1. An active contour model for brain magnetic resonance image segmentation based on multiple descriptors
- Author
-
Yu Xiaosheng, Wu Chengdong, Wu Jiahui, and Chen Hong
- Subjects
Active contour model ,business.industry ,Computer science ,lcsh:Electronics ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,lcsh:TK7800-8360 ,02 engineering and technology ,Image segmentation ,Brain tissue ,lcsh:QA75.5-76.95 ,030218 nuclear medicine & medical imaging ,Computer Science Applications ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,Computer Science::Computer Vision and Pattern Recognition ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Segmentation ,Brain magnetic resonance imaging ,Artificial intelligence ,lcsh:Electronic computers. Computer science ,business ,Software - Abstract
With the increasing use of surgical robots, robust and accurate segmentation techniques for brain tissue in the brain magnetic resonance image are needed to be embedded in the robot vision module. However, the brain magnetic resonance image segmentation results are often unsatisfactory because of noise and intensity inhomogeneity. To obtain accurate segmentation of brain tissue, one new multiphase active contour model, which is based on multiple descriptors mean, variance, and the local entropy, is proposed in this study. The model can bring about a more full description of local intensity distribution. Also, the entropy is introduced to improve the performance of robustness to noise of the algorithm. The segmentation and bias correction for brain magnetic resonance image can be simultaneously incorporated by introducing the bias factor in the proposed approach. At last, three experiments are carried out to test the performance of the method. The results in the experiments show that method proposed in this study performed better than most current methods in regard to accuracy and robustness. In addition, the bias-corrected images obtained by proposed method have better visual effect.
- Published
- 2018