Image Fusion Based on Adaptive Coefficient Gauss Wavelet

Yong Li; Zhuang Miao

doi:10.4028/www.scientific.net/AMR.1049-1050.1641

Paper Titles

Secondary Classification on One-against-Rest Multiclassification Structure
p.1622

Research of Classification Methods of EEG Signal Based on Wavelet Packet Transform and LVQ Neural
p.1626

Cluster Analysis of Seismic Data Based on Region Scalable Fitting Model
p.1631

A Novel Text Data Mining Method Based on Neural Network and its Application
p.1637

Image Fusion Based on Adaptive Coefficient Gauss Wavelet
p.1641

Image Denoising Based on K-SVD with Adaptive Dictionary Size
p.1645

Three-Dimensional Simulation of Virtual Plants Branch Structure Based on Quasi Binary-Tree and L System
p.1650

Hyper-Parameters Selection of LS-SVM Based on PSO Algorithm with Multi-Particles Sharing Strategy
p.1654

Hopfield Neural Network Modeling Method Based on Subset KPCA with FCMC Clustering
p.1658

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1049-1050Image Fusion Based on Adaptive Coefficient Gauss...

Image Fusion Based on Adaptive Coefficient Gauss Wavelet

Abstract:

According to CT and MR image features, we presented the medical image fusion algorithm based on adaptive Gaussian wavelet, which uses gray level co-occurrence matrix to modify space coefficients adaptively. Compared with other wavelet algorithm by simulation, the results proved that the proposed algorithm obtained valid information in a highly automated manner, and without human intervention or prior information.

You might also be interested in these eBooks

Modern Technologies in Materials, Mechanics and Intelligent Systems

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1049-1050)

Pages:

1641-1644

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1641

Citation:

Cite this paper

Online since:

October 2014

Authors:

Yong Li*, Zhuang Miao

Keywords:

Gauss Wavelet, Gray Level Co-Occurrence Matrix (GLCM), Medical Image

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Wellmer J, von Oertzen J, Schaller C, et al. Digital photography and 3D MRI-based multimodal imaging for indivdualized planning of resective neocortical epilepsy surgery. Epilepsia, 2002, 43(12): 1543-1550.

DOI: 10.1046/j.1528-1157.2002.30002.x

Google Scholar

[2] Hill D, Edwards P, Hawkes D. Fusing medical images. Image Processing, 1994, 6(2): 109-113.

Google Scholar

[3] Pelizzari CA, Chen GT, Spelbring DR, et al. Accurate three-dimensional registration of CT, PET, and/or MR image os the brain. J Comput Assist Tomogr, 1989, 13(1): 20-26.

DOI: 10.1097/00004728-198901000-00004

Google Scholar

[4] Woods PP, Dapretto M, Sicotte NL, et al. Creation and use of a talairach compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data. Hum Brain Mapp, 1999, 8(2-3): 73-79.

DOI: 10.1002/(sici)1097-0193(1999)8:2/3<73::aid-hbm1>3.0.co;2-7

Google Scholar

[5] Santos M, pajares G, portela M, et al. A new wavelets image fusion strategy. 2003 Iberian Conference on Pattern Recongnition and Image Analysis, LNCS, Vol. 2652: 919-926.

DOI: 10.1007/978-3-540-44871-6_106

Google Scholar

[6] Zhao Haitao, Dong Jiechun. Image adaptive edge detection based on GLCM [J]. Microcomputer Information, 2006, 22（6）: 186-188.

Google Scholar

[7] Yang Zheny, Wang Yong, Wang Chengdao. LOG operator edge detection improvement program [J]. Computer Applications and Software, 2004, 21（9）: 88-89.

Google Scholar

[8] Burt P J, Kolczyski R J. Enhanced Image Capture Through Fusion. 1993 International Conferenceon ComPuter Vision, Vol. l: 173-18.

Google Scholar