| Issue |
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 7 | |
| Section | Session 2: Bioinformatics | |
| DOI | https://doi.org/10.1051/itmconf/20171202001 | |
| Published online | 05 September 2017 | |
A New Model-CELBF for Medical Image Segmentation Based on Image Entropy
1 Department of Mathematical Sciences Delaware State University, Dover, DE, USA
2 School of Science Beijing University of Posts and Telecommunications, Beijing, China
3 Department of Mathematical Sciences Delaware State University, Dover, DE, USA
* czhang11@students.desu.edu
** ycguo@bupt.edu.cn
*** fliu@desu.edu
A new model (named CELBF) for medical image segmentation based on LBF and image entropy is proposed in this paper. We introduced image entropy to deal with the inhomogeneity of image gray level. Some real medical images are processed by using this new model and finite difference algorithm. The results show that new model improves the speed of segmentation and increases noise robustness. Compared with LBF model, the new model can segment inhomogeneity medical image more quickly and more accurately. Meanwhile the CELBF model has more strong robustness with noise.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.