Combining radiometric and spatial structural information in a new metric for minimal surface segmentation.
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.
Nempont
Olivier
O
Ecole Nationale Supérieure des Télécommunications (GET - Télécom Paris) CNRS UMR 5141 LTCI, Paris, France. name@enst.fr
Atif
Jamal
J
Angelini
Elsa
E
Bloch
Isabelle
I
D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000175:diagnosis*; D002421:Caudate Nucleus / Q000473:pathology*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011874:Radiometry / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity