Abstract
This paper proposes automatic boundary tumor segmentation for the computer aided liver diagnosis system. As pre-processing, the liver structure is first segmented using histogram transformation, multi-modal threshold, C-class maximum a posteriori decision, and binary morphological filtering. After binary transformation of the liver structure, the image based bounding box is created and convex deficiencies are segmented. Large convex deficiencies are selected by pixel area estimation and selected deficiencies are transformed to gray-level deficiencies. The boundary tumor is selected by estimating the variance of deficiencies. In order to test the proposed algorithm, 225 slices from nine patients were selected. Experimental results show that the proposed algorithm is very useful for diagnosis of the abnormal liver with the boundary tumor.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Parkin, D.M.: Global cancer statistics in the year 2000. Lancet Oncology 2, 533–554 (2001)
Lee, H.: Liver cancer. The Korean Society of Gastroenterology, Seoul Korea (2001)
Bae, K.T., Giger, M.L., Chen, C.T., Kahn Jr., C.E.: Automatic segmentation of liver structure in CT images. Med. Phys. 20, 71–78 (1993)
Gao, L., Heath, D.G., Kuszyk, B.S., Fishman, E.K.: Automatic liver segmentation technique for three-dimensional visualization of CT data. Radiology 201, 359–364 (1996)
Park, H., Bland, P.H., Meyer, C.R.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans. Med. Imag. 22(4), 483–492 (2003)
Tsai, D.: Automatic segmentation of liver structure in CT images using a neural network. IEICE Trans. Fundamentals E77-A(11), 1892–1895 (1994)
Saitoh, T., Tamura, Y., Kaneko, T.: Automatic segmentation of liver region based on extracted blood vessels. System and Computers in Japan 35(5), 1–10 (2004)
Seo, K., Ludeman, L.C., Park, S., Park, J.: Efficient liver segmentation based on the spine. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 400–409. Springer, Heidelberg (2004)
Orfanidis, S.J.: Introduction to signal processing. Prentice Hall, Upper Saddle River (1996)
Schilling, R.J., Harris, S.L.: Applied numerical methods for engineers. Brooks/Cole Publishing Com, Pacific Grove (2000)
Ludeman, L.C.: Random processes: filtering, estimation, and detection. Wiley & Sons Inc., Hoboken (2003)
Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall, Upper Saddle River (2002)
Shapiro, L.G., Stockman, G.C.: Computer vision. Prentice-Hall, Upper Saddle River (2001)
Parker, J.R.: Algorithms for image processing and computer vision. Wiley Computer Publishing, New York (1997)
Rangayyan, R.M.: Biomedical signal analysis. Wiley, New York (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Seo, KS., Chung, TW. (2005). Automatic Boundary Tumor Segmentation of a Liver. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_87
Download citation
DOI: https://doi.org/10.1007/11424925_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25863-6
Online ISBN: 978-3-540-32309-9
eBook Packages: Computer ScienceComputer Science (R0)