Denoising, Segmentation and Characterization of Brain Tumor from Digital MR Images
- Rahul Malhotra
- Minu Sethi
- ParminderKumar Luthra
Abstract
The objective of this paper is to present an automated segmentation method which allows rapid identification of Tumor tissues/pathological structure with an accuracy and reproducibility comparable to those of manual segmentation. The authors uses the wiener filter for the removal of noise and then applies a new marker based watershed segmentation method using image processing and digital processing algorithms to detect Tumor tissues of Brain. This method is simple and intuitive in approach and provides higher computational efficiency along with the exact segmentation of an image. The proposed technique has been implemented on MATLAB 7.3 and the results are compared with the existing techniques.- Full Text: PDF
- DOI:10.5539/cis.v4n6p83
This work is licensed under a Creative Commons Attribution 4.0 License.
Journal Metrics
WJCI (2022): 0.636
Impact Factor 2022 (by WJCI): 0.419
h-index (January 2024): 43
i10-index (January 2024): 193
h5-index (January 2024): N/A
h5-median(January 2024): N/A
( The data was calculated based on Google Scholar Citations. Click Here to Learn More. )
Index
- Academic Journals Database
- BASE (Bielefeld Academic Search Engine)
- CiteFactor
- CNKI Scholar
- COPAC
- CrossRef
- DBLP (2008-2019)
- EBSCOhost
- EuroPub Database
- Excellence in Research for Australia (ERA)
- Genamics JournalSeek
- Google Scholar
- Harvard Library
- Infotrieve
- LOCKSS
- Mendeley
- PKP Open Archives Harvester
- Publons
- ResearchGate
- Scilit
- SHERPA/RoMEO
- Standard Periodical Directory
- The Index of Information Systems Journals
- The Keepers Registry
- UCR Library
- Universe Digital Library
- WJCI Report
- WorldCat
Contact
- Chris LeeEditorial Assistant
- cis@ccsenet.org