NDLI: Multimodal medical image fusion using fuzzy radial basis function neural networks

Content Provider	IEEE Xplore Digital Library
Author	Yang-Ping Wang Jian-Wu Dang Qiang Li Sha Li
Copyright Year	2007
Description	Author affiliation: Lanzhou Jiaotong Univ., Lanzhou (Yang-Ping Wang; Jian-Wu Dang; Qiang Li; Sha Li)
Abstract	Most medical images are blurry for two reasons: one is that noise signal blurs the high frequency signal of image edge, the other one is the border of tumor with normal tissues cannot be very well defined on the images, therefore it is difficult for radiology experts to delineate image. It is necessary that an approach based on fuzzy inference fusion is used for coping with these difficulties. The paper puts forward a new method for multimodal medical image fusion, that fuzzy radial basis function neural networks (fuzzy-RBFNN) is used to perform auto-adaptive image fusion. Global genetic algorithm (GA) is employed to train the networks. In the experiments, samples sets include artificial blurred medical image. Experimental results show, comparing with other image fusion method, the proposed approach is more excellent for multimodal medical images both in visual effect of fused images and in objective evaluation criteria, especially for blurry source images.
Starting Page	778
Ending Page	782
File Size	243088
Page Count	5
File Format	PDF
ISBN	9781424410651
DOI	10.1109/ICWAPR.2007.4420774
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	fuzzy inference RF signals Neoplasms Genetic algorithms genetic algorithm Radial basis function networks Radiology Fuzzy neural networks Frequency Visual effects Multimodal medical image fusion blurry image Image fusion Biomedical imaging radial basis function neural networks
Content Type	Text
Resource Type	Article

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