2011 | OriginalPaper | Buchkapitel
Graph Cuts with Invariant Object-Interaction Priors: Application to Intervertebral Disc Segmentation
verfasst von : Ismail Ben Ayed, Kumaradevan Punithakumar, Gregory Garvin, Walter Romano, Shuo Li
Erschienen in: Information Processing in Medical Imaging
Verlag: Springer Berlin Heidelberg
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This study investigates novel
object-interaction
priors for graph cut image segmentation with application to intervertebral disc delineation in magnetic resonance (MR) lumbar spine images. The algorithm optimizes an original cost function which constrains the solution with learned prior knowledge about the geometric interactions between different objects in the image. Based on a global measure of similarity between distributions, the proposed priors are intrinsically invariant with respect to translation and rotation. We further introduce a scale variable from which we derive an original
fixed-point equation (FPE)
, thereby achieving scale-invariance with only few fast computations. The proposed priors relax the need of costly pose estimation (or registration) procedures and large training sets (we used a single subject for training), and can tolerate shape deformations, unlike template-based priors. Our formulation leads to an
NP-hard
problem which does not afford a form directly amenable to graph cut optimization. We proceeded to a relaxation of the problem via an
auxiliary function
, thereby obtaining a nearly real-time solution with few graph cuts. Quantitative evaluations over 60 intervertebral discs acquired from 10 subjects demonstrated that the proposed algorithm yields a high correlation with independent manual segmentations by an expert. We further demonstrate experimentally the invariance of the proposed geometric attributes. This supports the fact that a single subject is sufficient for training our algorithm, and confirms the relevance of the proposed priors to disc segmentation.