A stochastic approach for automated brain MRI segmentation-Reference-Cited by-同舟云学术

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A stochastic approach for automated brain MRI segmentation

Published:2021-01-18 Issue:3 Volume:15 Page:735-745
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Chatterjee Pubali¹^ORCID,Sharma Kaushik Das²,Chakrabarti Amlan¹

Affiliation:

1. A. K. Choudhury School of Information Technology University of Calcutta India

2. Department of Applied Physics University of Calcutta India

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12058

Reference69 articles.

1. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

2. Brain tumor segmentation from multimodal magnetic resonance images via sparse representation

3. Medical image analysis for cancer management in natural computing framework

4. Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification

5. Normalized cuts and image segmentation

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1. Self-Supervised Contrastive Learning for Automated Segmentation of Brain Tumor MRI Images in Schizophrenia;International Journal of Computational Intelligence Systems;2024-07-29

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3. M-Net: An encoder-decoder architecture for medical image analysis using ensemble learning;Results in Engineering;2023-03

4. TCRNet: Make Transformer, CNN and RNN Complement Each Other;ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2022-05-23

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