| 吴骏,孙明明,肖志涛,张芳,耿磊.联合蚁群算法和PCNN的脑部MRI图像分割方法[J].光电子激光,2014,(3):614~619 |
| 联合蚁群算法和PCNN的脑部MRI图像分割方法 |
| Ant colony optimization combined with PCNN for brain MRI image segmentation |
| 投稿时间：2013-08-05 |
| DOI： |
| 中文关键词: 医学图像分割 脉冲耦合神经网络(PCNN) 蚁群算法(ACO) |
| 英文关键词:medical image segmentation pulse coupled neural network (PCNN) ant c olony optimization (ACO) |
| 基金项目:国家自然科学基金(61102150)、天津市科技支撑计划重点项目(12ZC ZDGX02100)和天津市高等学校科技发展基金(20120805)资助项目 (天津工业大学 电子与信息工程学院,天津 300387) |
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| 中文摘要: |
| 采用蚁群算法(ACO)联合脉冲耦合神经网络(PCNN )的脑部磁共振成像(MRI)图像分割方法。其中利用ACO解决了PCNN参数设置困 难的问题,同时能够克服图像的低对比度和噪声对图像分割的影响,实现图像的精确分割。 首先利用ACO的全局搜索能力,以图像信息熵与灰度期望值的和作为ACO的目标函数 ,对PCNN的3个关键参 数β、αE和VE进行设定；然后基于PCNN简化模型,结合 最大熵值准则对脑部MRI图像进行分割；最后 对分割结果进行面积滤波,得到最终的分割结果。实验结果表明,本文方法能够实现脑部MR I图像的自动 分割,具有较高的精度和较强的鲁棒性。对于没有噪声的图像,本文方法分割结果的平均正 确提取率达到 97.0%以上,平均错误提取率达到0.4%以下, 平均杰卡德相似系数达到94.8% 以上；对于添加了不同级别噪声的图像,本文方法的分割效果也优于FCM和自适应PCNN。 |
| 英文摘要: |
| A novel method that combines ant colony optimization (ACO) and pulse coupled neu ral network (PCNN) for brain MRI image segmentation is proposed.The ACO is used to solve the problem of parameters setting for PCNN.The influence of low contrast and noise on image se gmentation is overcome and accurate segmentation can be achieved.Firstly,the sum of image information ent ropy and gray mean is used as the target function of ACO and the global search ability of ACO is used to set three key parameters of PCNN.Then the brain MRI image is segmented by the simp lified PCNN model which combines the maximum entropy criterion.Finally,the ultimate segment ation result is obtained via area filtering.Experimental results show that the proposed method can segment brain MRI image automatically and achieve relatively high accuracy and robustness.For images without noise,the average correct extraction rate of the method is above 97.0%,the average error extraction rate of it is below 0.4% and the average Jaccard similarity coefficient of it is above 94.8%.For images a dded with different levels of noise,segmentation results of the method are bette r than those of FCM and adaptive PCNN. |
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