计算机工程 ›› 2018, Vol. 44 ›› Issue (9): 224-229.doi: 10.19678/j.issn.1000-3428.0048000

• 图形图像处理 • 上一篇    下一篇

基于压缩感知与自适应PCNN的医学图像融合

高媛,贾紫婷,秦品乐,王丽芳   

  1. 中北大学 计算机与控制工程学院,太原 030051
  • 收稿日期:2017-07-18 出版日期:2018-09-15 发布日期:2018-09-15
  • 作者简介:高媛(1972—),女,副教授,主研方向为图像处理、人工智能;贾紫婷,硕士研究生;秦品乐、王丽芳,副教授。
  • 基金项目:

    山西省自然科学基金(2015011045)。

Medical Image Fusion Based on Compressive Sensing and Adaptive PCNN

GAO Yuan,JIA Ziting,QIN Pinle,WANG Lifang   

  1. School of Computer and Control Engineering,North University of China,Taiyuan 030051,China
  • Received:2017-07-18 Online:2018-09-15 Published:2018-09-15

摘要:

针对非下采样轮廓波变换(NSCT)域内基于脉冲耦合神经网络(PCNN)的图像融合方法融合效果较差、计算复杂度较高等问题,提出一种在非下采样剪切波变换(NSST)域内基于压缩感知(CS)和自适应PCNN的融合算法。源图像在NSST域内被分解成高低频,采用改进的PCNN融合低 频子带系数,使用像素的平方差总和当作其激励因素,选取方向梯度总和作为其链接强度,对计算量较大的高频子带系数采用CS进行处理,经过NSST逆变换获得融合图像。实验结果表明,与NSCT融合算法、NSST与PCNN相结合的算法等相比,该算法能获得较好的信息熵、空间频 率、边缘信息评价因子,且运行时间较短。

关键词: 压缩感知, 非下采样剪切波变换, 脉冲耦合神经网络, 图像融合, 核磁共振成像

Abstract:

Aiming at the problem of poor performance and high computational complexity in the image fusion algorithm which based on Pulse Coupled Neural Network(PCNN) in the Non-subsampled Contourlet Transform(NSCT) domain,an fusion algorithm based on Compressed Sensing(CS) and adaptive PCNN in NSST domain is proposed.The source image is decomposed into high and low frequencies in the NSST domain.An improved PCNN is used to fuse the low frequency subband coefficients.The sum of the squared differences of the pixels is used as the excitation.The sum of the direction gradients is used as the link strength,and the high frequency which need lots of calculations are processed using CS,and the fused image is obtained by inverse transform of NSST.Experimental results show that the algorithm performs better in information entropy,spatial frequency,edge information evaluation factor,and the running time is shorter compared with NSCT fusion algorithm,NSST combined with PCNN algorithm,and so on.

Key words: Compressive Sensing(CS), Non-subsampled Shearlet Transform(NSST), Pulse Coupled Neural Network(PCNN), image fusion, Magnetic Resonance Imaging(MRI)

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