Dualsurfacemin -
an object oriented implementation for the dual surface minimization (DSM) algorithm

Mikko Itäranta and Jouni Mykkänen
Department of Computer Sciences University of Tampere Finland
Jussi Tohka
Institute of Signal Processing Tampere University of Technology, Finland.


What is it?

The dualsurfacemin is a C++ implementation of the fully automatic dual surface minimization (DSM) algorithm for the optimization of deformable surfaces [1]. The method is developed for automatic surface extraction from noisy volumetric images.

Where can it be applied?

The dualsurfacemin software can be applied to surface extraction problems arising from volumetric imaging.

We have applied the DSM algorithm to brain surface extraction from the positron emission tomography (PET) brain images [2,3]. The method is quantitatively evaluated with simulated data [4]. Since then, The method has been applied to extract the striatum from Raclopride PET brain images and the heart volume from cardiac FDG-PET images [5,6].


for compiling from the source

We have tested the software in Linux and Solaris operating systems (32-bit).

for using


Dualsurfacemin is licensed under the GNU LGPL See 'LICENSE' file in the software package.


dualsurfacemin-1.1.-src.tar.gz (3.4Mb)

The Blitz++ library, an example image (artificial) and an initial mesh are included. Follow the instructions from README file in the package top directory.

The current implementation (V1.1.1) is still considered as beta stage software. If you discover a bug, please, inform us. We would also like to know, if you find the program useful.

Related software

For visualization, we have developed a Surface Mesh Visualizer (SMV) Java package for ImageJ. The SMV is also freely available. Note, this software is a beta release (V1.1).

The extracted brain surface mesh can be used to determine the mid-sagittal plane. We have developed the BrainSplitter software for extracting the mid-sagittal plane and dividing the brain surface mesh into hemispheres.


J. Tohka and J.M. Mykkänen.
Deformable mesh for automated surface extraction from noisy images.
International Journal of Image and Graphics, 4:405-432, 2004.

J. Mykkänen, J. Tohka, and U. Ruotsalainen.
Delineation of brain structures from positron emission tomography images using deformable models.
In R. Baud, M. Fieschi, P. Le Beaux, and P. Ruch, editors, The new navigators: from professionals to patients, volume 95, pages 33-38. IOS Press, 2003.

J. Mykkänen, J. Tohka, J. Luoma, and U. Ruotsalainen.
Automatic ectraction of brain surface and mid-sagittal plane from PET images applying deformable models.
Computer Methods and Programs in Biomedicine, 2005.
In press.

J. Tohka, A. Kivimäki, A. Reilhac, J. Mykkänen, and U. Ruotsalainen.
Assessment of brain surface extraction from pet images using monte carlo simulations.
IEEE Transactions in Nuclear Science, 51(5):2641-2648, 2004.

A. Kivimäki, J. Tohka, M. Anttila, and U. Ruotsalainen.
Automatic extraction of the heart volumes from dynamic FDG PET emission images for movement corrections.
European Journal of Nuclear Medicine and Molecular Imaging, pp. S406, Volume 31, Supplement 2 (Abstracts, Annual Congress of The EANM, Helsinki 2004).

J. Tohka, E. Wallius, J. Hirvonen, J. Hietala, and U. Ruotsalainen.
Improved reproducibility in dopamine D2-receptor studies with automatic segmentation of striatum from PET images.
In Proc. of IEEE Medical Imaging Conference (MIC2004), Rome, Italy, October 2004 (In press, 5 pages).


Jouni Mykk{nen 2005-05-31