基于3-D剪切波和广义高斯模型的多模态医学序列图像融合

Multi-modal Medical Volumetric Image Fusion Based on 3-D Shearlet Transform and Generalized Gaussian Model

鉴于大多数传统的多模态医学图像融合算法面临无法处理医学序列图像的局限性,提出了一种基于3-D剪切波(3DST)和广义高斯模型的多模态医学序列图像融合方法。首先,通过3-D剪切波变换获得序列图像的低频部分和高频部分;其次,低频部分采用一种新的基于局部能量的融合方法;然后,高频部分采用基于广义高斯模型(Gene-ralized Gaussian Model,GGD)和模糊逻辑的融合方法;最后,通过3-D剪切波的逆变换获得融合的医学序列图像。通过实验对融合图像的主客观性能进行比较,结果表明所提算法获得了更好的融合效果。

In view of the limitation of most traditional multi-modal medical image fusion methods that cannot deal with the medical volumetric images,this paper presented a multi-modal medical volumetric image fusion method based on 3-D shearlet transform(3DST)and generalized gaussian model.Firstly,the preregistered medical volumetric images are decomposed into low frequency parts and high frequency parts by using the 3DST.Next,a novel fusion rule with the local energy is performed on the low frequency subbands.Moreover,an effective fusion rule based on Generalized Gaussian Model(GGD)and fuzzy logic is proposed for integrating the high frequency subbands.Finally,the fused image is obtained by the inverse 3DST.Through subjective and objective performance comparison,experiments on medical volumetric images show that the proposed method can obtain better fusion results.