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  Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use
 
 
Title: Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use
Author: Apostolou, N.
Papazoglou, Th.
Koutsouris, D.
Appeared in: Technology & health care
Paging: Volume 14 (2006) nr. 3 pages 143-156
Year: 2006-09-07
Contents: Image fusion is a process of combining information from multiple sensors. It is a useful tool implemented in the treatment planning programme of Gamma Knife Radiosurgery. In this paper we evaluate advanced image fusion algorithms for MatlabĀ® platform and head images. We develop nine level grayscale image fusion methods: average, principal component analysis (PCA), discrete wavelet transform (DWT) and Laplacian, filter - subtract - decimate (FSD), contrast, gradient, morphological pyramid and a shift invariant discrete wavelet transform (SIDWT) method in MatlabĀ® platform. We test these methods qualitatively and quantitatively. The quantitative criteria we use are the Root Mean Square Error (RMSE), the Mutual Information (MI), the Standard Deviation (STD), the Entropy (H), the Difference Entropy (DH) and the Cross Entropy (CEN). The qualitative are: natural appearance, brilliance contrast, presence of complementary features and enhancement of common features. Finally we make clinically useful suggestions.
Publisher: IOS Press
Source file: Elektronische Wetenschappelijke Tijdschriften
 
 

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