title: Brain segmentation with competitive level sets and fuzzy control.
abstract: We propose to segment 3D structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the borders of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real MR images are presented, quantitatively assessed and discussed.
authors.affiliation: IRISA / CNRS - Team Visages, 35042 Rennes, France. Cybele.Ciofolo@irisa.fr
authors.forename: Cybèle
authors.initials: C
authors.lastname: Ciofolo
authors.forename: Christian
authors.initials: C
authors.lastname: Barillot
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity