Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis.

Journal: BMC medical imaging
PMID:

Abstract

BACKGROUND: In medical diagnosis of brain, the role of multi-modal medical image fusion is becoming more prominent. Among them, there is no lack of filtering layered fusion and newly emerging deep learning algorithms. The former has a fast fusion speed but the fusion image texture is blurred; the latter has a better fusion effect but requires higher machine computing capabilities. Therefore, how to find a balanced algorithm in terms of image quality, speed and computing power is still the focus of all scholars.

Authors

  • Kai Guo
    Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND 58202, USA. kai.guo@med.und.edu.
  • Xiongfei Li
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
  • Xiaohan Hu
    Department of Radiology, The First Hospital of Jilin University, Changchun, China. xhhu@jlu.edu.cn.
  • Jichen Liu
    College of Software, Jilin University, Changchun, China.
  • Tiehu Fan
    College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China.