Medical image fusion using m-PCNN
Medical image fusion using m-PCNN
复制标题
使用 m-PCNN 进行医学图像融合
DOI：
10.1016/j.inffus.2007.04.003
复制
发表时间：
2008-04-01
影响因子：
18.6
通讯作者：
Ma, Yide
中科院分区：
文献类型：
--
作者：
Wang, Zhaobin;Ma, Yide
医学图像融合在图像引导手术、图像引导放射治疗、无创诊断和治疗计划等临床应用中发挥着重要作用。脉冲耦合神经网络（PCNN）是从猫视皮层神经元的同步爆发现象中衍生出来的。然而，由于PCNN的模型存在一定的缺陷，将其直接应用于图像融合领域是非常困难的。虽然已经在开发各种医学图像算法方面做了大量的研究工作，但是这些方法的一个缺点是它们不能处理不同种类的医学图像。在这种情况下，我们首次提出了一种新的多通道模型--m-PCNN，并将其应用于医学图像融合。本文首先描述了m-PCNN的数学模型，然后详细介绍了m-PCNN的一种特殊情况--双通道模型。为了证明m-PCNN可以处理多模态医学图像，我们使用了四对不同模态的医学图像作为我们的实验对象。同时，与其他方法（对比度金字塔，FSD金字塔，梯度金字塔，拉普拉斯金字塔等）相比，使用互信息准则研究各种方法的性能和相对重要性。实验结果表明，该方法在视觉效果和客观评价标准上都优于其他方法。(c)2007 Elsevier B. V.保留所有权利。
Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, non-invasive diagnosis, and treatment planning. Pulse coupled neural network (PCNN) is derived from the synchronous neuronal burst phenomena in the cat visual cortex. However, it is very difficult to directly apply original PCNN into the field of image fusion, because its model has some shortcomings. Although a significant amount of research work has been done in developing various medical image algorithms, one disadvantage with the approaches is that they cannot deal with different kinds of medical images. In this instance, we propose a novel multi-channel model -m-PCNN for the first time and apply it to medical image fusion, In the paper, firstly the mathematical model of m-PCNN is described, and then dual-channel model as a special case of m-PCNN is introduced in detail. In order to show that the m-PCNN can deal with multimodal medical images, we used four pairs of medical images with different modalities as our experimental subjects. At the same time, in comparison with other methods (Contrast pyramid, FSD pyramid, Gradient pyramid, Laplacian pyramid, etc.), the performance and relative importance of various methods is investigated using the Mutual Information criteria. Experimental results show our method outperforms other methods, in both visual effect and objective evaluation criteria. (c) 2007 Elsevier B.V. All rights reserved.