Open AccessAdopting Quaternion Wavelet Transform to Fuse Multi-Modal Medical Images
Author(s) -
Peng Geng,
Xiuming Sun,
Jianhua Liu
Publication year - 2017
Publication title -
journal of medical and biological engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.3
H-Index - 35
eISSN - 2199-4757
pISSN - 1609-0985
DOI - 10.1007/s40846-016-0200-6
Subject(s) - image fusion , artificial intelligence , fuse (electrical) , fusion , fusion rules , modal , computer science , medical imaging , pattern recognition (psychology) , computer vision , wavelet transform , image (mathematics) , wavelet , engineering , linguistics , philosophy , polymer chemistry , electrical engineering , chemistry
Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In this paper, we propose a novel multi-modal medical image fusion method based on simplified pulse-coupled neural network and quaternion wavelet transform. The proposed fusion algorithm is capable of combining not only pairs of computed tomography (CT) and magnetic resonance (MR) images, but also pairs of CT and proton-density-weighted MR images, and multi-spectral MR images such as T1 and T2. Experiments on six pairs of multi-modal medical images are conducted to compare the proposed scheme with four existing methods. The performances of various methods are investigated using mutual information metrics and comprehensive fusion performance characterization (total fusion performance, fusion loss, and modified fusion artifacts criteria). The experimental results show that the proposed algorithm not only extracts more important visual information from source images, but also effectively avoids introducing artificial information into fused medical images. It significantly outperforms existing medical image fusion methods in terms of subjective performance and objective evaluation metrics.