ERUDIT-Workshop
Fuzzy Diagnostic and Therapeutic Decision Support
11–12 May 2000, Vienna, Austria
Istituto Elaborazione Segnali ed Immagini
Consiglio Nazionale Delle Ricerche (I.E.S.I.- C.N.R.)
Via Amendola 166/5 - 70126 Bari, Italy
e-mail: blonda@iesi.ba.cnr.it
Istituto Scienze dell'Atmosfera e dell'Oceano (I.S.A.O.- C.N.R.)
Via Gobetti 101 - 43100 Bologna, Italy
e-mail: baraldi@imga.bo.cnr.it
Cattedra e Servizio di Neuroradiologia, University of Bari
P.za G. Cesare, 11 - 70126 Bari, Italy
Abstract. In this paper two fuzzy neural networks have been applied for the segmentation of Magnetic Resonance (MR) Images. The objective of the work is to state the effectiveness of a fuzzy-neuro approach for the detection of the small lesions present in thick MR slices of multiple sclerosis patients. The data set included the Proton Density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1-weighted three dimensional sequence, i.e., the magnetization-prepared rapid gradient echo (MP-RAGE) of a volunteer. The Fuzzy Learning Vector Quantization (FLVQ) and the Fully Self Organizing Map (FOSART) models have been used for the semi-automatic tissue segmentation of the multispectral data set. Both models were trained with the pixels extracted from some labelled areas, interactively selected by a neuro-radiologist on the input raw images. A quantitative comparison between the two neural network model performance has been provided on the base of the labelled areas.
(In: Adlassnig, K.-P. (ed.) Fuzzy Diagnostic and Therapeutic Decision Support. Proc. of the ERUDIT Workshop, Österreichische Computer Gesellschaft, Vienna, p.119.)