To avoid biopsies, doctors use non invasive medical techniques such as the computed tomography. Even that, the detection of the liver remains a big challenge because of the gray level and shape variations which depend on patients and acquisition modalites. In this work, we propose to create a 3D liver model in the training phase of 3D active shape model algorithm. This training model will be deformed according to any given 3D data for liver segmentation. The contribution of our work is the use of the Non-rigid registration with a B-spline registration on the training phase. We tested our method on an open access database ("3D-IRCADb") and on our database obtained from the radiology department of the National Oncology Institute of Tunis. Both data-sets showed the reliability of the method with an accuracy equal to 69.98% and 71.18% respectively for our database and "3D-IRCADb".
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.