Multimodal medical image fusion aims to reduce insignificant information and improve clinical diagnosis accuracy. The purpose of image fusion is to retain salient image features and detail information of multiple source images to yield a more informative fused image. A hybrid algorithm based on both pixel and feature levels of multimodal medical image fusion is presented in this paper. For the pixel-level fusion, the source images are decomposed into low- and high-frequency components using Discrete Wavelet Transform (DWT), and then the low-frequency coefficients are fused using maximum fusion rule. Thereafter, the curvelet transform is applied on the high-frequency coefficients. The obtained high-frequency subbands (fine scale) are fused using Principal Component Analysis (PCA) fusion rule. On the other hand, the feature-level fusion is accomplished by extracting various features form the coarse and detail subbands and using them for the fusion process. These features involve mean, variance, entropy, visibility, and standard deviation. Thereafter, the inverse curvelet transform is implemented on the fused high-frequency coefficients, and finally the resultant fused image is acquired by applying the inverse DWT on the fused low- and high-frequency components. The proposed method is evaluated and implemented on different pairs of medical image modalities. The results demonstrate that the proposed method improves the quality of the final fused image in terms of Mutual Information (MI), Correlation Coefficient (CC), entropy, Structural Similarity index (SSIM), Edge Strength Similarity for Image quality (ESSIM), Peak Signal-to-Noise Ratio (PSNR), and edge-based similarity measure (Q^AB/F).

多模式医学图像融合旨在减少无关紧要的信息并提高临床诊断准确性。图像融合的目的是保留多个源图像的显着图像特征和详细信息，以产生更具信息量的融合图像。提出了一种基于像素和特征水平的多峰医学图像融合的混合算法。对于像素级融合，使用离散小波变换（DWT）将源图像分解为低频分量和高频分量，然后使用最大融合规则融合低频系数。此后，将曲线波变换应用于高频系数。使用主成分分析（PCA）融合规则对获得的高频子带（精细比例）进行融合。另一方面，特征级融合是通过从粗糙和细节子带中提取各种特征并将它们用于融合过程来完成的。这些特征包括均值，方差，熵，可见性和标准偏差。此后，对融合后的高频系数执行反曲波变换，最后通过对融合后的低频和高频分量应用反DWT来获得最终的融合图像。所提出的方法是在不同的医学图像模态对上评估和实现的。结果表明，该方法在互信息方面提高了最终融合图像的质量（和标准偏差。此后，对融合后的高频系数执行反曲波变换，最后通过对融合后的低频和高频分量应用反DWT来获得最终的融合图像。所提出的方法是在不同的医学图像模态对上评估和实现的。结果表明，该方法在互信息方面提高了最终融合图像的质量（和标准偏差。此后，对融合后的高频系数执行反曲波变换，最后通过对融合后的低频和高频分量应用反DWT来获得最终的融合图像。所提出的方法是在不同的医学图像模态对上评估和实现的。结果表明，该方法在互信息方面提高了最终融合图像的质量（MI），相关系数（CC），熵，结构相似性指数（SSIM），图像质量的边缘强度相似度（ESSIM），峰值信噪比（PSNR）和基于边缘的相似性度量（Q ^{AB / F}）。