Medical image fusion using multi-level local extrema
Medical image fusion using multi-level local extrema
复制标题
使用多级局部极值的医学图像融合
DOI：
10.1016/j.inffus.2013.01.001
复制
发表时间：
2014-09-01
影响因子：
18.6
通讯作者：
Xu, Zhiping
中科院分区：
文献类型：
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作者：
Xu, Zhiping
医学成像数据的融合已经成为图像引导手术和放射治疗等生物医学应用中的一个中心问题。与传统的图像表示方法相比，多层局部极值(MLE)表示法具有许多优点。本文提出了一种基于最大似然估计的多模式医学图像融合算法。该方法在最大似然估计模式下将输入图像分解为粗细层，并利用局部能量和对比度融合规则在不同层中选择系数。这保留了源图像中的更多细节，并进一步提高了融合图像的质量。最终的融合图像是由选择的系数在粗细层中叠加得到的。以三组不同来源的医学图像为实验对象，说明了该方法的性能。我们还将我们的方法与使用累积互信息、客观图像融合性能度量、空间频率和盲质量指数的其他方法进行了比较。实验结果表明，该方法在主观和客观评价标准上都取得了较好的效果。(C)2013爱思唯尔B.V.保留所有权利。
The fusion of data for medical imaging has become a central issue in such biomedical applications as image-guided surgery and radiotherapy. The multi-level local extrema (MLE) representation has been shown to have many advantages over conventional image representation methods. In this paper, we propose a new fusion algorithm for multi-modal medical images based on MLE. Our method enables the decomposition of input images into coarse and detailed layers in the MLE schema, and utilizes local energy and contrast fusion rules for coefficient selection in the different layers. This preserves more detail in the source images and further improves the quality of the fused image. The final fused image is obtained from the superposition of selected coefficients in the coarse and detailed layers. We illustrate the performance of the proposed method using three groups of medical images from different sources as our experimental subjects. We also compare our method with other techniques using cumulative mutual information, the objective image fusion performance measure, spatial frequency, and a blind quality index. Experimental results show that our method achieves a superior performance in both subjective and objective assessment criteria. (C) 2013 Elsevier B.V. All rights reserved.