5
Views
2
CrossRef citations to date
0
Altmetric
Original Articles

Distinction of Liver Disease from CT Images Using Kernel-based Classifiers

, , &
Pages 113-120 | Received 03 Nov 2006, Accepted 09 Mar 2007, Published online: 21 Feb 2013

REFERENCES

  • B.B. Gosnik, S.K. Lemon, W. Scheible, and G. R. Leupold “Accuracy of ultrasonography in diagnosis of hepatocellular disease,” AJR, vol. 133, pp. 19–23, 1979.
  • K.J. Foster, K.C. Dewbury, A.H. Griffith, and R. Wright, “The accuracy of ultrasound in the detection of fatty infiltration of the liver,” Br. J. Radiol., vol. 53, pp. 440–442, 1980.
  • Wei, Liyang, Yongyi Yang, Robert M. Nishikawa, and Yulei Jiang, “A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications,” IEEE Trans. Medical Imaging, vol. 24, no. 3, pp. 371–380, 2005.
  • Bernhard Scholkopf, Kah-Kay Sung, Chris J. C. Borges, Federico Girosi, Partha Niyogi, Tomaso Poggio, and Vladimir Vapnik, “Comparing support vector machines with Gaussian kernels to radial basis function classifiers,” IEEE Trans. Signal Processing, vol. 45, no. 11, pp. 2758–2765, 1997.
  • E-Liang Chen, Pau-Choo Chung, Ching-Liang Chen, Hong-Ming Tsai, and Chein-I Chang, “An automatic diagnostic system for CT liver image classification,” IEEE Trans. Biomedical Engineering, vol.45, no. 6, pp. 783–794 1998.
  • Kwang In Kim, Keechul Jung, Se Hyun Park, and Hang Joon Kim, “Support vector machines for texture classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 11, 2002.
  • Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, New York, Chapman and Hall, 1993.
  • V. Vapnik, The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995.
  • C. C. Chang, C. J. Lin, Libsvm: a library for support vector machines, 2001, available at http://www.csie.ntu.edu.tw/˜cjlin/libsvm .
  • William K Pratt, Digital lmage Processing, New York, Wiley, 2001.
  • Yasser M. Kadah, Aly A. Farag, Jacek M. Zurada, Ahmed M. Badawi, and Abou-Bakr M. Youssef, “Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images,” IEEE Trans. Medical lmaging, vol. 15, no. 4, pp. 466–478, 1996.
  • Y. N. Sun, M.-H. Horng, X.-Z. Lin, and J.-Y. Wang, “Ultrasound image analysis for liver diagnosis: A noninvasive alternative to determine liver disease,” IEEE Eng. Med. Biol. Mag., pp. 93–101, 1996.
  • M. Gletsos, et al., “A computer-aided diagnostic system to characterize CT focal liver eesions: design and optimization of a neural network classifier,” IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 3, pp. 153–162, 2003.
  • Jain, Anil K., Robert P.W. Duin, and Jianchang Mao, “Statistical pattern recognition: a review,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4–37, 2000.
  • K. Z. Mao, “Orthogonal forward selection and backward elimination algorithms for feature subset selection,” IEEE Trans. Syst., Man, Cybern. B, vol. 34, no. 1, pp. 629–634, 2004.
  • D. W. Aha and R. L. Bankert, “A comparative evaluation of sequential feature selection algorithms,” In Doug Fisher and Hans-J. Lenz, editors, Learning from Data, chapter 4, pp. 199–206. Springer, New York, 1996.
  • C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans Neural Networks, vol. 13, No. 2, pp. 415–425, 2002.
  • Seong Ho Park, Mo God Jin, and Chan-Hee, “Receiver operating characteristics (ROC) curve: practical review for radiologists,” Korean J. Radiol., pp. 11–18, 2004.
  • Huang, Jin and Charles X. Ling, “Using AUC and accuracy in evaluating learning algorithms,” IEEE Trans. Knowledge and Data Engineering, vol. 17, no. 3, pp. 299–310, 2005.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.