Landmark-based segmentation of lungs while handling partial correspondences using sparse graph-based priors
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold learning and unsupervised clustering. In a graph-matching-like manner, we formulate the segmentation task as a labeling problem where we seek to match the model landmarks to image points that are extracted using the boosted classifiers. We also propose to overcome the limitation of missing correspondences by incorporating an additional label to account for outliers. Then, we repair the outlier positions to complete the segmentation. State-of-the-art discrete optimization techniques are used to provide our experimental results for the segmentation of the right lung in 2D chest radiographs, demonstrating the potentials of our method.