In this paper, we propose an efficient method for drawing shape samples using a point distribution model (PDM) that is conditioned on given points. This technique is suited for sample-based segmentation methods that rely on a PDM, e.g. [6], [2] and [3]. It enables these algorithms to effectively constrain the solution space by considering a small number of user inputs - often one or two landmarks are sufficient. The algorithm is easy to implement, highly efficient and usually converges in less than 10 iterations. We demonstrate how conditional PDMs based on a single user-specified vertebra landmark significantly improve the aorta and vertebrae segmentation on standard lateral radiographs. This is an important step towards a fast and cheap quantification of calcifications on X-ray radiographs for the prognosis and diagnosis of cardiovascular disease (CVD) and mortality. © 2011 Springer-Verlag.
CITATION STYLE
Petersen, K., Nielsen, M., & Brandt, S. S. (2011). Conditional point distribution models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6533 LNCS, pp. 1–10). https://doi.org/10.1007/978-3-642-18421-5_1
Mendeley helps you to discover research relevant for your work.