Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (7): 2092-2099.DOI: 10.11772/j.issn.1001-9081.2020081303

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles     Next Articles

Medical image fusion based on edge-preserving decomposition and improved sparse representation

PEI Chunyang, FAN Kuangang, MA Zheng   

  1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China
  • Received:2020-08-27 Revised:2020-12-11 Online:2021-07-10 Published:2021-07-22
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61763018), the 03 Special Project and 5G Program of Science and Technology Department of Jiangxi Province (20193ABC03A058).


裴春阳, 樊宽刚, 马政   

  1. 江西理工大学 电气工程与自动化学院, 江西 赣州 341000
  • 通讯作者: 樊宽刚
  • 作者简介:裴春阳(1995-),男,山东临沂人,硕士研究生,主要研究方向:医学图像处理、信息融合;樊宽刚(1981-),男,江西赣州人,副教授,博士,主要研究方向:数字信号处理,自适应控制;马政(1995-),男,河南信阳人,硕士研究生,主要研究方向:物联网。
  • 基金资助:

Abstract: Aiming at the problems of artifacts and loss of details in multimodal medical fusion, a two-scale multimodal medical image fusion method framework using multiscale edge-preserving decomposition and sparse representation was proposed. Firstly, the source image was decomposed at multiple scales by utilizing an edge-preserving filter to obtain the smoothing and detail layers of the source image. Then, an improved sparse representation fusion algorithm was employed to fuse the smoothing layers, and on this basis, an image block selection based strategy was proposed to construct the dataset of the over-complete dictionary and the dictionary learning algorithm was used for training the joint dictionary, as well as a novel multi-norm based activity level measurement method was introduced to select the sparse coefficients; the detail layers were merged by an adaptive weighted local regional energy fusion rule. Finally, the fused smoothing layer and detail layers were reconstructed with multi-scale to obtain the fused image. Comparison experiments were conducted on the medical images from three different imaging modalities. The results demonstrate that the proposed method preserves more salient edge features with the improvement of contrast and has advantages in both visual effect and objective evaluation compared to other multi-scale transform and sparse representation methods.

Key words: edge-preserving decomposition, smooth layer, detail layer, over-complete dictionary, improved sparse representation, activity level

摘要: 针对多模态医学图像融合中容易产生伪影且存在细节缺失的问题,提出一种利用多尺度边缘保留分解和稀疏表示的二尺度多模态医学图像融合方法框架。首先利用边缘保留滤波器对源图像进行多尺度分解,得到源图像的平滑层和细节层。然后,将改进的稀疏表示算法用于融合平滑层,并在此基础上提出一种基于图像块筛选的策略来构建过完备字典的数据集,再利用字典学习算法训练出一种联合字典,同时引入一种多范数的活跃度度量方法选择稀疏系数;细节层的融合则采用自适应加权局部区域能量的融合规则。最后将融合后的平滑层和细节层进行多尺度重构得到融合图像。针对三类不同成像模态的医学图像进行对比实验,结果表明,该方法较其他多尺度变换和稀疏表示的方法能够保留更多显著的边缘特征,对比度也有明显提升,在视觉效果和客观评价上都具有一定优势。

关键词: 边缘保留分解, 平滑层, 细节层, 过完备字典, 改进稀疏表示, 活跃度

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