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

Apostolou, N; Papazoglou, Th; Koutsouris, D

dc.contributor.author	Apostolou, N	en
dc.contributor.author	Papazoglou, Th	en
dc.contributor.author	Koutsouris, D	en
dc.date.accessioned	2014-03-01T01:55:20Z
dc.date.available	2014-03-01T01:55:20Z
dc.date.issued	2006	en
dc.identifier.issn	09287329	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27693
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-33748514831&partnerID=40&md5=5c605ae85ab90b7c82973044b557ce0e	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	computer assisted tomography	en
dc.subject.other	computer program	en
dc.subject.other	controlled study	en
dc.subject.other	entropy	en
dc.subject.other	evaluation research	en
dc.subject.other	gamma knife radiosurgery	en
dc.subject.other	Gray scale echography	en
dc.subject.other	human	en
dc.subject.other	image analysis	en
dc.subject.other	imaging system	en
dc.subject.other	mathematical computing	en
dc.subject.other	morphology	en
dc.subject.other	nuclear magnetic resonance imaging	en
dc.subject.other	principal component analysis	en
dc.subject.other	priority journal	en
dc.subject.other	quality control	en
dc.subject.other	quantitative analysis	en
dc.subject.other	Algorithms	en
dc.subject.other	Entropy	en
dc.subject.other	Humans	en
dc.subject.other	Image Processing, Computer-Assisted	en
dc.subject.other	Magnetic Resonance Imaging	en
dc.subject.other	Radiosurgery	en
dc.subject.other	Surgery, Computer-Assisted	en
dc.subject.other	Tomography, X-Ray Computed	en
dc.title	Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use	en
heal.type	journalArticle	en
heal.publicationDate	2006	en
heal.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. © 2006 - IOS Press and the authors. All rights reserved.	en
heal.journalName	Technology and Health Care	en
dc.identifier.volume	14	en
dc.identifier.issue	3	en
dc.identifier.spage	143	en
dc.identifier.epage	156	en