Brain Tumor Segmentation with Skull Stripping and Modified Fuzzy C-Means
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Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-13-1742-2_23
Reference10 articles.
1. Dale, A.M., Halgren, E.: Spatiotemporal mapping of brain activity by integration of multiple imaging modalities. Curr. Opin. Neurobiol. 11(2), 202–208 (2001)
2. Karnan, M., Logheshwari, T.: Improved implementation of brain MRI image segmentation using ant colony system. In: 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4. IEEE (2010)
3. A. Srivastava, A. Asati, and M. Bhattacharya, “A fast and noise-adaptive rough-fuzzy hybrid algorithm for medical image segmentation. In: 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 416–421. IEEE (2010)
4. Ayoobkhan, M.U.A., Chikkannan, E., Ramakrishnan, K.: Feed-forward neural network-based predictive image coding for medical image compression. Arab. J. Sci. Eng. 1–9 (2017)
5. Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004)
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