Research on fundus image registration and fusion method based on nonsubsampled contourlet and adaptive pulse coupled neural network
Research on fundus image registration and fusion method based on nonsubsampled contourlet and adaptive pulse coupled neural network
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基于非下采样轮廓波和自适应脉冲耦合神经网络的眼底图像配准与融合方法研究
DOI：
10.1007/s11042-019-08194-9
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发表时间：
2019-10
影响因子：
3.6
通讯作者：
Zhang Shihao
中科院分区：
文献类型：
--
作者：
Wu Jun;Ren Xingxing;Xiao Zhitao;Zhang Fang;Geng Lei;Zhang Shihao
提出了一种结合非下采样Contourlet（NSCT）和自适应脉冲耦合神经网络（PCNN）的荧光素眼底血管造影图像与彩色眼底图像的配准与融合方法。首先，通过SURF特征点法、最近邻距离比法和次最近邻距离比法对两幅图像进行配准，消除源图像之间的空间差异。其次，我们使用随机抽样一致性（RANSAC）算法来实现精确的匹配特征点。然后，根据RANSAC算法得到的变换参数，对浮动图像进行空间变换，完成配准。最后通过NSCT分解得到待融合图像的低频子带和高频子带。低频子带采用区域能量进行融合。高频子带的研究，使用一个简化的PCNN模型和粒子群优化算法。该算法采用改进的Laplacian能量作为连接强度，并根据像素点被点燃的次数进行图像融合。与现有的眼底图像融合方法相比，该方法具有更高的平均梯度（AG）值和信息熵（IE）值以及更低的相对全局维综合误差（ERGAS）。融合后的图像能够准确地综合图像信息，清晰地表现细节，在光谱范围内具有较好的光谱质量。融合后的图像为眼底疾病的临床诊断提供了有效的参考。
We present a registration and fusion method of fluorescein fundus angiography image and color fundus image which combines Nonsubsampled Contourlet (NSCT) and adaptive Pulse Coupled Neural Network (PCNN). Firstly, we register two images by Speeded Up Robust Features (SURF) feature points, the nearest neighbor and the next nearest neighbor distance ratio method to eliminate the spatial difference between the source images. Secondly, we use Random Sample Consensus (RANSAC) algorithm to achieve precise matching of feature points. Then, according to the transformation parameters obtained by RANSAC algorithm, we perform spatial transformation on the floating image to complete the registration. Finally, we obtain the low-frequency sub-band and high-frequency sub-band of the image to be fused by NSCT decomposition. The low-frequency sub-band is fused by the regional energy. The high-frequency sub-bands are studied using a simplified-PCNN model and the Particle Swarm Optimization algorithm. The link strength of the simplified-PCNN is an improved Laplacian energy and the images are fused based on the number of times the pixels are ignited. The proposed method has higher average gradient (AG) value and information entropy (IE) value and lower relative global dimensional synthesis error (ERGAS) than the existing fusion methods of the fundus image. The fusion image can accurately synthesize the image information, clarify the performance of the details, and has better spectral quality in the spectral range. The image of fused provides an effective reference for the clinical diagnosis of fundus diseases.
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DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
DOI：
--
发表时间：
1986
期刊：
--
影响因子：
--
作者：
S. Brody;G. Ruff
通讯作者：
S. Brody;G. Ruff
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
--
作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
DOI：
10.1007/978-3-319-44427-7
发表时间：
2016
期刊：
--
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1201/9781003206477-5
发表时间：
2021-08
期刊：
Evolutionary Optimization Algorithms
影响因子：
--
作者：
A. Badar
通讯作者：
A. Badar