E-fuzzy feature fusion and thresholding for morphology segmentation of brain MRI modalities-Reference-Cited by-同舟云学术

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E-fuzzy feature fusion and thresholding for morphology segmentation of brain MRI modalities

Published:2021-03-01 Issue:13 Volume:80 Page:19715-19735
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Kumar G. Anand,Sridevi P. V.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-020-08760-6.pdf

Reference32 articles.

1. Alansary A, Ismail M, Soliman A, Khalifa F, Nitzken M, Elnakib A, Zurada JM (2016) Infant brain extraction in T1-weighted MR images using BET and refinement using LCDG and MGRF models. IEEE Journal of Biomedical and Health Informatics 20(3):925–935

2. Binaghi, E., Omodei, M., Pedoia, V., Balbi, S., Lattanzi, D., & Monti, E. (2014). Automatic segmentation of MR brain tumor images using support vector machine in combination with graph cut. In IJCCI (NCTA), pp. 152–157.

3. Chen H, Dou Q, Yu L, Qin J, Heng PA (2017) VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage

4. Deepa AR, Emmanuel WS (2019 May 1) An efficient detection of brain tumor using fused feature adaptive firefly backpropagation neural network. Multimed Tools Appl 78(9):11799–11814

5. Deng Y, Ren Z, Kong Y, Bao F, Dai Q (2017) A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst 25(4):1006–1012

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