Abstract
The first significant process for liver diagnosis of the computed tomography is to segment the liver structure from other abdominal organs. In this paper, we propose an efficient liver segmentation algorithm using the spine as a reference point without the reference image and training data. A multi-modal threshold method based on piecewise linear interpolation extracts ranges of regions of interest. Spine segmentation is performed to find the reference point providing geometrical coordinates. C-class maximum a posteriori decision using the reference point selects the liver region. Then binary morphological filtering is processed to provide better segmentation and boundary smoothing. In order to evaluate automatically segmented results of the proposed algorithm, the area error rate and rotational binary region projection matching method are applied. Evaluation results suggest proposed liver segmentation has strong similarity performance as the manual method of a medical doctor.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Tsai, D.: Automatic segmentation of liver structure in CT images using a neural network. IEICE Trans. Fundamentals E77-A(11), 1892–1895 (1994)
Husain, S.A., Shigeru, E.: Use of neural networks for feature based recognition of liver region on CT images. In: Neural Networks for Sig. Proc.-Proceedings of the IEEE Work, 2nd edn., pp. 831–840 (2000)
Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice-Hall, Upper Saddle River (2001)
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 CA (2000)
Ludeman, L.C.: Random Processes: Filtering, Estimation, and Detection. Wiley & Sons, Inc., Hoboken (2003)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. 2nd. edn. Prentice-Hall Inc., Upper Saddle River (2002)
Pitas, I.: Digital Image Processing Algorithms and Applications. Wiley & Sons, Inc., New York (2000)
Jahne, B.: Digital Image Processing, 5th edn. Springer, Heidelberg (2002)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 2nd edn. Brooks/Cole Publishing Com, Pacific Grove (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Seo, KS., Ludeman, L.C., Park, SJ., Park, JA. (2004). Efficient Liver Segmentation Based on the Spine. In: Yakhno, T. (eds) Advances in Information Systems. ADVIS 2004. Lecture Notes in Computer Science, vol 3261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30198-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-540-30198-1_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23478-4
Online ISBN: 978-3-540-30198-1
eBook Packages: Computer ScienceComputer Science (R0)