Authors:
Darwin Martínez
1
;
Isabelle Bloch
1
and
Tiberio Hernández
2
Affiliations:
1
TELECOM ParisTech (ENST), France
;
2
Universidad Los Andes, Colombia
Keyword(s):
Anatomical variability, MRI images, Brain structures, Principal component analysis.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Statistical Approach
;
Surface Geometry and Shape
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.