Multi-Mode Medical Image Fusion Algorithm Based on Principal Component Analysis

Author

Wang Hao-quan ; Xing Hao

Author_Institution

Key Lab. on Instrum. Sci. & Dynamic Meas. of the Minist. Educ., North Univ. of China, Taiyuan, China

fYear

2009

fDate

18-20 Jan. 2009

Firstpage

1

Lastpage

4

Abstract

Based on research of principal component analysis, the principal component analysis is introduced to medical image fusion. The K-L transform is used to multi-mode images. Then a new matrix is composed. A eigenvector which accounts for above 90 percent in contribution of variance about the new matrix is adopted to obtain principal components. Using principal components can carry on image fusion. The result indicates that the method has many characters, such as fast execution, great information entropy and broad dynamic range.

Keywords

eigenvalues and eigenfunctions; image fusion; matrix algebra; medical image processing; principal component analysis; transforms; K-L transform; eigenvector; information entropy; multimode medical image fusion algorithm; principal component analysis; Biomedical imaging; Computed tomography; Covariance matrix; Dynamic range; Image analysis; Image fusion; Instruments; Medical diagnostic imaging; Principal component analysis; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Network and Multimedia Technology, 2009. CNMT 2009. International Symposium on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5272-9

Type

conf

DOI

10.1109/CNMT.2009.5374652

Filename

5374652