The fuzzy and complex glioma boundary can cause inaccurate segmentation of the glioma. In order to solve this problem, this paper proposed a new glioma segmentation algorithm combining GLH with improved cellular automaton. Firstly, this method fused T2-weighted and fluid attenuated inversion recovery MR images of brain glioma. Then, it used the histogram feature to enhance glioma region. And, it calculated the weighted distance eigenvector of glioma images. Finally, it utilized the improved algorithm of cellular automata to obtain the segmentation result of glioma tissues. It separately segmented twenty groups of brain tumor segmentation database data and ten groups of clinical glioma data. The average segmentation accuracy rate of the entire tumor area and core tumor area reached to 90.76% and 89.73% respectively. The experimental results show that compared with the contrast method, the proposed algorithm can better segment the glioma region with obvious contrast. And it solves the problem of inaccurate segmentation due to the fuzzy glioma region to some extent. While, it also improves the accuracy and robustness without increasing the complexity.

[2019-09-05] Printed Article /doc/2018.03.0193/v2