Abstract
In this paper, we propose a semi-supervised method for segmentation of the liver in three-dimensional (3D) magnetic resonance images (MRI), based on a Support Vector Machine (SVM) classifier. For segmentation, two classes have been considered: ‘Liver’ and ‘Background’. Firstly, an anisotropic diffusion filter is applied to eliminate noise in the image and generate a multi-band image. Then a method based on an edge detector is used to select the training set. This method minimizes the user intervention during the training process of the SVM. Finally, the 3D volume of the image is segmented by the SVM classifier. The experiments on real MRI have shown that the proposed method allows segmenting the liver with high accuracy.
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© 2014 Springer International Publishing Switzerland
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Moyano-Cuevas, J.L. et al. (2014). 3D Segmentation of MRI of the Liver Using Support Vector Machine. In: Roa Romero, L. (eds) XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. IFMBE Proceedings, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-00846-2_91
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DOI: https://doi.org/10.1007/978-3-319-00846-2_91
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00845-5
Online ISBN: 978-3-319-00846-2
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