title: Liver segmentation in MRI: A fully automatic method based on stochastic partitions.
abstract: There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 ± 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.
authors: Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain. Electronic address: ferlomir@labhuman.i3bh.es.
authors: F
authors: F
authors: López-Mir
authors: Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
authors: V
authors: V
authors: Naranjo
authors: CMM-Centre de Morphologie Mathématique, Mathématiques et Systèmes, MINES Paristech, France.
authors: J
authors: J
authors: Angulo
authors: Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain; Ciber, Fisiopatología de Obesidad y Nutrición, CB06/03 Instituto de Salud Carlos III, Spain.
authors: M
authors: M
authors: Alcañiz
authors: Hospital Clínica Benidorm (Unidad de Resonancia Magnética INSCANNER), Spain.
authors: L
authors: L
authors: Luna
mesh_terms: D000465:Algorithms; D006801:Humans; D008099:Liver / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D013269:Stochastic Processes
keywords: Liver segmentation; Magnetic resonance imaging; Mathematical morphology; Stochastic partitions; Watershed transform