Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use

Article type: Research Article

Authors: Apostolou, N.^{a; *} | Papazoglou, Th.^{b; c} | Koutsouris, D.^a

Affiliations: [a] Biomedical Engineering Laboratory, National Technical University of Athens, Greece | [b] Hygeia Hospital, Athens, Greece | [c] Harvard Medical International

Correspondence: [*] Address for correspondence: Nikos Apostolou, 9 Iroon Polytechneiou Str., Zografou Campus, H/Y building, 15773 Athens, Greece. Tel.: +30210 7722430; Fax: +30210 7722431; E-mail: [email protected]; [email protected].

Abstract: 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 Matlab® 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 Matlab® 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.

DOI: 10.3233/THC-2006-14302

Journal: Technology and Health Care, vol. 14, no. 3, pp. 143-156, 2006