Cybèle Ciofolo

Brain segmentation with competitive level sets and fuzzy control

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.

Combining fuzzy logic and level set methods for 3D MRI brain segmentation

We propose to segment volumetric brain structures with a level set method including a fuzzy decision in the design of the evolution force. The role of fuzzy logic is to fuse gradient-based and region-based information into a single force term, to take advantage of their properties. The gradient-based approach increases the level set speed in high-contrasted areas, whereas the region-based approach is useful to treat nonhomogeneous tissues and areas of complex shapes, on which borders do not appear clearly. This fusion does not require any manually-tuned parameter to balance the respective influence of the gradient and region terms. We integrated this segmentation algorithm in a fully automatic succession of operations involving a registration step from known data to decrease the computation time. Experimental results on the MNI Brainweb dataset and on a database of real MRI volumes are presented and discussed.

Atlas-Based Segmentation Using Level Sets and Fuzzy Labels

We propose to segment volumetric structures with an atlas-based level set method. The classical formulation of the level set evolution force presents a stopping criterion, a directional term and a regularization term. Fuzzy labels registered from an atlas provide useful information allowing to automatically tune the respective influence of the different terms according to the desired application. This is done with a fuzzy decision system based on simple rules corresponding to an expert knowledge. Two applications are presented in details in the context of 3D brain MRI: the segmentation of white matter with the tuning of the regularization term, and the segmentation of the right hemisphere. Experimental results on the MNI Brainweb dataset and on a database of real MRI volumes are presented and discussed.

Shape analysis and fuzzy control for 3d competitive segmentation of brain structures with level sets

We propose a new method to segment 3D structures with competitive level sets driven by a shape model and fuzzy control. To this end, several contours evolve simultaneously toward previously defined targets. The main contribution of this paper is the original introduction of prior information provided by a shape model, which is used as an anatomical atlas, into a fuzzy decision system. The shape information is combined 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. The shape model is produced with a principal component analysis, and the resulting mean shape and variations are used to estimate the target location and the fuzzy states corresponding to the distance between the current contour and the target. By combining shape analysis and fuzzy control, we take advantage of both approaches to improve the level set segmentation process with prior information. Experiments are shown for the 3D segmentation of deep brain structures from MRI and a quantitative evaluation is performed on a 18 volumes dataset.