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.