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Integrating Map-Reduce and Stream-Processing for Efficiency (MRSP) Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Segmenting Lungs from Whole-Body CT Scans

Authors : Maksym Walczak, Izabela Burda, Jakub Nalepa, Michal Kawulok

Published in: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

Publisher: Springer International Publishing

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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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Literature
1.
Annangi, P., Thiruvenkadam, S., et al.: A region based active contour method for X-Ray lung segmentation using prior shape and low level features. In: Biomedical Imaging From Nano to Macro. IEEE (2010)
2.
Costa, A., Carvalho, B.: SALSA–A simple automatic lung segmentation algorithm. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 501–508 (2015)
3.
Cyganek, B., Graña, M., Porwik, P., Wozniak, M.: Intelligent methods applied to health-care information systems. Appl. Artif. Intell. 30(6), 495–496 (2016)CrossRef
4.
5.
van Ginneken, B., et al.: Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: Proceedings of IEEE ISBI, pp. 286–289 (2015)
6.
Hu, S., Hoffmann, E.: Automatic lung segmentation for accurate quantitation of volumetric X-Ray CT images. IEEE Trans. Med. Imaging 20(6), 490–498 (2001)CrossRef
7.
Kawulok, M., Kawulok, J., Nalepa, J., Smolka, B.: Self-adaptive algorithm for segmenting skin regions. EURASIP J. Adv. Sig. Proc. 2014, 170 (2014)CrossRef
8.
Mansoor, A., et al.: A generic approach to pathological lung segmentation. IEEE Trans. Med. Imaging 33(12), 2293–2310 (2014)CrossRef
9.
Mostafa, A., Elfattah, M.A., Fouad, A., Hassanien, A.E., Hefny, H.: Enhanced region growing segmentation for CT liver images. Adv. Intell. Syst. Comput. 407, 115–127 (2015)CrossRef
10.
Nalepa, J., Kawulok, M.: Fast and accurate hand shape classification. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 364–373. Springer, Cham (2014). doi:10.​1007/​978-3-319-06932-6_​35 CrossRef
11.
Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. SMC–9(1), 62–66 (1979)MathSciNetCrossRef
12.
Perez, M.G., et al.: A multi-level thresholding method based on histogram derivatives for accurate brain MRI segmentation. Rev. Politcnica 35, 82 (2015)
13.
Schlegl, T., Ofner, J., Langs, G.: Unsupervised pre-training across image domains improves lung tissue classification. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Zhang, S., Cai, W.T., Metaxas, D. (eds.) MCV 2014. LNCS, vol. 8848, pp. 82–93. Springer, Cham (2014). doi:10.​1007/​978-3-319-13972-2_​8
14.
Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J.: Multi-scale convolutional neural networks for lung nodule classification. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 588–599. Springer, Cham (2015). doi:10.​1007/​978-3-319-19992-4_​46 CrossRef
15.
Shin, H.C., Roth, H., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35, 1285–1298 (2016)CrossRef
16.
Siminski, K.: Clustering with missing values. Fundam. Inform. 123(3), 331–350 (2013)MATH
17.
Starosolski, R.: New simple and efficient color space transformations for lossless image compression. J. Vis. Commun. Image Represent. 25(5), 1056–1063 (2014)CrossRef
18.
Sternberg, S.: Biomedical image processing. IEEE Comput. 16(1), 22–34 (1983)CrossRef
19.
20.
Wang, Q., et al.: HOSVD-based 3D active appearance model: segmentation of lung fields in CT images. J. Med. Syst. 40(176), 1–11 (2016)
21.
Zghidi, H., Walczak, M., et al.: Image processing and analysis of textile fibers by virtual random walk. In: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, vol. 5, pp. 717–720 (2013)
22.
Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)CrossRef
Metadata
Title
Segmenting Lungs from Whole-Body CT Scans
Authors
Maksym Walczak
Izabela Burda
Jakub Nalepa
Michal Kawulok
Copyright Year
2017
Book
Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

Print ISBN: 978-3-319-58273-3

Electronic ISBN: 978-3-319-58274-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-58274-0_32

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