Coupled nonparametric shape and moment-based intershape pose priors for multiple basal ganglia structure segmentation

Mustafa Gökhan Uzunbaş, Octavian Soldea*, Devrim Ünay, Mjdat Çetin, Gözde Ünal, Ayütl Erçil, Ahmet Ekin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.

Original languageEnglish
Article number5492224
Pages (from-to)1959-1978
Number of pages20
JournalIEEE Transactions on Medical Imaging
Volume29
Issue number12
DOIs
Publication statusPublished - Dec 2010
Externally publishedYes

Funding

Manuscript received April 05, 2010; accepted June 09, 2010. Date of publication June 28, 2010; date of current version November 30, 2010. This work was supported in part by the European Commission under Grant MTKI-CT-2006-042717, Grant FP6-2004-ACC-SSA-2 (SPICE), and Grant MIRG-CT-2006-041919, and in part by a graduate fellowship from The Scientific and Technological Research Council of Turkey (TUBITAK). Asterisk indicates corresponding author M. G. Uzunbas¸ was with the Faculty of Engineering and Natural Sciences, Sa-banci University, 34956 Istanbul, Turkey. He is now with the Computer Science Department, Rutgers University, Piscataway, 08854 USA (e-mail: uzunbas@cs. rutgers.edu). *O. Soldea is with the Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey (e-mail: octavian@sabanciuniv.edu).

FundersFunder number
TUBITAK
European CommissionFP6-2004-ACC-SSA-2, MIRG-CT-2006-041919
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

    Keywords

    • Active contours
    • basal ganglia
    • kernel density estimation
    • magnetic resonance (MR) imagery
    • moments
    • shape prior
    • volumetric segmentation

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