Abstract
In this paper, an improved segmentation approach based on Neutrosophic sets (NS) and fuzzy c-mean clustering (FCM) is proposed. An application of abdominal CT imaging has been chosen and segmentation approach has been applied to see their ability and accuracy to segment abdominal CT images. The abdominal CT image is transformed into NS domain, which is described using three subsets namely; the percentage of truth in a subset T, the percentage of indeterminacy in a subset I, and the percentage of falsity in a subset F. The entropy in NS is defined and employed to evaluate the indeterminacy. Threshold for NS image is adapted using Fuzzy C-mean algorithm. Finally, abdominal CT image is segmented and liver parenchyma is selected using connected component algorithm. The proposed approach denoted as NSFCM and compared with FCM using Jaccard Index and Dice Coefficient. The experimental results demonstrate that the proposed approach is less sensitive to noise and performs better on nonuniform CT images.
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
Preview
Unable to display preview. Download preview PDF.
References
Cheng, H.D., Guot, Y., Zhang, Y.: A Novel Image Segmentation Approach Based on Neutrosophic Set And Improved Fuzzy C-Means Algorithm. New Mathematics and Natural Computation 7(1), 155–171 (2011)
Smarandache, F.: A Unifying Field in Logics Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability, 3rd edn. American Research Press (2003)
Cheng, H.D., Guot, Y., Zhang, Y.: A novel image segmentation approach based on neutrosophic set and improved fuzzy c-means algorithm. New Mathematics and Natural Computation 7(1), 155–171 (2011)
Mohan, J., Krishnaveni, V., Guo, Y.: A new neutrosophic approach of wiener Filtering for MRI denoising. Measurement Science Review 13(4), 177–186 (2013)
Cao, B.-Y., Wang, G.-J., Guo, S.-Z., Chen, S.-l. (eds.): Fuzzy Information and Engineering 2010. AISC, vol. 78. Springer, Heidelberg (2010)
Maji, P., Pal, S.: Maximum Class Separability for Rough-Fuzzy CMeansBased Brain MR Image Segmentation. T. Rough Sets 9, 114–134 (2008)
Guo, Y., Cheng, H.D., Zhang, Y., Zhao, W.: A new neutrosophic appraoch to image thresholding. In: Proceedings of the 11th Joint Conference on Information Sciences, pp. 1–6. Atlantis Press (2008)
Zhang, M., Zhang, L., Cheng, H.D.: A neutrosophic approach to image segmentation based on watershed method. Signal Processing 90, 1510–1517 (2010)
Mohan, J., Krishnaveni, V., Guo, Y., Kanchana, J.: MRI denoising based on neutrosophic wiener filtering. In: IEEE International Conference on Imaging Systems and Techniques (IST), pp. 327–331 (2012)
Zhang, M., Zhang, L., Cheng, H.D.: A neutrosophic approach to image segmentation based on watershed method. Signal Processing 90, 1510–1517 (2010)
Abdominal CT Dataset, http://radiopaedia.org/search?q=CTscope=all
Anter, A.M., Azar, M., Hassanien, A.T., El-Bendary, A., Abu-ElSoud, N., Automatic, M.: Computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 193–198. IEEE (2013)
Ge, F., Wang, S., Liu, T.: New benchmark for image segmentation evaluation. J. Electronic Imaging 16(3), 33011 (2007)
Frakes, W.B., Yates, R.B.: Information Retrieval, Data Structure and Algorithms. Prentice Hall (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Anter, A.M., Hassanien, A.E., ElSoud, M.A.A., Tolba, M.F. (2014). Neutrosophic Sets and Fuzzy C-Means Clustering for Improving CT Liver Image Segmentation. In: Kömer, P., Abraham, A., Snášel, V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-08156-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-08156-4_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08155-7
Online ISBN: 978-3-319-08156-4
eBook Packages: EngineeringEngineering (R0)