针对当前多模态医学图像融合方法中功能与结构信息互补性不强,易出现边缘失真与轮廓模糊等现象, 提出了基于局部极值分解耦合显著特征的医学图像融合方案。引入局部极值,将源图像在不同尺度下分解为一系 列的平滑与细节子图像；利用Canny算子获得边缘显著加权映射,以保持源图像的结构信息,并通过上下文感知算子来输出色彩显著加权映射, 提取色彩与亮度信息；分别定义基于边缘和色彩的显著特征函数,将其作为加权映 射系数的融合准则,得到平滑与细节融合图像；对平滑与细节图像进行重构,形成新图像。结果表明与当前融合技术相比, 在CT图像与MRI图像、CT图像与PET图像融合中,所提方法得到的边缘与轮廓更清晰,细节更丰富。提 出算法具有较高的融合质量,在医学、遥感与红外探测等领域有一定的应用价值。

In view of the fact that the complementarily of functional and structural information is not strong in the multimodal medical image fusion method, which easily appears the phenomenon of edge distortion and contour blur, a medical image fusion scheme based on the salient features coupling local extreme value decomposition is proposed. The local extremum is introduced to decompose the source image into a series of smoothing sub-images in different scales. Canny operator is used to obtain the edge weighted mapping to keep the structure information of source image. The color perception mapping is extracted by the context aware operator. The fusion criterion based on the edge and color saliency feature functions as the weighted mapping coefficients are defined respectively to get smooth and detail fusion image. A new image is reconstructed from the smooth and detail image. Experimental results show that in CT and MRI, CT and PET image fusion, the edges and contours obtained by the proposed method are clearer and richer in details compared with the current fusion algorithms. The proposed algorithm has good fusion quality which has certain application value in the fields of medicine, remote sensing and infrared detection.