基于PCNN图像分割的医学图像融合算法

Medical image fusion algorithm based on PCNN image segmentation

为充分提取源图像间的互补信息,改进传统的图像融合算法在亮度维持、能量保留、边缘信息保持等方面的不足,本文提出了基于脉冲耦合神经网络(pulse coupled neural network, PCNN)图像分割的医学图像融合算法。该算法综合了非下采样剪切波变换(non-subsampled shearlet transform, NSST)与PCNN。首先,选取标准差较大的源图像作为被分割图像,标准差较小的源图像作为参照图像,将源图像进行NSST分解,获取源图像低频子带系数和高频子带系数;在低频融合中,利用参数自适应的PCNN对被分割图像的低频子带进行分割,根据分割结果获取融合低频子带系数;在高频融合中,采用以区域能量和与拉普拉斯能量和两者的乘积作为判断函数,获取融合高频子带系数;利用NSST逆变换获取融合图像。最后,应用本文提出的算法,对脑萎缩、急性中风和高血压性脑病等3组电脑断层扫描/磁共振成像(computerized tomography/magnetic resonance imaging, CT/MRI)图像进行了融合仿真,并将仿真结果与2018年后国际刊上提出的5种算法的融合图像进行比较。结果表明,应用本文提出的融合算法得到的图像,有效地增强了不同模态间的信息互补,保持了融合图像与源图像具有相同明亮程度,又保留了源图像低亮度部分的边缘信息,更加符合人眼视觉特性,具有更高的客观评价指标。

In order to fully extract the complementary information between source images and improve the shortcomings of traditional image fusion algorithms in brightness maintenance, energy preservation and edge information preservation, a medical image fusion algorithm based on pulse coupled neural network(PCNN) image segmentation is proposed in this paper.The algorithm combines non-subsampled shearlet transform(NSST) and PCNN.Firstly, the source image with large standard deviation is selected as the segmented image and the source image with small standard deviation is used as the reference image.The source image is decomposed by NSST to obtain the low-frequency subband coefficients and high-frequency subband coefficients of the source image;In the low-frequency fusion, the parameter adaptive PCNN is used to segment the low-frequency subband of the segmented image, and the fused low-frequency subband coefficients are obtained according to the segmentation results;In high-frequency fusion, the product of regional energy and Laplace energy is used as the judgment function to obtain the fusion high-frequency subband coefficient;The fused image is obtained by inverse NSST transform.Finally, using the algorithm proposed in this paper, the fusion simulation of three groups of computerized tomography/magnetic resonance imaging(CT/MRI) images such as brain atrophy, acute stroke and hypertensive encephalopathy is carried out, and the simulation results are compared with the fusion images of five proposed algorithms in the international journal after 2018.The results show that the image obtained by using the fusion algorithm proposed in this paper effectively enhances the information complementarity between different modes, maintains the same brightness between the fused image and the source image, and retains the edge information of the low brightness part of the source image, which is more in line with the human visual characteristics and has higher objective evaluation indexes.