格子波尔兹曼(lattice Boltzmann, LB)分割模型具有算法简单、运算快捷的优点,但对于低对比度和受到噪声污染的图像,经常产生欠分割或者过分割现象.为此,引入图像局部区域统计信息,构建了一种新的格子波尔兹曼图像分割模型.为验证该模型及算法的分割性能,在相似性系数和豪斯多夫距离等评价技术指标下,利用真实脑磁共振图像作为实验数据进行分割,并与现有LB分割模型以及水平集分割模型进行对比.实验结果表明,该模型在分割精度方面比现有LB模型提高10倍,在计算速度方面比传统水平集分割模型提高3倍.
The lattice Boltzmann (LB) model has advantages of simple programming and faster operation, but for images with low contrast and noise, segmentation may fail. This paper proposes a novel LB model using local statistical region information. As the method can enhance contrast of the object and background, and reduce noise, it provides improved delineation accuracy. To verify effectiveness of the model, comparison experiments among the existing LB model, level set models and the proposed model are made, using real magnetic resonance (MR) images. Dice coefficient and Hausdorf distance are used as the measurement index. The results show that the proposed model produces segmentation results with precision 10 times better than the existing LB method. In addition, the computing speed is 3 times faster than level set models.
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