Review of the Software Used for 3D Volumetric Reconstruction of the Liver
In medical imaging, segmentation of different areas of
human body like bones, organs, tissues, etc. is an important issue.
Image segmentation allows isolating the object of interest for further
processing that can lead for example to 3D model reconstruction of
whole organs. Difficulty of this procedure varies from trivial for
bones to quite difficult for organs like liver. The liver is being
considered as one of the most difficult human body organ to segment.
It is mainly for its complexity, shape versatility and proximity of
other organs and tissues. Due to this facts usually substantial user
effort has to be applied to obtain satisfactory results of the image
segmentation. Process of image segmentation then deteriorates from
automatic or semi-automatic to fairly manual one. In this paper,
overview of selected available software applications that can handle
semi-automatic image segmentation with further 3D volume
reconstruction of human liver is presented. The applications are being
evaluated based on the segmentation results of several consecutive
DICOM images covering the abdominal area of the human body.
[1] Suhuai, L., Xuechen, L., Jiaming, L., 2014. Review on the methods of
automatic liver segmentation from abdominal images. In Journal of
Computer and Communications, Vol. 2, No. 2, pp 1-7. Scientific
Research. DOI: 10.4236/jcc.2014.22001.
[2] Mharib, A., Ramli, A., Mashohor, S., Mahmood, R., 2012. Survey on
liver CT image segmentation methods. In Artificial Intelligence Review,
Vol. 37, No. 2 , pp 83-95. Springer Netherlands. DOI: 10.1007/s10462-
011-9220-3.
[3] ITK - Segmentation & Registration Toolkit, 2014. Available from:
<http://www.itk.org/>. (9 November 2014).
[4] VTK - The Visualization Toolkit, 2014. Available from:
<http://www.vtk.org/>. (9 November 2014).
[5] ITK-SNAP Home Page, 2014. Available from:
<http://www.itksnap.org/pmwiki/pmwiki.php>. (9 November 2014).
[6] General Public License, 2007. Available from:
<http://www.gnu.org/licenses/gpl-3.0.txt>. (9 November 2014).
[7] Yushkevich, P., Piven, J., Cody, H., Smith, R., Ho, S., Gee, J., Gerig, G.,
2006. User-guided 3D active contour segmentation of anatomical
structures: Significantly improved efficiency and reliability. In
Neuroimage,Vol. 31, No. 3, pp. 1116-28. Elsevier. DOI:
10.1016/j.neuroimage.2006.01.015.
[8] DICOM Homepage, 2014. Available from: <http://dicom.nema.org/>. (9
November 2014).
[9] GeoS - Microsoft Research, 2014. Available from:
<http://research.microsoft.com/en-us/downloads/10698b08-42fe-46cbb6fb-
d4ef261906b1/>. (9 November 2014).
[10] Microsoft Research License Agreement, 2013. Available from:
<http://research.microsoft.com/en-us/downloads/10698b08-42fe-46cbb6fb-
d4ef261906b1/MSR-LA%20-%20GeoS.rtf>. (9 November 2014).
[11] Criminisi, A., Sharp, T., Blake, A., 2008. GeoS: Geodesic Image
Segmentation. In ECCV 2008, eds D. Forsyth, P. Torr, and A.
Zisserman, Part I, LNCS 5302, pp. 99–112, Springer-Verlag Berlin
Heidelberg.
[12] Jirik, M., Ryba, T., Svobodova, M., Mira, H., Liska, V., 2014. Lisa –
Liver surgery analyser software development. In Proceedings of WCCM
XI-ECCM V-ECFD VI. Barcelona.
[13] Lisa GitHub, 2012. Available from: <https://github.com/mjirik/
lisa/blob/master/license.txt>. (9 November 2014).
[14] Boykov, Y., Funka-Lea, G., 2006. Graph cuts and efficient N-D image
segmentation. In International Journal of Computer Vision, Vol. 70, No.
2, pp 109–131. Kluwer Academic Publishers. DOI: 10.1007/s11263-
006-7934-5.
[15] Introduction – SlicerWeb, 2014. Available from:
<http://www.slicer.org/pages/Introduction>. (9 November 2014).
[16] LicenseText – SlicerWeb, 2005. Available from:
<http://www.slicer.org/pages/LicenseText>. (9 November 2014).
[17] Ghosh, P., Antani, S. K., Long, L. R., Thoma, G. R., 2011.
Unsupervised grow-cut: Cellular automata-based medical image
segmentation. In Healthcare Informatics, Imaging and Systems Biology,
pp 40-47. IEEE. DOI: 10.1109/HISB.2011.44.
[18] About OsiriX, 2014. Available from: <http://www.osirixviewer.
com/AboutOsiriX.html>. (9 November 2014).
[19] License - license.pdf, 2007. Available from: <http://www.osirixviewer.
com/license.pdf>. (9 November 2014).
[20] DICOM files, 2014. Available from: <http://www.osirixviewer.
com/datasets/>. (9 November 2014).
[1] Suhuai, L., Xuechen, L., Jiaming, L., 2014. Review on the methods of
automatic liver segmentation from abdominal images. In Journal of
Computer and Communications, Vol. 2, No. 2, pp 1-7. Scientific
Research. DOI: 10.4236/jcc.2014.22001.
