An Unsupervised Learning with Feature Approach for Brain Tumor Segmentation Using Magnetic Resonance Imaging-Reference-Cited by-同舟云学术

An Unsupervised Learning with Feature Approach for Brain Tumor Segmentation Using Magnetic Resonance Imaging

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19
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Author:

Ejaz Khurram¹,Rahim Mohd Shafy Mohd¹,Bajwa Usama Ijaz²,Rana Nadim³,Rehman Amjad⁴

Affiliation:

1. UTM, Johor Bahru, Malaysia

2. Computer Science department, COMSATS University Islamabad, Lahore Campus, Pakistan

3. Jazzan University, Jazzan, Saudi Arabia

4. Al Yamama University, Riyadh, Saudi Arabia

Publisher

ACM Press

Reference26 articles.

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2. Bauer, S., Fejes, T., and Reyes, M. (2013). A skull-stripping filter for ITK. Insight Journal, 2012.

3. Pereira, S., Pinto, A., Alves, V., and Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE transactions on medical imaging, 35(5), 1240--1251.

4. Cordier, N., Menze, B., Delingette, H., and Ayache, N. (2013). Patch-based segmentation of brain tissues. Paper presented at the MICCAI challenge on multimodal brain tumor segmentation, 6--17.

5. Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al. (2015). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, 34(10), 1993.

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