Combining radiometric and spatial structural information in a new metric for minimal surface segmentation

Authors:
Olivier Nempont;Jamal Atif;Elsa Angelini;Isabelle Bloch
Affiliations:
Ecole Nationale Supérieure des Télécommunications, GET, Télécom Paris, CNRS UMR, Paris, France;Ecole Nationale Supérieure des Télécommunications, GET, Télécom Paris, CNRS UMR, Paris, France;Ecole Nationale Supérieure des Télécommunications, GET, Télécom Paris, CNRS UMR, Paris, France;Ecole Nationale Supérieure des Télécommunications, GET, Télécom Paris, CNRS UMR, Paris, France
Venue:
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Year:
2007

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Abstract

Segmentation of anatomical structures via minimal surface extraction using gradient-based metrics is a popular approach, but exhibits some limits in the case of weak or missing contour information. We propose a new framework to define metrics, robust to missing image information. Given an object of interest we combine gray-level information and knowledge about the spatial organization of cerebral structures, into a fuzzy set which is guaranteed to include the object's boundaries. From this set we derive a metric which is used in a minimal surface segmentation framework. We show how this metric leads to improved segmentation of subcortical gray matter structures. Quantitative results on the segmentation of the caudate nucleus in T1 MRI are reported on 18 normal subjects and 6 pathological cases.