access icon free Multilabel statistical shape prior for image segmentation

Statistical shape models have been widely used to guide the segmentation in an image, thus overcoming noise and occlusions. In this study, the authors present a graph cut-based segmentation framework, in which multiple objects can be segmented. They design a specific multilabel shape prior, which is integrated into the graph cost function. They also want to enforce spatial constraint between the objects. Towards this aim, they propose a local constraint to forbid the inclusion of an object into another, which is enforced in the regularisation term of the graph energy. They apply the authors’ method to cardiac magnetic resonance images, in which left and right ventricles, and the myocardium are segmented and for which encouraging results are obtained.

Inspec keywords: statistical analysis; medical image processing; graph theory; biomedical MRI; image segmentation

Other keywords: image segmentation; regularisation term; local constraint; right ventricle segmentation; multilabel statistical shape prior; graph cost function; cardiac magnetic resonance images; myocardium segmentation; left ventricle segmentation; graph energy; spatial constraint; graph cut-based segmentation framework

Subjects: Patient diagnostic methods and instrumentation; Probability theory, stochastic processes, and statistics; Other topics in statistics; Other topics in statistics; Combinatorial mathematics; Computer vision and image processing techniques; Biomedical magnetic resonance imaging and spectroscopy; Optical, image and video signal processing; Combinatorial mathematics; Medical magnetic resonance imaging and spectroscopy; Biology and medical computing; Algebra, set theory, and graph theory

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