title: Self-Trained Supervised Segmentation of Subcortical Brain Structures Using Multispectral Magnetic Resonance Images.
abstract: The aim of this paper is investigate the feasibility of automatically training supervised methods, such as k-nearest neighbor (kNN) and principal component discriminant analysis (PCDA), and to segment the four subcortical brain structures: caudate, thalamus, pallidum, and putamen. The adoption of supervised classification methods so far has been limited by the need to define a representative training dataset, operation that usually requires the intervention of an operator. In this work the selection of the training data was performed on the subject to be segmented in a fully automated manner by registering probabilistic atlases. Evaluation of automatically trained kNN and PCDA classifiers that combine voxel intensities and spatial coordinates was performed on 20 real datasets selected from two publicly available sources of multispectral magnetic resonance studies. The results demonstrate that atlas-guided training is an effective way to automatically define a representative and reliable training dataset, thus giving supervised methods the chance to successfully segment magnetic resonance brain images without the need for user interaction.
authors.affiliation: Istituto di Biostrutture e Bioimmagini, CNR, Via Tommaso De Amicis 95, 80145 Napoli, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Larobina
authors.affiliation: Istituto per le Applicazioni del Calcolo "Mauro Picone", CNR, Via Pietro Castellino 111, 80131 Napoli, Italy.
authors.forename: Loredana
authors.initials: L
authors.lastname: Murino
authors.affiliation: Dipartimento di Scienze Biomediche Avanzate, Università "Federico II", Via Sergio Pansini 5, 80131 Napoli, Italy.
authors.forename: Amedeo
authors.initials: A
authors.lastname: Cervo
authors.affiliation: Istituto di Biostrutture e Bioimmagini, CNR, Via Tommaso De Amicis 95, 80145 Napoli, Italy.
authors.forename: Bruno
authors.initials: B
authors.lastname: Alfano
mesh_terms: D000818:Animals; D016002:Discriminant Analysis; D005917:Globus Pallidus / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis; D011699:Putamen / Q000000981:diagnostic imaging*; D011859:Radiography; D013788:Thalamus / Q000000981:diagnostic imaging*