner. We utilize kernel PCA (KPCA) and show that this method outperforms linear PCA by allowing only those shapes that are close enough to the training data. In our segmentation framework, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description permits us to fully take advantage of the KPCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes and offers a convincing level of robustness with respect to noise, occlusions, or smearing.