计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 194-199.doi: 10.11896/jsjkx.190700185
所属专题: 医学图像
李昌兴1, 雷柳2, 张晓璐2
LI Chang-xing1, LEI Liu2, ZHANG Xiao-lu2
摘要: 图像在融合过程中容易引入伪吉布斯现象,在图像的边缘细节处容易产生“伪影”和振铃现象。针对以上问题,提出了一种基于形态学图像增强和脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)的脑部CT与MRI图像融合方法。首先基于形态学对源图像进行开运算和闭运算增强处理,再将增强处理过的图像作为PCNN接收域的输入激励,输入PCNN融合模型内,对模型输出的权重图进行判定,形成一幅清晰并且易于处理的图像。实验结果表明,所提方法在保持边缘清晰化、保留有效信息、平衡冗余现象方面都优于其他方法,经过形态学图像增强和PCNN融合后的图像相较于未经增强处理的PCNN方法所得图像的平均梯度提高了24.59%左右,空间频率提高了42.56%左右;相较于基于拉普拉斯的图像融合方法,图像的标准差提高了16.67%左右。
中图分类号:
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