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Segmentation of multiple sclerosis lesions in MRI by fuzzy neural networks: FLVQ and FOSART

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4 Author(s)

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-lesions 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 multi-spectral 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 networks' performance has been provided on the base of the labelled areas

Published in:

Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American

Date of Conference:

20-21 Aug 1998