Automatic segmentation of intracranial hematoma and volume measurement.
In this paper, a two-step segmentation method is developed for segmenting the hematoma area from brain CT images. The volume of hematoma area is calculated after the segmentation. During the second segmentation process, the method of two-dimensional entropy is introduced to separate hematoma. In using the method of two-dimensional entropy, most important is to find the optional threshold which can be achieved by an improved genetic algorithm (GA) i.e. hierarchical genetic algorithm (HGA). HGA is more efficient than simple GA in overcoming the shortcoming of standard GA in local optimal solution and low precision convergence. An experiment is designed to test the effectiveness of automatic segmentation. The results prove that the precision of automatic segmentation is better than artificial segmentation, and the clinical needs are met.
Liu
Boqiang
B
Shangdong University, China. bqliu@sdu.edu.cn
Yuan
Qingwei
Q
Liu
Zhongguo
Z
Li
Xiaomei
X
Yin
Xiaohong
X
D001921:Brain / Q000000981:diagnostic imaging*; D002543:Cerebral Hemorrhage / Q000000981:diagnostic imaging*; D006406:Hematoma / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional; D010363:Pattern Recognition, Automated; D014057:Tomography, X-Ray Computed