Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model.
Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model.
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基于滚动引导滤波器和尖峰皮质模型的医学图像融合
DOI：
10.1155/2015/156043
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发表时间：
2015
影响因子：
--
通讯作者：
Mingzhu S
中科院分区：
文献类型：
--
作者：
Shuaiqi L;Jie Z;Mingzhu S
医学图像融合在图像引导放射治疗、外科手术等疾病的诊断和治疗中发挥着重要作用。虽然已经提出了许多医学图像融合方法，但这些方法大多对噪声敏感，通常会导致融合图像失真和图像信息丢失。此外，它们在处理不同类型的医学图像时缺乏通用性。本文提出了一种新的医学图像融合方法，以克服现有方法的上述问题。它是由滚动制导滤波器(RGF)和尖峰皮层模型(SCM)相结合实现的。首先，利用RGF获取医学图像的显著特征。其次，利用源图像的均值和方差得到SCM的自适应阈值。最后由RGF系数激励的SCM得到融合后的图像。实验结果表明，该方法在主观视觉性能和客观评价指标上均优于目前流行的其他方法。
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF) and spiking cortical model (SCM). Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.
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影响因子：
3.3
作者：
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通讯作者：
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具有引导过滤的图像融合
影响因子：
10.6
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通讯作者：
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影响因子：
1.1
作者：
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通讯作者：
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影响因子：
18.6
作者：
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通讯作者：
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