Assessing the Variability of Internal Brain Structures Using Pca on Sampled Surface Points
Year: 2009
Type: conference-paper
Abstract: In this paper we propose to analyze the variability of brain structures using principal component analysis (PCA). We rely on a data base of registered and segmented 3D MRI images of normal subjects. We propose to use as input of PCA sampled points on the surface of the considered objects, selected using uniformity criteria or based on mean and Gaussian curvatures. Results are shown on the lateral ventricles. The main variation tendencies are observed in the orthogonal eigenvector space. Dimensionality reduction can be achieved and the variability of each landmark point is accurately described using the first three components.
Authors: Darwin Martínez, Isabelle Bloch, Tiberio Hernández
Institutions: Télécom Paris, Universidad de Los Andes
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W43545940