A priori fluorophore distribution estimation in fluorescence imaging through application of a segmentation process and a data fitting technique

Gorpas, D; Yova, D; Politopoulos, K

dc.contributor.author	Gorpas, D	en
dc.contributor.author	Yova, D	en
dc.contributor.author	Politopoulos, K	en
dc.date.accessioned	2014-03-01T01:32:31Z
dc.date.available	2014-03-01T01:32:31Z
dc.date.issued	2010	en
dc.identifier.issn	0895-6111	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20168
dc.subject	Data fitting	en
dc.subject	Diffusion approximation	en
dc.subject	Finite elements	en
dc.subject	Fluorescence molecular imaging	en
dc.subject	Image segmentation	en
dc.subject.classification	Radiology, Nuclear Medicine & Medical Imaging	en
dc.subject.other	Data fittings	en
dc.subject.other	Diffusion approximations	en
dc.subject.other	Finite Element	en
dc.subject.other	Fluorescence molecular	en
dc.subject.other	Fluorescence molecular imaging	en
dc.subject.other	Data handling	en
dc.subject.other	Digital image storage	en
dc.subject.other	Fluorophores	en
dc.subject.other	Inverse problems	en
dc.subject.other	Medical imaging	en
dc.subject.other	Medical problems	en
dc.subject.other	Pixels	en
dc.subject.other	Image segmentation	en
dc.subject.other	article	en
dc.subject.other	fluorescence imaging	en
dc.subject.other	image analysis	en
dc.subject.other	molecular imaging	en
dc.subject.other	priority journal	en
dc.subject.other	simulation	en
dc.subject.other	Algorithms	en
dc.subject.other	Diagnostic Imaging	en
dc.subject.other	Finite Element Analysis	en
dc.subject.other	Fluorescence	en
dc.subject.other	Image Processing, Computer-Assisted	en
dc.subject.other	Phantoms, Imaging	en
dc.title	A priori fluorophore distribution estimation in fluorescence imaging through application of a segmentation process and a data fitting technique	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.compmedimag.2009.12.010	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.compmedimag.2009.12.010	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	During the last few years a quite large number of fluorescence imaging applications have been reported in the literature, as one of the most challenging problems in medical imaging is to "see" a tumor embedded in tissue, which is a turbid medium. This problem has not been fully encountered yet, due to the non-linear nature of the inverse problem. In this paper, a novel method for processing the forward solver outcomes is presented. Through this technique the comparison between the simulated and the acquired data can be performed only at the region-of-interest, minimizing time-consuming pixel-to-pixel comparison. With this modus operandi a-priori information about the initial fluorophore distribution becomes available, leading to a more feasible inverse problem solution. (C) 2009 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Computerized Medical Imaging and Graphics	en
dc.identifier.doi	10.1016/j.compmedimag.2009.12.010	en
dc.identifier.isi	ISI:000280543300004	en
dc.identifier.volume	34	en
dc.identifier.issue	6	en
dc.identifier.spage	435	en
dc.identifier.epage	445	en