Cascade pulse coupled neural network for multimodal medical image fusion

Yaqian Zhao; Aimin Hao; Qinping Zhao

doi:10.2495/HHME131561

Cascade pulse coupled neural network for multimodal medical image fusion

Yaqian Zhao
, Aimin Hao
, Qinping Zhao

School of Computer Science and Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposed a novel cascade pulse coupled neural network (CPCNN) with two-layer structure, which is used to multimodal medical image fusion. The first layer of CPCNN contains m single-channel PCNNs, which is used to calculate weighted coefficients for the second layer of CPCNN. The second layer of CPCNN is a multi-channel PCNN, which is used to fuse the source images. The proposed CPCNN model exploits the advantages of both the single-channel PCNN and multi-channel PCNN to obtain better fusion results. It retains the same fusing speed as multi-channel PCNN and achieves a better result similar to the single-channel PCNN and wavelet transform based method. Experimental results showed the better performance of CPCNN in both visual effect and objective evaluation criteria.

Original language	English
Title of host publication	Human Health and Medical Engineering
Editors	Zhenyu Du, Maozhu Jin
Publisher	WITPress
Pages	247-254
Number of pages	8
ISBN (Electronic)	9781845648923
ISBN (Print)	9781845648923
DOIs	https://doi.org/10.2495/HHME131561
State	Published - 2014
Event	2013 International Conference on Human Health and Medical Engineering, HHME 2013 - Wuhan, China Duration: 7 Dec 2013 → 8 Dec 2013

Publication series

Name	WIT Transactions on Biomedicine and Health
Volume	18
ISSN (Print)	1743-3525

Conference

Conference	2013 International Conference on Human Health and Medical Engineering, HHME 2013
Country/Territory	China
City	Wuhan
Period	7/12/13 → 8/12/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Medical image fusion
Multi-channel PCNN
Multimodal medical image
Pulse coupled neural network

Access to Document

10.2495/HHME131561

Cite this

@inproceedings{5741b51f88a8457790eb56488eb51d78,

title = "Cascade pulse coupled neural network for multimodal medical image fusion",

abstract = "This paper proposed a novel cascade pulse coupled neural network (CPCNN) with two-layer structure, which is used to multimodal medical image fusion. The first layer of CPCNN contains m single-channel PCNNs, which is used to calculate weighted coefficients for the second layer of CPCNN. The second layer of CPCNN is a multi-channel PCNN, which is used to fuse the source images. The proposed CPCNN model exploits the advantages of both the single-channel PCNN and multi-channel PCNN to obtain better fusion results. It retains the same fusing speed as multi-channel PCNN and achieves a better result similar to the single-channel PCNN and wavelet transform based method. Experimental results showed the better performance of CPCNN in both visual effect and objective evaluation criteria.",

keywords = "Medical image fusion, Multi-channel PCNN, Multimodal medical image, Pulse coupled neural network",

author = "Yaqian Zhao and Aimin Hao and Qinping Zhao",

year = "2014",

doi = "10.2495/HHME131561",

language = "英语",

isbn = "9781845648923",

series = "WIT Transactions on Biomedicine and Health",

publisher = "WITPress",

pages = "247--254",

editor = "Zhenyu Du and Maozhu Jin",

booktitle = "Human Health and Medical Engineering",

address = "英国",

note = "2013 International Conference on Human Health and Medical Engineering, HHME 2013 ; Conference date: 07-12-2013 Through 08-12-2013",

}

Zhao, Y, Hao, A & Zhao, Q 2014, Cascade pulse coupled neural network for multimodal medical image fusion. in Z Du & M Jin (eds), Human Health and Medical Engineering. WIT Transactions on Biomedicine and Health, vol. 18, WITPress, pp. 247-254, 2013 International Conference on Human Health and Medical Engineering, HHME 2013, Wuhan, China, 7/12/13. https://doi.org/10.2495/HHME131561

Cascade pulse coupled neural network for multimodal medical image fusion. / Zhao, Yaqian; Hao, Aimin ; Zhao, Qinping.
Human Health and Medical Engineering. ed. / Zhenyu Du; Maozhu Jin. WITPress, 2014. p. 247-254 (WIT Transactions on Biomedicine and Health; Vol. 18).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cascade pulse coupled neural network for multimodal medical image fusion

AU - Zhao, Yaqian

AU - Hao, Aimin

AU - Zhao, Qinping

PY - 2014

Y1 - 2014

N2 - This paper proposed a novel cascade pulse coupled neural network (CPCNN) with two-layer structure, which is used to multimodal medical image fusion. The first layer of CPCNN contains m single-channel PCNNs, which is used to calculate weighted coefficients for the second layer of CPCNN. The second layer of CPCNN is a multi-channel PCNN, which is used to fuse the source images. The proposed CPCNN model exploits the advantages of both the single-channel PCNN and multi-channel PCNN to obtain better fusion results. It retains the same fusing speed as multi-channel PCNN and achieves a better result similar to the single-channel PCNN and wavelet transform based method. Experimental results showed the better performance of CPCNN in both visual effect and objective evaluation criteria.

AB - This paper proposed a novel cascade pulse coupled neural network (CPCNN) with two-layer structure, which is used to multimodal medical image fusion. The first layer of CPCNN contains m single-channel PCNNs, which is used to calculate weighted coefficients for the second layer of CPCNN. The second layer of CPCNN is a multi-channel PCNN, which is used to fuse the source images. The proposed CPCNN model exploits the advantages of both the single-channel PCNN and multi-channel PCNN to obtain better fusion results. It retains the same fusing speed as multi-channel PCNN and achieves a better result similar to the single-channel PCNN and wavelet transform based method. Experimental results showed the better performance of CPCNN in both visual effect and objective evaluation criteria.

KW - Medical image fusion

KW - Multi-channel PCNN

KW - Multimodal medical image

KW - Pulse coupled neural network

UR - https://www.scopus.com/pages/publications/84905189942

U2 - 10.2495/HHME131561

DO - 10.2495/HHME131561

M3 - 会议稿件

AN - SCOPUS:84905189942

SN - 9781845648923

T3 - WIT Transactions on Biomedicine and Health

SP - 247

EP - 254

BT - Human Health and Medical Engineering

A2 - Du, Zhenyu

A2 - Jin, Maozhu

PB - WITPress

T2 - 2013 International Conference on Human Health and Medical Engineering, HHME 2013

Y2 - 7 December 2013 through 8 December 2013

ER -

Cascade pulse coupled neural network for multimodal medical image fusion

Abstract

Publication series

Conference

UN SDGs

Keywords

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