结合贝叶斯最优分类器的GACV模型对医学彩色细胞图像进行分割

Bayesian Optimal Classifier for Medical Color Cell Image Segmentation Based on GACV Model

目的:本文对GACV模型进行改进,并用改进的模型对医学彩色细胞图像进行分割。方法:本文在GACV模型基础上加入了贝叶斯最优分类器,得到了结合贝叶斯最优分类器的GACV模型,并用该模型对医学彩色细胞图像进行分割。结果:应用本文提出的模型分割3组不同特点的医学彩色细胞图像,分割结果显示,该模型能正确将细胞从不同噪声环境中分割出来。结论:结合贝叶斯最优分类器的GACV模型对弱边界,噪声以及复杂背景有很强的鲁棒性可以有效、准确的分割医学彩色细胞图像。

Objective： In this article, we improve the GACV model and applied the improved model to medical color cell image segmentation. Methods： In this paper, by applying bayesian optimal classifier to GACV model, we can get a new model and applied this model to medical color image segmentation. Results： we applied the improved model to 3 groups of medical color cell image. The results show that the improved model can correctly separate out the cells from different noise environments. Conclusion： The improved model has strong robustness against weak border,noise and complex backgroud and can segment the medical color cell image effectively and accurately.