Abstract
The growing technology in medical image processing helps in quick as well as an accurate analysis of several life threatening diseases. Interestingly, domain of brain tumor analysis has effectively utilized this trend to automate core steps, i.e. extraction, detection, and the most important proximate segmentation for tumor examination. To diagnose neurological disorders magnetic resonance (MR) imaging methods are of great help. Discussing the MR image types this paper briefs the parameters influencing the process of brain tumor detection. Also, the study proposes a hybrid segmentation approach combining k-means with fuzzy c-means (FCM) and support vector machine (SVM) with fuzzy c-means. Experimentation performed show that fusion outperforms three of the base approaches in brain tumor identification on DICOM dataset using 200 T1W and T2W MR images. The evaluation parameters show that k-means combined with fuzzy c-means produce better accuracy. Results further prove applicability of the proposal in detecting ranges and shapes of brain tumor using MR 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
References
Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R., Murtagh, F.R., Silbiger, M.S.: Automatic tumor segmentation using knowledge-based techniques. IEEE Trans. Med. Imaging 17(2), 187–201 (1998)
Kaus, M.R., Warfield, S.K., Nabavi, A., Black, P.M., Jolesz, F.A., Kikinis, R.: Automated segmentation of MR images of brain tumors. Radiology 218(2), 586–591 (2001)
Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31(8), 1426–1438 (2013)
Georgiadis, P., et al.: Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features. Comput. Methods Programs Biomed. 89(1), 24–32 (2008)
Dvorak, P., Bartusek, K., Kropatsch, W., Smékal, Z.: Automated multi-contrast brain pathological area extraction from 2D MR images. J. Appl. Res. Technol. 13(1), 58–69 (2015)
Vishnuvarthanan, G., Rajasekaran, M.P., Subbaraj, P., Vishnuvarthanan, A.: An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl. Soft Comput. 38, 190–212 (2016)
Abdel-Maksoud, E., Elmogy, M., Al-Awadi, R.: Brain tumor segmentation based on a hybrid clustering technique. Egypt. Inform. J. 16(1), 71–81 (2015)
Vishnuvarthanan, A., Rajasekaran, M.P., Govindaraj, V., Zhang, Y., Thiyagarajan, A.: An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images. Appl. Soft Comput. 57, 399–426 (2017)
Srinivas, B., Rao, G.S.: Unsupervised learning algorithms for MRI brain tumor segmentation. In: 2018 Conference on Signal Processing And Communication Engineering Systems (SPACES), pp. 181–184, January 2018
Zhou, J., Chan, K., Chong, V., Krishnan, S.M.: Extraction of brain tumor from MR images using one-class support vector machine. In: IEEE Engineering in Medicine and Biology 27th Annual Conference, vol. 2006, pp. 6411–6414. IEEE (2005)
Chaplot, S., Patnaik, L., Jagannathan, N.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Process. Control 1(1), 86–92 (2006)
Kharrat, A., Gasmi, K., Messaoud, M.B., Benamrane, N., Abid, M.: A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo J. Sci. 17(1), 71–82 (2010)
Singh, A., et al.: Detection of brain tumor in MRI images, using combination of fuzzy C-means and SVM. In: 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 98–102. IEEE (2015)
Ehrlich, R., Bezdek, J.C., Full, W.: FCM the fuzzy C-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chahal, P.K., Pandey, S., Goel, S. (2019). Hybrid Approaches for Brain Tumor Detection in MR Images. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-9939-8_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9938-1
Online ISBN: 978-981-13-9939-8
eBook Packages: Computer ScienceComputer Science (R0)