Title :
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
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;
Conference_Titel :
Computer Network and Multimedia Technology, 2009. CNMT 2009. International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5272-9
DOI :
10.1109/CNMT.2009.5374652
