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Algorithms 2012, 5(4), 636-653; doi:10.3390/a5040636
Article

Edge Detection from MRI and DTI Images with an Anisotropic Vector Field Flow Using a Divergence Map

Scientific Didactic Polo of Rimini, University of Bologna, Via Anghera 22 Rimini, Italy
Received: 31 July 2012 / Revised: 5 November 2012 / Accepted: 3 December 2012 / Published: 13 December 2012
(This article belongs to the Special Issue Machine Learning for Medical Imaging)
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Abstract

The aim of this work is the extraction of edges from Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) images by a deformable contour procedure, using an external force field derived from an anisotropic flow. Moreover, we introduce a divergence map in order to check the convergence of the process. As we know from vector calculus, divergence is a measure of the magnitude of a vector field convergence at a given point. Thus by means level curves of the divergence map, we have automatically selected an initial contour for the deformation process. If the initial curve includes the areas from which the vector field diverges, it will be able to push the curve towards the edges. Furthermore the divergence map highlights the presence of curves pointing to the most significant geometric parts of boundaries corresponding to high curvature values. In this way, the skeleton of the extracted object will be rather well defined and may subsequently be employed in shape analysis and morphological studies.
Keywords: DTI; MRI; edge extraction; active contour; GGVF; anisotropic diffusion; divergence map DTI; MRI; edge extraction; active contour; GGVF; anisotropic diffusion; divergence map
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Giuliani, D. Edge Detection from MRI and DTI Images with an Anisotropic Vector Field Flow Using a Divergence Map. Algorithms 2012, 5, 636-653.

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