an object oriented
implementation for the dual surface minimization (DSM) algorithm
Mikko Itäranta and Jouni Mykkänen
Department of Computer Sciences
University of Tampere
Institute of Signal Processing
Tampere University of Technology,
The dualsurfacemin is a C++ implementation of
the fully automatic dual surface minimization (DSM) algorithm
for the optimization of deformable surfaces .
The method is developed for automatic surface
extraction from noisy volumetric images.
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 .
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
We have tested the software in Linux and Solaris
operating systems (32-bit).
- An energy image is needed.
The dualsurfacemin can read raw image data and
format image files.
- An initial mesh is required. One example mesh is
provided in the package.
The program has a limited support for
mesh file formats.
- A software to view resulting meshes on image.
Dualsurfacemin is licensed under the GNU LGPL
See 'LICENSE' file in the software package.
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.
For visualization, we have developed a
Surface Mesh Visualizer (SMV)
Java package for
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
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.
J. Tohka, A. Kivimäki, A. Reilhac, J. Mykkänen, and U. Ruotsalainen.
Assessment of brain surface extraction from pet images using monte
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
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).