Abstract
This paper presents an automatic method for liver tumor segmentation in CT scans. The proposed segmentation algorithm is a two-stage process. In the first step, the curvature filter is employed for removing the noise in CT images, and a trained mask is used to be a spatial regularization to constrain our segmentation in a specific region. In the second step, basing on the preprocessed results, we combine random forest with fuzzy clustering to segment liver tumor. In the experiments, the proposed method obtains promising results on the liver tumor segmentation challenge testing dataset. The calculated mean scores of Dice, volume of overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), and maximum symmetric surface distance (MSD) are 0.47, 0.65, −0.35, 11.49, and 64.31, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Christ P (2017) LiTS – liver tumor segmentation challenge. http://competitions.codalab.org/competitions/15595
Gong Y, Sbalzarini IF (2017) Curvature filters efficiently reduce certain variational energies. IEEE Trans Image Process 26(4):1786–1798
Häme Y, Pollari M (2012) Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation. Med Image Anal 16(1): 140–149
Hoogi A, Beaulieu CF, Cunha GM, Heba E, Sirlin CB, Napel S, Rubin DL (2017) Adaptive local window for level set segmentation of CT and MRI liver lesions. Med Image Anal 37: 46–55
Hoogi A, Subramaniam A, Veerapaneni R, Rubin DL (2017) Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis. IEEE Trans Med Imaging 36(3):781–791. https://doi.org/10.1109/TMI.2016.2628084
Linguraru MG, Richbourg WJ, Liu J, Watt JM, Pamulapati V, Wang S, Summers RM (2012) Tumor burden analysis on computed tomography by automated liver and tumor segmentation. IEEE Trans Med Imaging 31(10):1965–1976. https://doi.org/10.1109/TMI.2012.2211887
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J et al (2015) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993–2024. https://doi.org/10.1109/TMI.2014.2377694
Yaqub M, Javaid MK, Cooper C, Noble JA (2014) Investigation of the role of feature selection and weighted voting in random forests for 3-D volumetric segmentation. IEEE Trans Med Imaging 33(2):258–271. https://doi.org/10.1109/TMI.2013.2284025
Acknowledgements
This study was supported by National Nature Science Foundation of China for funding (Grant Nos. 11531005). Thanks to the organizers of the LiTS Challenge for the public liver tumor dataset.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ma, J. et al. (2019). Automatic Liver Tumor Segmentation Based on Random Forest and Fuzzy Clustering. In: Jiang, M., Ida, N., Louis, A., Quinto, E. (eds) The Proceedings of the International Conference on Sensing and Imaging. ICSI 2017. Lecture Notes in Electrical Engineering, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-91659-0_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-91659-0_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91658-3
Online ISBN: 978-3-319-91659-0
eBook Packages: EngineeringEngineering (R0)