Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2007, Article ID 10526, 15 pages
Research Article

Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study

1Ecole Nationale Supérieure des Télécommunications, Groupe des Ecoles des Télécommunications, CNRS UMR 5141, Paris 75013, France
2Department of Biomedical Engineering, School of Engineering and Applied Science, Columbia University, New York, NY 10027, USA
3Department of Biological Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA

Received 3 August 2006; Revised 10 November 2006; Accepted 19 December 2006

Academic Editor: Yue Wang

Copyright © 2007 Elsa D. Angelini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.