Abstract
Image segmentation is an initial, yet crucial procedure in a number of medical imaging systems. Despite the existence of numerous generic solutions that address this problem, there is still a need for developing fast and accurate techniques specialized at extracting particular organs from the CT scans. In this paper, we present an approach based on simple operations, which is controlled with a few easy-to-adjust parameters and works without any user interaction. The proposed approach, despite its simplicity, was shown to be reliable and efficient for a dataset of over 50 studies, containing both healthy and pathologic lungs.
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Acknowledgments
This research was supported by the National Centre for Research and Development under the Innomed Research and Development Grant No. POIR.01.02.00-00-0030/15.
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Walczak, M., Burda, I., Nalepa, J., Kawulok, M. (2017). Segmenting Lungs from Whole-Body CT Scans. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-58274-0_32
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