Abstract—Purpose of this work is the design and
implementation of an automated method for digital volume
segmentation, based on multi-parametric densities, fuzzy
topology, and adaptive growth mechanism. The processing
objective is the global segmentation of the digital volume, that is
its partitioning into significant connected subsets, in a fully
automatic way. The main advantage consists in the very nature
of the algorithm that enables the automatic segmentation by
running an iterative process that adapts to the volume at hand
and does not require any user intervention. The designed
method can be applied to multi-parametric volumes where
different characteristics are available to analyze the same
target. The robustness of the method has been evaluated and
verified through statistical parameters, that will be discussed
below, after application on volumes of biomedical images
obtained through Magnetic Resonance Imaging.
Index Terms—Segmentation, fuzzy processing,
connectedness, multi-parametric data fusion.
S. Nardotto is with Università degli Studi di Genova, DITEN, via Opera
Pia 11a – I16145 Genova, Italy (e-mail: sonia.nardotto@edu.unige.it).
S. G. Dellepiane is with Università degli Studi di Genova, via Opera Pia
11a – I16145 Genova, Italy (e-mail: silvana.dellepiane@unige.it).
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Cite:Sonia Nardotto and Silvana G. Dellepiane, "An Automatic Segmentation Method for MRI Multiparametric Volumes," International Journal of Computer Theory and Engineering vol. 6, no. 2, pp. 75-80, 2014.
