基于人工蜂群算法的SPECT-B超甲状腺图像配准

Registration of SPECT Image and B-type Ultrasound Image Based on Artificial Bee Colony Algorithm

为了提高甲状腺肿瘤检出的准确率,提出一种基于人工蜂群算法的SPECT和B超甲状腺图像配准。首先,针对来自两个不同成像设备的SPECT和B超甲状腺图像灰度差异大的特点,使用NSCT和GCBAC相结合的方法提取B超图像感兴趣的轮廓特征,用KFCM的方法提取SPECT图像的轮廓特征;然后以互信息作为相似性测度,建立仿射变换模型,并以改进的人工蜂群算法作为优化策略来优化配准所需的空间变换参数。实验结果表明,该方法可以有效提高配准速度,具有较好的配准效果。

In order to improve the accuracy in thyroid tumor detection, a registration of SPECT image and B-type ultrasound image based on artificial bee colony algorithm is improved. First, NSCT-GCBAC segmentation and KFCM segmentation are used respectively to extract the contours, according to the different characteristics of thyroid SPECT image and B-type ultrasound image from two imaging devices. Second, mutual information is used for similarity measure, and affine transform model is adopted to solve the space transform parameters. Finally, an improved artificial bee colony algorithm is used to optimize the space registration transform parameters. The experimental results show that the method can effectively improve the speed and the effect of registration.