Abstract
Liver segmentation in MR Image is the first step of our automated liver perfusion analysis project. Traditional Level Set methods and active contours were often used to segment the liver, but the results were not always promising due to noise and the low gradient response on the liver boundary. In this paper we propose a novel level set based variational approach that incorporates shape prior knowledge into the improved Chan-Vese’s model [1] which can overcome the leakage and over-segmentation problems. The experiments are taken on abdomen MRI series and the results reveal that our improved level set based shape prior method can segment liver shape precisely and a refined liver perfusion curve without respiration affection can be achieved.
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Cheng, K., Gu, L., Wu, J., Li, W., Xu, J. (2008). A Novel Level Set Based Shape Prior Method for Liver Segmentation from MRI Images. In: Dohi, T., Sakuma, I., Liao, H. (eds) Medical Imaging and Augmented Reality. MIAR 2008. Lecture Notes in Computer Science, vol 5128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79982-5_17
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DOI: https://doi.org/10.1007/978-3-540-79982-5_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79981-8
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