Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector-Reference-Cited by-同舟云学术

Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector

Published:2018-11-03 Issue:12 Volume:42 Page:
ISSN:0148-5598
Container-title:Journal of Medical Systems
language:en
Short-container-title:J Med Syst

Author:

Arokia Jesu Prabhu L.,Jayachandran A.

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Link

http://link.springer.com/article/10.1007/s10916-018-1094-3/fulltext.html

Reference23 articles.

1. Ramsay, C. R., Matowe, L., Grilli, R., Grimshaw, J. M., and Thomas, R. E., Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies. Int. J. Technol. Assess. Health Care 19(4):613–623, 2003.

2. Zikic, B., Glocker, E. K., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O. M., Das, T., Jena, R., and Price, S. J., Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel. Journal of Medical Image Computing and Computer Assisted Intervention 7512:369–376, 2012.

3. Dhanasekaran, R., and Jayachandran, A., Severity analysis of brain tumor in MRI images uses modified multi-texton structure descriptor and kernel-SVM. Arab. J. Sci. Eng. 39(10):7073–7086, 2014.

4. Dubey, M. H., Gupta, S. K., and Gupta, S. K., Semi-automatic Segmentation of MRI Brain Tumor. Journal of Graphics, Vision and Image Processing. 9:33–40, 2009.

5. Kromer, C., Xu, J., Ostrom, Q. T. et al., Estimating the annual frequency of synchronous brain metastasis in the United States 2010-2013: a population-based study. J. Neuro-Oncol. 134(1):55–64, 2017.

Cited by 30 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi‐class brain tumor classification system in MRI images using cascades neural network;Computational Intelligence;2024-07-17

2. Segnet with Unet3+ and EfficientNet: a novel framework of brain tumour segmentation and classification model by multiscale attention-based deep learning techniques with hybrid heuristic improvement using 3D MRI brain images;The Imaging Science Journal;2024-04-06

3. Designing a deep hybridized residual and SE model for MRI image‐based brain tumor prediction;Journal of Clinical Ultrasound;2024-04-03

4. Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor;Expert Systems with Applications;2024-03

5. Automated Diagnosis of Brain Tumor Based on Deep Learning Feature Fusion Using MRI Images;2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC);2023-11-24