title: Robust nonparametric segmentation of infarct lesion from diffusion-weighted MR images.
abstract: Magnetic Resonance Imaging (MRI) is increasingly used for the diagnosis and monitoring of neurological disorders. In particular Diffusion-Weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke. Cerebral infarction lesion segmentation from DWI is accomplished in this work by applying nonparametric density estimation. The quality of the class boundaries is improved by including an edge confidence map, that is the confidence of truly being in the presence of a border between adjacent regions. The adjacency graph, that is constructed with the label regions, is analyzed and pruned to merge adjacent regions. The method was applied to real images, keeping all parameters constant throughout the process for each data set. The combination of region segmentation and edge detection proved to be a robust automatic technique of segmentation from DWI images of cerebral infarction regions in acute ischemic stroke. In a comparison with the reference infarct lesions segmentation, the automatic segmentation presented a significant correlation (r=0.935), and an average Tanimoto index of 0.538.
authors.affiliation: Neuroimaging Laboratory, Department of Electrical Engineering, Universidad Autónoma Metropolitana - Iztapalapa, Av San Rafael Atlixco 186, Col Vicentina, México, DF, 09340, Mexico. nidiyarehevia@hotmail.com
authors.forename: Nidiyare
authors.initials: N
authors.lastname: Hevia-Montiel
authors.forename: Juan Ramón
authors.initials: JR
authors.lastname: Jiménez-Alaniz
authors.forename: Verónica
authors.initials: V
authors.lastname: Medina-Bañuelos
authors.forename: Oscar
authors.initials: O
authors.lastname: Yáñez-Suárez
authors.forename: Charlotte
authors.initials: C
authors.lastname: Rosso
authors.forename: Yves
authors.initials: Y
authors.lastname: Samson
authors.forename: Sylvain
authors.initials: S
authors.lastname: Baillet
mesh_terms: D000465:Algorithms; D001331:Automation; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods*; D001330:Electronic Data Processing; D004867:Equipment Design; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012984:Software; D020521:Stroke / Q000175:diagnosis / Q000473:pathology*; D013382:Subtraction Technique