Open Access
Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
---|---|---|
Article Number | 01157 | |
Number of page(s) | 15 | |
DOI | https://doi.org/10.1051/matecconf/202439201157 | |
Published online | 18 March 2024 |
- “Meningioma”, American Brain Tumor Association. http://www.abtatrialconnect.org A. Buades, B. Coll, J.M. Morel, A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 2005. [Google Scholar]
- A.O. Rodriguez, “Principles of magnetic resonance imaging”, Rev. Mex. Fis.2004. A.R.Kavitha, et al., “AnEfficient Approach for Brain Tumour Detection Based on Modified Region Growing and Network in MRI Images” IEEE, 2012. [Google Scholar]
- AbazarShamekhi, “An Improved Differential Evolution Optimization Algorithm”, IJRRAS 15 (2),2013, pp 132-145. [Google Scholar]
- Aboul Ella Hassanien, Ajith Abraham, James F. Peters, Gerald Schaefer, Christopher Henry, “Rough Sets and Near Sets in Medical Imaging: A Review”, IEEE Trans. on Information Technology in Biomedicine, 2009. [Google Scholar]
- Ahlem Melouah and Radia Amirouche, “Comparative study of automatic seed selection methods for medical image segmentation by region growing technique”, Recent Advances in Biology, Biomedicine and Bioengineering, 2014, pp 91-97. [Google Scholar]
- Ahmed Faisal, SharminParveen, ShahriarBadsha and HasanSarwar, “An Improved Image Denoising and Segmentation Approach for Detecting Tumor from 2-D MRI Brain Images”, International Conference on Advanced Computer Science Applications and Technologies,2012,pp 452-457. [Google Scholar]
- Alain Hore and Djemel Ziou, “Image quality metrics: PSNR vs. SSIM”, International Conference on Pattern Recognition, IEEE,2010,pp 2366–2369. [Google Scholar]
- Ali Ism et al., “Review of MRI-based brain tumor image segmentation using deep learning methods”, Procedia Computer Science 102,2016, pp 317 –324. [CrossRef] [Google Scholar]
- Ali Qusay Al-Faris, et al., “Breast MRI Tumour Segmentation using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering”, 17th Online World Conference on Soft Computing in Industrial Applications, Springer, 2012,pp 10-21. [Google Scholar]
- Amanpreet Kaur and M.D. Singh, “An Overview of PSO– Based Approaches in Image Segmentation”,International Journal of Engineering and Technology Volume 2, 2012,pp 1349-1357. [Google Scholar]
- Andrzej Bargiela, Witold Pedrycz, “The roots of Granular Computing”, IEEE, 2006 Andrzej Bargiela, Witold Pedrycz, “Toward a theory of Granular Computing for human-centred information processing”, IEEE,2006. [Google Scholar]
- Angulakshmi M And Lakshmi Priya G. G, “Automatic Brain Tumour Segmentation of Magnetic Resonance Images (MRI) based on Region of Interest (ROI)”, Journal of engineering science and technology, vol. 12, no. 4,2017,pp 875 –887. [Google Scholar]
- Anupurba Nandi, “Detection of human brain tumour using MRI image segmentation and morphological operators”, IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), 2015,pp 55–60. [Google Scholar]
- B. Shanthi Gowri and Gnanambal Ilango, “A Region Growing Segmentation Algorithm using Metric Topological Neighbourhoods”, International Journal of Pure and Applied Mathematics, Volume 106,2016,pp 175-185. [Google Scholar]
- Badri Narayan Subudhi, Veerakumar Thangaraj, Esakkirajan Sankaralingam, Ashish Ghosh “Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation”, Magnetic Resonance Imaging, 2016. [Google Scholar]
- C. Jayalakshmi, et al., “Analysis of Brain tumor techniques,” International Conference on Advanced Communication Control and Computing Technologies,2016, pp. 48-52. [Google Scholar]
- C. R. Traynora, et al., “Segmentation of the thalamus in MRI based on T1 and T2,” NeuroImage, vol. 56, no. 1,2011, pp 939–950. [CrossRef] [Google Scholar]
- C.Sasi varnan, et al., “Image Quality Assessment Techniques pn Spatial Domain”, International Journal of Computer Science and Technology, Vol. 2, Issue 3,2011,pp 177-184. [Google Scholar]
- Camille Couprie, Laurent Najman and Hugues Talbot, “Seeded Segmentation Methods for Medical Image Analysis”, Medical Image Processing Techniques and Applications, Springer,2011,pp 27-57. [CrossRef] [Google Scholar]
- Gollanapalli V Prasad, Kapil Sharma, Rama Krishna B, S Krishna Mohan Rao and Venkatadri M, “Labelled Classifier with Weighted Drift Trigger Model using Machine Learning for Streaming Data Analysis”, IJECS Vol. 13 No. 5 2022, doi.org/10.32985/ijeces.13.5.3 [Google Scholar]
- CH.Sravana Lakshmi et al., “Impulse Noise Removal Inimages Using Modified Trimmed Median Filter:Matlab Implementation And Comparitive Study”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, 2012,pp 2163-2166. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.