[2] Mharib, A., Ramli, A., Mashohor, S., Mahmood, R., 2012. Survey on
liver CT image segmentation methods. In Artificial Intelligence Review,
Vol. 37, No. 2 , pp 83-95. Springer Netherlands. DOI: 10.1007/s10462-
011-9220-3.
[3] ITK - Segmentation & Registration Toolkit, 2014. Available from:
<http://www.itk.org/>. (9 November 2014).
[4] VTK - The Visualization Toolkit, 2014. Available from:
<http://www.vtk.org/>. (9 November 2014).
[5] ITK-SNAP Home Page, 2014. Available from:
<http://www.itksnap.org/pmwiki/pmwiki.php>. (9 November 2014).
[6] General Public License, 2007. Available from:
<http://www.gnu.org/licenses/gpl-3.0.txt>. (9 November 2014).
[7] Yushkevich, P., Piven, J., Cody, H., Smith, R., Ho, S., Gee, J., Gerig, G.,
2006. User-guided 3D active contour segmentation of anatomical
structures: Significantly improved efficiency and reliability. In
Neuroimage,Vol. 31, No. 3, pp. 1116-28. Elsevier. DOI:
10.1016/j.neuroimage.2006.01.015.
[8] DICOM Homepage, 2014. Available from: <http://dicom.nema.org/>. (9
November 2014).
[9] GeoS - Microsoft Research, 2014. Available from:
<http://research.microsoft.com/en-us/downloads/10698b08-42fe-46cbb6fb-
d4ef261906b1/>. (9 November 2014).
[10] Microsoft Research License Agreement, 2013. Available from:
<http://research.microsoft.com/en-us/downloads/10698b08-42fe-46cbb6fb-
d4ef261906b1/MSR-LA%20-%20GeoS.rtf>. (9 November 2014).
[11] Criminisi, A., Sharp, T., Blake, A., 2008. GeoS: Geodesic Image
Segmentation. In ECCV 2008, eds D. Forsyth, P. Torr, and A.
Zisserman, Part I, LNCS 5302, pp. 99–112, Springer-Verlag Berlin
Heidelberg.
[12] Jirik, M., Ryba, T., Svobodova, M., Mira, H., Liska, V., 2014. Lisa –
Liver surgery analyser software development. In Proceedings of WCCM
XI-ECCM V-ECFD VI. Barcelona.
[13] Lisa GitHub, 2012. Available from: <https://github.com/mjirik/
lisa/blob/master/license.txt>. (9 November 2014).
[14] Boykov, Y., Funka-Lea, G., 2006. Graph cuts and efficient N-D image
segmentation. In International Journal of Computer Vision, Vol. 70, No.
2, pp 109–131. Kluwer Academic Publishers. DOI: 10.1007/s11263-
006-7934-5.
[15] Introduction – SlicerWeb, 2014. Available from:
<http://www.slicer.org/pages/Introduction>. (9 November 2014).
[16] LicenseText – SlicerWeb, 2005. Available from:
<http://www.slicer.org/pages/LicenseText>. (9 November 2014).
[17] Ghosh, P., Antani, S. K., Long, L. R., Thoma, G. R., 2011.
Unsupervised grow-cut: Cellular automata-based medical image
segmentation. In Healthcare Informatics, Imaging and Systems Biology,
pp 40-47. IEEE. DOI: 10.1109/HISB.2011.44.
[18] About OsiriX, 2014. Available from: <http://www.osirixviewer.
com/AboutOsiriX.html>. (9 November 2014).
[19] License - license.pdf, 2007. Available from: <http://www.osirixviewer.
com/license.pdf>. (9 November 2014).
[20] DICOM files, 2014. Available from: <http://www.osirixviewer.
com/datasets/>. (9 November 2014).
@article{"International Journal of Information, Control and Computer Sciences:69012", author = "P. Strakos and M. Jaros and T. Karasek and T. Kozubek and P. Vavra and T. Jonszta", title = "Review of the Software Used for 3D Volumetric Reconstruction of the Liver", abstract = "In medical imaging, segmentation of different areas of
human body like bones, organs, tissues, etc. is an important issue.
Image segmentation allows isolating the object of interest for further
processing that can lead for example to 3D model reconstruction of
whole organs. Difficulty of this procedure varies from trivial for
bones to quite difficult for organs like liver. The liver is being
considered as one of the most difficult human body organ to segment.
It is mainly for its complexity, shape versatility and proximity of
other organs and tissues. Due to this facts usually substantial user
effort has to be applied to obtain satisfactory results of the image
segmentation. Process of image segmentation then deteriorates from
automatic or semi-automatic to fairly manual one. In this paper,
overview of selected available software applications that can handle
semi-automatic image segmentation with further 3D volume
reconstruction of human liver is presented. The applications are being
evaluated based on the segmentation results of several consecutive
DICOM images covering the abdominal area of the human body.
", keywords = "Image segmentation, semi-automatic, software, 3D
volumetric reconstruction.", volume = "9", number = "2", pages = "422-5", }
