Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods

Frollo, I., Andris, P., Přibil, J., Vojtíšek, L., Dermek, T., Valkovič, L., (2010). Measurement and imaging of planar electromagnetic phantoms based on NMR imaging methods. Measurement Science Review, 10 (3), 97-101.10.2478/v10048-010-0013-7Search in Google Scholar

Jan, J. (2006). Medical Image Processing, Reconstruction and Restoration : Concepts and Methods. CRC Press.Search in Google Scholar

Hlavac, V., Sonka, M. (1992). Image Vision. Praha: Grada.Search in Google Scholar

Valkovič, L., Windiwschberger, C. (2010). Method for geometric distortion correction in fMRI based on three echoplanar phase images. Measurement Science Review, 10 (4), 116-119.Search in Google Scholar

Marcoň, P., Bartušek, K., Kořínek, R., Dokoupil, Z. (2011). Correction of artifacts in diffusion-weighted MR images. In 34th International Conference on Telecommunications and Signal Processing, 391-397.10.1109/TSP.2011.6043703Search in Google Scholar

Deng, X., Du, G. (2008). Editorial: 3D segmentation in the clinic: A grand challenge II - liver tumor segmentation.http://grand-challenge2008.bigr.nl/proceedings/liver/articles.htmlSearch in Google Scholar

Seo, K. S., Chung, T. W. (2005). Automatic boundary tumor segmentation of a liver. In Computational Science and Its Applicationas - ICCSA 2005, LNCS 3483. Springer, 836-842.10.1007/11424925_87Search in Google Scholar

LI, B. N., Chui, Ch.K., Ong, S. H., Chang, S. (2009). In IFMBE Proceedings (Integrating FCM and Level Sets for Liver Tumor Segmentation - ICBME 2008), Vol. 23, 202-205.Search in Google Scholar

Smeets, D., Loeckx, D., Stijnen, B., Dobbelaer, B., Vandeurmeulen, D., Suetens, P. (2010). Semiautomatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification. Medical Image Analysis, 14, 13-20.10.1016/j.media.2009.09.00219828356Search in Google Scholar

Stawiaski, J., Decenciere, E., Bidault, F. (2008). Interactive liver tumor segmentation using graph-cuts and watershed. MiDAS Journal (MICCAI 2008 Workshop : Grand Challenge Liver Tumor Segmentation). http://www.insight-journal.org/browse/journal/45 http://www.insight-journal.org/browse/journal/4510.54294/5clvrbSearch in Google Scholar

Abdel-Massieh, N. H., Hadhoud, M. M., Amin, K. M. (2010). Automatic liver tumor segmentation from CT scans with knowledge-based constraints. In 5^th Cairo International Biomedical Engineering Conference.10.1109/CIBEC.2010.5716054Search in Google Scholar

Qi, Y. et al. (2008). Semi-automatic segmentation of liver tumors from CT scans using Bayesian rule-based 3D region growing. MiDAS Journal (MICCAI 2008 Workshop : Grand Challenge Liver Tumor Segmentation). http://www.insight-journal.org/browse/journal/45 http://www.insight-journal.org/browse/journal/4510.54294/rofibwSearch in Google Scholar

Häme, Y., Pollari, M. (2011). Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation. Medical Image Analysis, 16 (1), 140-149.Search in Google Scholar

Mikulka, J., Gescheidtova, E., Bartusek, K. (2010). Processing of MR slices of human liver for volumetry. In Proceedings of PIERS 2010, 22-26 March 2010. Xi'an, China, 202-204.Search in Google Scholar

Smirg, O., Liberda, O., Sprlakova, A., Smekal, Z. (2011). Creating a 3D model of the temporomandibular joint disc on the basis of segmented MRI slices. In 34th International Conference on Telecommunications and Signal Processing - TSP 2011, 18-20 August 2011. Budapest, Hungary, 369-375.10.1109/TSP.2011.6043707Search in Google Scholar

Smekal, Z., Liberda, O., Bartusek, K., Mikulka, J. (2009). Data processing in studying the temporomandibular joint, using MR imaging and sonographic techniques. In 16th International Conference on Digital Signal Processing - DSP 2009. Santorini, Greece, 100-106.Search in Google Scholar

Osher, S., Fedkiw, R. (2003). Level Set Method and Dynamic Implicit Surfaces. New York: Springer-Verlag.10.1007/b98879Search in Google Scholar

Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. Academic Press.Search in Google Scholar

Bushberg, J. T. et al. (2006). The Essential Physics of Medical Imaging. Philadelphia: Lippincott Williams & Wilkins.Search in Google Scholar

Rabbani, H. (2011). Statistical modeling of low SNR magnetic resonance images in wavelet domain using Laplacian prior and two-sided Rayleigh noise for visual quality improvement. In Measurement Science Review, 11 (4), 125-130.10.2478/v10048-011-0023-0Search in Google Scholar

Peli, E. (1990). Contrast in complex images. Journal of the Optical Society of America, 7 (10), 2032-2040.10.1364/JOSAA.7.002032Search in Google Scholar

Michelson, A. A. (1995). Studies in Optics. Dover Publications.Search in Google Scholar

Xiaomei, S. et al. (2004). Automated region detection based on the contrast-to-noise ratio in near-infrared tomography. Applied Optics, 43 (5), 1053-1062.Search in Google Scholar

Aubert, G., Kornprobst, P. (2006). Mathematical Problems in Image Processing : Partial Differential Equations and the Calculus of Variations (2nd ed.) New York: Springer.10.1007/978-0-387-44588-5Search in Google Scholar

Li, Ch., Xu, Ch., Gui, Ch., Fox, M. D. (2005). Level set evolution without re-initialization: A new variational formulation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition - CVPR'05. IEEE, 430-436.Search in Google Scholar

Osher, S., Sethian, J. A. (1988). Fronts propagation with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79, 12-49.10.1016/0021-9991(88)90002-2Search in Google Scholar

Vese, L. A., Chan, T. F. (2002). A multiphase level set framework for image segmentation using the Mumford and Shah Model. International Journal of Computer Vision, 50 (3), 271-293.10.1023/A:1020874308076Search in Google Scholar

Lankton, S., Tannenbaum, A. (2008). Localizing region-based active contours. IEEE Transactions on Image Processing, 17 (11), 2029-2039.10.1109/TIP.2008.2004611279611218854247Search in Google Scholar

Shi, Y., Karl, W. C. (2008). A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Transactions on Image Processing, 17 (5), 645-655.Search in Google Scholar

Xu, C., Yezzi, A., Prince, J. (2000). On the relationship between parametric and geometric active contours. In 34th Asilomar Conference on Signals, Systems and Computers, 483-489.Search in Google Scholar

Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W. E., Willsky, A. (2003). A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22 (2), 137-154.10.1109/TMI.2002.80835512715991Search in Google Scholar