Abstract
It is necessary to analyze an image from CT or MR and then to segment an image of a certain organ from that of other tissues for 3D (Three-Dimensional) visualization. There are many ways for segmentation, but they have a somewhat ineffective problem because they are combined with manual treatment. In this study, we developed a new segmenting method using a region-growing technique and a deformable modeling technique with control points for more effective segmentation. As a result, we try to extract the image of liver and identify the improved performance by applying the algorithm suggested in this study to two-dimensional CT image of the stomach that has a wide gap between slices.
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References
Jayaram, K., Gabor, T.: 3D Imaging in Medicine, 2nd edn. CRC Press, Boca Raton (2000)
Yoo, S.: A Study on the Segmentation of Liver and Spleen From the CT Image. Master thesis of Industrial Education, Chungnam National University in korea (1999)
Luomin, G., David, G., Brian, S., Elliot, K.: Automatic Liver Segmentation Technique for Three-dimensional Visualization of CT Data. Radiology 201, 359–364 (1996)
Marc, J., Bernard, M.: Three-Dimensional Segmentation and Interpolation of Magnetic Resonance Brain Images. IEEE Transactions on Medical Imaging 12(2), 573–577 (1993)
Michael, B., Karl, H., Ulf, T., Martin, R.: 3-D Segmentation of MR images of the Head for 3-D Display. IEEE Transactions on Medical Imaging 9(2), 152–164 (1990)
Karl, H., William, A.: Interactive 3D Segmentation of MRI and CT Volumes using Morphological Operations. J. Com. Assist. Tomogr. 578–580 (1992)
Zahid, H.: Digital Image Processing. Ellis Horwood (1991)
Parker, J.: Algorithms For Image Processing and Computer Vision. John Wiley & Sons, Chichester (1997)
Bourke, F.: Spline curves in 3D, http://astronomy.swin.edu.au/pbourke/curves/spline
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© 2004 Springer-Verlag Berlin Heidelberg
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Seong, W., Kim, EJ., Park, JW. (2004). Automatic Segmentation Technique Without User Modification for 3D Visualization in Medical Images. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_93
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DOI: https://doi.org/10.1007/978-3-540-30497-5_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24127-0
Online ISBN: 978-3-540-30497-5
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