title: ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm.
abstract: In this paper, a semi-automatic segmentation method for volume assessment of Intestinal-type adenocarcinoma (ITAC) is presented and validated. The method is based on a Gaussian hidden Markov random field (GHMRF) model that represents an advanced version of a finite Gaussian mixture (FGM) model as it encodes spatial information through the mutual influences of neighboring sites. To fit the GHMRF model an expectation maximization (EM) algorithm is used. We applied the method to a magnetic resonance data sets (each of them composed by T1-weighted, Contrast Enhanced T1-weighted and T2-weighted images) for a total of 49 tumor-contained slices. We tested GHMRF performances with respect to FGM by both a numerical and a clinical evaluation. Results show that the proposed method has a higher accuracy in quantifying lesion area than FGM and it can be applied in the evaluation of tumor response to therapy.
authors.affiliation: Dipartimento di Ingegneria Biomedica, Politecnico di Milano, Italy. katia.passera@polimi.it
authors.forename: Katia M
authors.initials: KM
authors.lastname: Passera
authors.forename: Paolo
authors.initials: P
authors.lastname: Potepan
authors.forename: Luca
authors.initials: L
authors.lastname: Brambilla
authors.forename: Luca T
authors.initials: LT
authors.lastname: Mainardi
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology*; D000465:Algorithms; D006801:Humans; D008279:Magnetic Resonance Imaging; D008390:Markov Chains; D015233:Models, Statistical; D010255:Paranasal Sinus Neoplasms / Q000473:pathology*