基于稀疏理论与快速有限剪切波变换的医学图像融合算法

在临床医学领域, 图像辅助诊断对医学视图的处理效果要求很高. 针对医学图像融合过程中图像视觉效果较差的问题, 提出了一种基于稀疏理论与快速有限剪切变换的医学图像融合算法, 提高了医学图像处理效率. 首先, 采用快速有限剪切波变换(FFST)分解源图像, 将其分解为高频系数和低频系数; 其次, 根据高频系数和低频系数的不同性质, 提供不同的融合策略, 通过相对标准差比较法对高频系数进行处理, 对于稀疏性较差的低频系数利用K-SVD方法训练, 得到字典并采用稀疏原理进行处理; 最后, 将融合后的高频和低频系数通过FFST逆变换融合到医学图像中. 实验结果表明, 算法的图像融合效果好, 尤其是在提高图像清晰度等方面, 具有良好的实用价值和应用前景.

Medical Image Fusion Algorithm Based on Sparse Theory and Fast Finite Shear Wave Transform

In the field of clinical medicine, image aided diagnosis has a high demand for the effect of medical view processing. To solve the problem of poor visual effect in medical image fusion, a medical image fusion algorithm based on sparse theory and fast finite shear transform is proposed, which improves the efficiency of medical image processing. Firstly, Fast Finite Shear wave Transform (FFST) is used to decompose the source image into high-frequency coefficients and low-frequency coefficients. Secondly, according to the different properties of high-frequency coefficients and low-frequency coefficients, different fusion strategies are provided, and the high-frequency coefficients are processed by the relative standard deviation comparison method. For the low-frequency coefficients with poor sparsity, K-SVD method is used to train, and a dictionary is obtained Finally, the high-frequency and low-frequency coefficients after fusion are fused into the medical image by FFST inverse transform. The experimental results show that the image fusion effect of the algorithm is good, especially in improving the image clarity and so on, which has sound practical value and application prospects.