Abstract
In this paper we propose an original method of segmentation of the portal network in the liver. For this, we combine two applications of the grey scale hit-or- miss transform. The automatic segmentation is performed in two steps. In the first step, we detect the shape of the entrance of the portal vein in the liver by application of a grey scale hit-or-miss transform. This gives the seed or starting point of the region-growing algorithm. In a second step, we apply a region- growing algorithm by using a criterion still based on a hit-or-miss. Our method performs better than a previous method based on region-growing algorithm with a single threshold criterion.
Keywords
- Vessel segmentation
- grey scale hit-or-miss transform
- shape detection
- CT-scan
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
D. Bloomberg. Multiresolution morphological approach to document image analysis. In International Conference on Document Analysis and Recognition, Saint-Malo, France, pages 963–971, 1991.
D. Bloomberg and P Maragos. Generalized hit-miss operations. In SPIE Conference Image analysis and morphological image processing., volume 1350, pages 116–128, July 1990.
D. Bloomberg and L. Vincent. Pattern matching using the blur hit-miss transform. Jourtnal of Electronic Imaging, 9(2):140–150, April 2000.
D. Casasent and E. Botha. Optical symbolic substitution for morphological transformations. Applied Optics, 27(18):3806–3810, 1988.
P. Dokládal, C. Lohou, Perroto L., and G. Bertrand. A new thinning algorithm and its application to extraction of blood vessels. In BioMedSim ‘99, pages 32–37, April 1999.
P. Dokládal, C. Lohou, L. Perroton, and G. Bertrand. Liver blood vessels extraction by a 3-D topological approach. In MICCAI’99, pages 98–105, September 1999.
J.B. Fasquel. Une méthode opto-informatique de détection et de reconnaissance d’objets d’intérêt: Application à la détection des lésions cancéreuses du foie et à la vérification en temps-réel de signatures manuscrites. Thèse de Doctorat, Université Louis Pasteur, Strasbourg I, 2002.
K. Krissian, G. Malandain, N. Ayache, R. Vaillant, and Y. Trousset. Model-based detection of tubular structures in 3D images. Computer Vision and Image Understanding, 80(2):130–171, 2000.
H. Liu, M. Wu, G. Jin, G. Cheng, and Q. He. An automatic human face recognition system. Optics and Lasers in Engineering, 30:305–314, 1998.
H. Liu, M. Wu, G. Jin, Y. Yan, and G. Cheng. Optical implementation of the morphological hit-miss transform using extensive complementary encoding. Optics Communications, 156:245–251, 1998.
C. Ronse. Extraction of narrow peaks and ridges in images by combination of local low rank and max filters: implementation and applications to clinical angiography. Working document, Philips Research Laboratory Brussels, October 1988.
C. Ronse. A lattice-theoretical morphological view on template extraction in images. Journal of Visual Communication and Image Representation, 7(3):273–295, 1996.
C. Ronse. Removing and extracting features in images using mathematical morphology. CWI Quarterly, 11(4):439–457, 1998.
R. Schaefer and D. Casasent. Nonlinear optical hit-miss transform for detection. Applied Optics, 34(20):3869–3882, 1995.
D. Selle, B. Preim, A. Schenk, and H.-O. Peitgen. Analysis of vasculature for liver surgery planning. IEEE Transactions on Medical Imaging, 21(11):1344–1357, 2002.
D. Selle, T. Schindewolf, C. Evertsz, and H. Peitgen. Quantitative analysis of CT liver images. In Proceedings of the First International Workshop on Computer-Aided Diagnosis, pages 435–444. Elsevier, 1998.
D. Selle, W. Spindler, B. Preim, and H.-O. Peitgen. Mathematical Methods in Medical Image Processing: Analysis of vascular structures for Preoperative Planning in Liver Surgery, pages 1039–1059. Springer, 2000.
D. Sinha and P. Laplante. A rough set-based approach to handling spatial uncertainty in binary images. Engineering Applications of Artifical Intelligence, 17:97–110, 2004.
P. Soille. Advances in the analysis of topographic features on discrete images. In DGCI: 10th International Conference, volume 2301 of Lecture Notes in Computer Science, pages 175–186. Springer, March 2002.
P. Soille. On morphological operators based on rank filters. Pattern Recognition, 35:527–535, 2002.
P. Soille. Morphological Image Analysis: Principles and Applications. Springer-Verlag, Berlin, Heidelberg, 2nd edition, 2003.
L. Soler. Une nouvelle méthode de segmentation des structures anatomiques et pathologiques: application aux angioscanners 3D du foie pour la planification chirurgicale. Thèse de Doctorat, Université d’Orsay, 1999.
M. Van Droogenbroeck and H.. Talbot. Fast computation of morphological operations with arbitrary structuring elements. Pattern Recognition Letters, 17(14):1451–1460, 1996.
FT.S Yu and S. Jutamulia. Optical Pattern Recognition. Cambridge University Press, first edition, 1998.
S. Yuan, M. Wu, G. Jin, X. Zhang, and L. Chen. Programmable optical hit-miss transformation using an incoherent optical correlator. Optics and Lasers in Engineering, 24:289–299, 1996.
C. Zahlten, H. Jitrgens, C. Evertsz, R. Leppek, H. Peitgen, and K. Klose. Portal vein reconstruction based on topology. European Journal of Radiology, 19(2):96–100, 1995.
C. Zahlten, H. Jürgens, and H. Peitgen. Reconstruction of branching blood vessels from CT-data. In R. Scateni, J. van Wijk, and P Zanarini, editors, Workshop of Visualization in Scientific Computing, pages 41–52. Springer-Verlag, 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer
About this paper
Cite this paper
Naegell, B., Ronse, C., Soler, L. (2005). Using Grey Scale Hit-Or-Miss Transform for Segmenting the Portal Network of the Liver. In: Ronse, C., Najman, L., Decencière, E. (eds) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3443-1_39
Download citation
DOI: https://doi.org/10.1007/1-4020-3443-1_39
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3442-8
Online ISBN: 978-1-4020-3443-5
eBook Packages: Computer ScienceComputer Science (R0)