[1]田娟秀,刘国才. 基于NSST变换和PCNN的医学图像融合方法[J].中国医学物理学杂志,2018,35(8):914-920.[doi:DOI:10.3969/j.issn.1005-202X.2018.08.010]
　TIAN Juanxiu,LIU Guocai. Medical image fusion method based on non-subsampled shearlet transform and pulse coupled neural network[J].Chinese Journal of Medical Physics,2018,35(8):914-920.[doi:DOI:10.3969/j.issn.1005-202X.2018.08.010]
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 基于NSST变换和PCNN的医学图像融合方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第8期
页码:
914-920
栏目:
医学影像物理
出版日期:
2018-08-20

文章信息/Info

Title:
 Medical image fusion method based on non-subsampled shearlet transform and pulse coupled neural network
文章编号:
1005-202X（2018）08-0914-07
作者:
 田娟秀1; 2; 刘国才1
 1.湖南大学电气与信息工程学院， 湖南 长沙 410082； 2.湖南工程学院计算机与通信学院， 湖南 湘潭 411104
Author(s):
 TIAN Juanxiu1;  2;  LIU Guocai1
 1. School of Electrical and Information Engineering, Hunan University, Changsha 410082, China; 2. School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China
关键词:
 ; 【关键词】非下采样剪切波变换; 脉冲耦合神经网络; 医学图像融合; PET; CT; MRI
Keywords:
 ; Keywords: non-subsampled shearlet transform;  pulse coupled neural network;  medical image fusion;  positron emission tomography;  computed tomography;  magnetic resonance imaging
分类号:
R318；TP391.4
DOI:
DOI:10.3969/j.issn.1005-202X.2018.08.010
文献标志码:
A
摘要:
 【摘 要】 目的：融合PET/CT/MRI医学图像，使结果图像尽可能包含更多边缘和纹理特征等信息，以更好地区分病变、肿瘤与正常组织器官，为疾病诊断提供更多的有用信息。 方法：提出一种基于非下采样剪切波变换（NSST）和脉冲耦合神经网络（PCNN）模型的融合方法。首先，根据图像局部区域能量和，对图像NSST低频系数进行加权融合；然后，根据PCNN神经元的点火次数，选择图像NSST高频方向系数；最后，通过逆NSST变换，得到融合后的图像。 结果：分别对7组MRI/PET和CT/PET图像进行融合实验，其结果图像具有很好的视觉效果，且在互信息、边缘相似性、梯度相似性及空间频率4个指标综合评价中较其它算法更优。 结论：本方法可以自适应捕获边缘和纹理信息，具有良好的融合效果。
Abstract:
 Abstract: Objective To fuse positron emission tomography/computed tomography/magnetic resonance imaging (PET/CT/MRI) images for providing more information such as edge and texture features in fused images to distinguish lesions and tumors from normal tissues and organs and providing more useful information for diagnosis. Methods A fusion method based on non-subsampled shearlet transform (NSST) and pulse coupled neural network (PCNN) model was proposed. A weighted method based on the total of local regional energy was applied to fuse NSST low-frequency coefficients. Then based on the times of PCNN neuron activation, the NSST high-frequency direction coefficients were selected. Finally, the fused images were obtained by inverse NSST. Results Experiments performed on 7 groups of MRI/PET and CT/PET image sets demonstrated that the image visual effects of the fused images were good and that the proposed method had better performances than other algorithms in the comprehensive assessment of mutual information, edge similarity, gradient similarity and spatial frequency. Conclusion The proposed method can adaptively preserve the edge and textures of the source images and achieve a good fusion performance.

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备注/Memo

备注/Memo:
 【收稿日期】2018-02-11
【基金项目】国家自然科学基金（61671204, 61471166, 61771189）；湖南省科技计划重点研发项目（2016WK2001）
【作者简介】田娟秀，博士，讲师，主要研究方向：医学图像分析、模式识别与人工智能，E-mail: juanxiutian@126.com
【通信作者】刘国才，教授，博士生导师，主要研究方向：医学图像分析、模式识别与智能系统，E-mail: lgc630819@hnu.edu.cn
更新日期/Last Update: 2018-07-26