论文标题
双树复合小波变换的新型自适应优化，用于医学图像融合
A Novel adaptive optimization of Dual-Tree Complex Wavelet Transform for Medical Image Fusion
论文作者
论文摘要
近年来，医学图像融合领域都取得了许多研究成就。融合基本上是提取最佳输入并将其传达给输出的。医学图像融合意味着将几种各种模态图像信息一起理解以形成一个图像以表达其信息。图像融合的目的是整合互补和冗余信息。在本文中，提出了基于双树复合小波变换（DT-CWT）和自适应粒子群优化（APSO）的多模式图像融合算法。通过使用来自源图像的分解金字塔的DTCWT系数形成融合金字塔来实现融合。系数由基于像素的加权平均方法融合，并且权重由APSO估算以获得最佳的融合图像。融合图像是通过常规的逆双树复杂小波转换重建过程获得的。实验结果表明，基于自适应粒子群优化算法的提议方法比基于粒子群优化的方法要好得多。在视觉上和通过基准（例如熵（E），峰信号与噪声比，（PSNR），均方根误差（RMSE），标准偏差（SD）和结构相似性指标（SSIM）计算进行比较。
In recent years, many research achievements are made in the medical image fusion field. Fusion is basically extraction of best of inputs and conveying it to the output. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. In this paper, a multimodal image fusion algorithm based on the dual-tree complex wavelet transform (DT-CWT) and adaptive particle swarm optimization (APSO) is proposed. Fusion is achieved through the formation of a fused pyramid using the DTCWT coefficients from the decomposed pyramids of the source images. The coefficients are fused by the weighted average method based on pixels, and the weights are estimated by the APSO to gain optimal fused images. The fused image is obtained through conventional inverse dual-tree complex wavelet transform reconstruction process. Experiment results show that the proposed method based on adaptive particle swarm optimization algorithm is remarkably better than the method based on particle swarm optimization. The resulting fused images are compared visually and through benchmarks such as Entropy (E), Peak Signal to Noise Ratio, (PSNR), Root Mean Square Error (RMSE), Standard deviation (SD) and Structure Similarity Index Metric (SSIM) computations.
