Multimodal Image Fusion for Brain Image Based on Nonsubsampled Shearlets Transform

DOI

10.2991/ncce-18.2018.12How to use a DOI?

Keywords

Medical Image Fusion; Nonsubsampled Shear Let’s Transform; HIS Transform.

Abstract

Multi-modal medical image fusion has been emerging as a new and promising research area due to the increasing demands in clinical application. A novel fusion rule for medical brain image fusion is proposed in this paper based on the Nonsubsampled Shearlets Transform (NSST). The functional image is converted into HIS color space for bettered-correlation of intensity channel from chromatic channels. The high frequency sub-bands are fused by using fusion rule based on edge feature measurement. The low frequency sub-bands are combined by using fusion rule based on information and clarity measurement. The performance of the proposed fusion method is assessed by experiment, and the results indicate the proposed method outperforms the traditional approaches.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Na Zhang
AU  - Peiguang Wang
PY  - 2018/05
DA  - 2018/05
TI  - Multimodal Image Fusion for Brain Image Based on Nonsubsampled Shearlets Transform
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 69
EP  - 74
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.12
DO  - 10.2991/ncce-18.2018.12
ID  - Zhang2018/05
ER  -