Deformable Template Models and
Shape Feature Descriptors for
Brain Shape Morphometry


Brain shape morphometry has emerged as a preferred tool to investigate the morphological changes of them with respect to pathological processes (e.g. neurodegenerative diseases and aging). Various methods have been proposed in this field, but it is still challenging in estimating accurate and smooth surface boundaries against rough boundaries and in achieving good anatomical correspondence between the individual models. In addition, the sensitivity and comparability of the geometric measures, which quantify the shape differences between subjects, need to be validated and guaranteed for the comparison of the individuals'shapes with anatomical knowledge.

The objective of this research is to develop computational models to address these issues in the brain shape morphometry. Our approach can be defined as a template-based shape modeling and measurement. For this objective, we develop deformable surface models using a “progressive surface deformation” to reconstruct the target shapes robustly while preserving the geometric details of initial models (template models) against size and shape variations across subjects. In addition, we proposed structural feature descriptors and anatomical landmarks, anatomically and geometrically defined on the template models, to investigate the shape characteristics and changes of the brain ventricles with explicit measurement basis on anatomical knowledge. The proposed feature descriptors and landmarks guarantee the comparability and consistency across subjects. More specific information can be found in relevant papers.

Currently, we validated the accuracy, robustness and clinical consistency of the proposed methods on the human data of hippocampus and brain ventricles, collected from the Lothian Birth Cohort 1936 (LBC 1936) study by collaborating with the Centre for Clinical Brain Sciences (University of Edinburgh).


Hippocampal shape modeling and analysis

Lateral Ventricle

Shape morphometry using the structural feature descriptors and anatomical landmarks of brain ventricles


Group-wise Shape Analysis Tool


  • AverageImageConstruction

    takes binary images of target structure, and outputs group-wise average image.
  • IndividualSurfaceReconstruction

    takes template surface model & individual binary mask, and computes individualized surface model using progressive Laplacian surface deformation.
  • SurfaceAlignment

    takes a template surface model & individualized surface models, and outputs an average surface model.
  • LocalDeformity

    takes the average surface model & individualized surface models, and outputs a matrix (TXT format) containing the deformity values (the signed Euclidean norm of the displacement vectors) at each vertex.

Download available on NITRC



KAIST University of Edinburgh
Name Computer Graphics and Visualization Lab. Brain Research Imaging Centre
Address Department of Computer Science, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea NeuroImaging, Division of Clinical Neurosciences, Western General Hospital, Crewe Road South, EH4 2XU, Edinburgh, UK
Phone +82-(0)42-350-3555 +44-(0)131-537-2664

Contact Us

Prof. Jinah Park, Ph.D.

Jaeil Kim, Ph.D.

Hojin Ryoo

Maria del C. Valdes Hernandez, Ph.D.


This work was funded by the National Research Foundation of Korea (Grant no. 2012K2A1A2033133/no.2011-0009761), the Row Fogo Charitable Trust, the Scottish Imaging Network A Platform for Scientific Excellence and Age UK for the LBC1936 Study. We also thank the Lothian Birth Cohort 1936 Study Collaborative Group at The University of Edinburgh led by Profs. Ian J. Deary and Joanna M. Wardlaw, who provided the data used in this manuscript.