Comparison of Filtering Methods and Segmentation Techniques for Brain Tumor MR Images
10.3969/j.issn.1005-5185.2015.07.020
- VernacularTitle:基于MRI脑肿瘤的滤波方法与分割技术对比研究
- Author:
Ziyou ZHOU
;
Qi LIU
;
Jing REN
- Publication Type:Journal Article
- Keywords:
Brain neoplasms;
Glioma;
Magnetic resonance imaging;
Image processing,computer-assisted;
Algorithms
- From:
Chinese Journal of Medical Imaging
2015;(7):553-556,560
- CountryChina
- Language:Chinese
-
Abstract:
PurposeTo explore the segmentation accuracy of different filtering and segmentation methods in brain tumor MRI, and to identify the best algorithm for brain glioma.Materials and Methods Using the nonlocal average ifltering, median ifltering, the anisotropic ifltering and improved mean shift algorithm segmentation, the watershed segmentation algorithm, fuzzy c-means segmentation algorithm to realize image segmentation in MATLAB program, 39 glioma images from different patients were analyzed. Pathology manual segmentation was used as gold standard to evaluate different segmentation precision.Results The signal-to-noise ratio was 7.9243, 6.2160 and 6.5426 for different iflter methods, respectively. The segmentation methods accuracy was 92.31%, 88.03% and 84.93%, respectively.Conclusion The nonlocal average ifltering effect is more accurate than median ifltering and the anisotropic ifltering. The improved mean shift algorithm segmentation is more accurate than watershed segmentation algorithm and fuzzy c-means segmentation algorithm with precision of 92.31%.
