title: Segmenting brain MRI using adaptive mean shift.
abstract: To delineate arbitrarily shaped clusters in a complex multimodal feature space, such as the brain MRI intensity space, often requires kernel estimation techniques with locally adaptive bandwidths, such as the adaptive mean shift procedure. Proper selection of the kernel bandwidth is a critical step for a better quality in the clustering. This paper presents a solution for the bandwidth selection, which is completely nonparametric and is based on the sample point estimator to yield a spatial pattern of local bandwidths. The method was applied to synthetic brain images, showing a high performance even in the presence of varying noise level and bias.
authors.affiliation: Dept. of Electr. Eng., Univ. Autonoma Metropolitana-Iztapalapa, Mexico City, Mexico. jajr@xanum.uam.mx
authors.forename: Juan Ramón
authors.initials: JR
authors.lastname: Jiménez-Alaniz
authors.forename: Mauricio
authors.initials: M
authors.lastname: Pohl-Alfaro
authors.forename: Verónica
authors.initials: V
authors.lastname: Medina-Bañuelos
authors.forename: Oscar
authors.initials: O
authors.lastname: Yáñez-Suárez
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering; D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical; D018709:Statistics, Nonparametric