Abstract
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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Petersen, K., Nielsen, M., Brandt, S.S. (2011). Conditional Point Distribution Models. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2010. Lecture Notes in Computer Science, vol 6533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18421-5_1
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DOI: https://doi.org/10.1007/978-3-642-18421-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18420-8
Online ISBN: 978-3-642-18421-5
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