Abstract
This paper presents a method for segmenting abdominal organs from 3D abdominal CT images based on atlas selection and graph cut. The training samples are divided into multiple clusters based on the image similarity. The average image and atlas for each cluster are created. For an input image, we select the most similar atlas to the input image by measuring the image similarity between the input and average images. Segmentation of organs based on the MAP estimation using the selected atlas is then performed, followed by the precise segmentation by the graph cut algorithm. We applied the proposed method to a hundred cases of CT images. The experimental results showed that the extraction accuracy could be improved using multiple atlases, achieving more than 90% of the precision rate except for the pancreas.
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Oda, M. et al. (2012). Organ Segmentation from 3D Abdominal CT Images Based on Atlas Selection and Graph Cut. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28557-8_23
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DOI: https://doi.org/10.1007/978-3-642-28557-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28556-1
Online ISBN: 978-3-642-28557-8
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