Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients
Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients
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DOI：
10.1016/j.inffus.2012.03.002
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发表时间：
2014-09-01
影响因子：
18.6
通讯作者：
Tian, Lian-Fang
中科院分区：
文献类型：
--
作者：
Wang, Lei;Li, Bin;Tian, Lian-Fang
融合结果的质量取决于从源图像中获取的信息量，因此，提出了一种基于平移不变剪切波变换（Shift-Invariant Shearlet Transform，SIST）域的多模态医学图像融合方法。将两状态隐马尔可夫树（HMT）模型扩展到SIST域，以描述跨尺度和子带间SIST系数的依赖关系。在此基础上，分析了传统的均值-最大值融合方法不能作为医学图像融合的最佳准则的原因，提出了一种新的融合方法，即利用SIST系数的概率密度函数和标准差来计算融合系数。最后，直接应用逆SIST得到融合图像。将SIST与HMT模型相结合，可以保留更多的奇异点的空间特征信息，并将更多的功能信息内容传递到融合结果中。视觉和统计分析表明，该方法在熵和互信息、边缘信息、标准差、峰值信噪比和结构相似度等方面均优于5种典型方法。此外，色彩失真可以在很大程度上得到抑制，提供更好的视觉感受。(C)2012爱思唯尔有限公司版权所有。
For the quality of the fused outcome is determined by the amount of the information captured from the source images, thus, a multi-modal medical image fusion method is developed in the shift-invariant shearlet transform (SIST) domain. The two-state Hidden Markov Tree (HMT) model is extended into the SIST domain to describe the dependent relationships of the SIST coefficients of the cross-scale and inter-subbands. Base on the model, we explain why the conventional Average-Maximum fusion scheme is not the best rule for medical image fusion, and therefore a new scheme is developed, where the probability density function and standard deviation of the SIST coefficients are employed to calculate the fused coefficients. Finally, the fused image is obtained by directly applying the inverse SIST. Integrating the SIST and the HMT model, more spatial feature information of the singularities and more functional information contents can be preserved and transferred into the fused results. Visual and statistical analyses demonstrate that the fusion quality can be significantly improved over that of five typical methods in terms of entropy and mutual information, edge information, standard deviation, peak signal to noise and structural similarity. Besides, color distortion can be suppressed to a great extent, providing a better visual sense. (C) 2012 Elsevier B.V. All rights reserved.