Computerized Medical Imaging and Graphics
Volume 33, Issue 8 , Pages 588-592 , December 2009

A computer-aided diagnostic system to discriminate SPIO-enhanced magnetic resonance hepatocellular carcinoma by a neural network classifier

  • Dongmei Guo

      Affiliations

    • Department of Electronic Engineering, Dalian University of Technology, Dalian 116024, China
    • Department of Radiology, Second Affiliated Hospital, Dalian Medical University, Dalian 116027, China
  • ,
  • Tianshuang Qiu

      Affiliations

    • Department of Electronic Engineering, Dalian University of Technology, Dalian 116024, China
    • Corresponding Author InformationCorresponding author.
  • ,
  • Jie Bian

      Affiliations

    • Department of Radiology, Second Affiliated Hospital, Dalian Medical University, Dalian 116027, China
  • ,
  • Wei Kang

      Affiliations

    • Department of Electronic Engineering, Dalian University of Technology, Dalian 116024, China
  • ,
  • Li Zhang

      Affiliations

    • Department of Microbiology, Dalian Medical University, Dalian 116027, China

Received 31 October 2008 ,Accepted 9 April 2009.

References 

  1. Willatt JM, Hussain HK, Adusumilli S, Marrero JA. Imaging of hepatocellular carcinoma in the cirrhotic liver: challenges and controversies. Radiology. 2008;247:311–330
  2. Hecht EM, Holland AE, Israel GM, Hahn WY, Kim DC, Westa B, et al. Hepatocellular carcinomain in the cirrhotic liver: gadolinium-enhanced 3D T1-weighted MR imaging as a stand-alone sequence for diagnosis. Radiology. 2006;239:438
  3. Tanimoto A, Kuribayashi S. Application of superparamagnetic iron oxide to imaging of hepatocellular carcinoma. Eur J Radiol. 2006;58:200–216
  4. Yarnamoto H, Yamashita Y, Takahashi M. Development of hepatomas in hyperplastic nodules induced in the rat liver: detection with superparamagnetic iron oxide-enhanced magnetic resonance imaging. Acad Radiol. 1996;3:330–335
  5. Joo S, Yang YS, Moon WK, Kim HC. Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Trans Med Imag. 2004;23:1292–1300
  6. Levman J, Leung T, Causer P, Plewes D, Martel AL. Classification of dynamic contrast-enhanced. Magnetic resonance breast lesions by support vector machines. IEEE Trans Med Imag. 2008;27(May):688–696
  7. Gletsos MG, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikta AS, Kelekis D, et al. A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans Inform Techn Biomed. 2003;7:153–162
  8. Chen EL, Chung PC, Chen CL, Tsai HM, Chang CI. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng. 1998;45:783–794
  9. Valavanis I, Mougiakakou SG, Nikita KS, Nikita A. Computer aided diagnosis of CT focal liver lesions by an ensemble of neural network and statistical classifiers. IEEE Int Joint Conf Neural Netw. 2004;3:1929–1933
  10. Zhang X, Fujita H, Kanematsu M, Zhou XR, Hara T, Kato H, et al. Improving the classifycation of Cirrhotic liver by using texture features. In: Engineering in Medicine and Biology 27th Annual International Conference. Shanghai. 2005;p. 867–870
  11. McNeal CR, Maynard WH, Branch RA, Powers TA, Arns PA, Gunter K, et al. Liver volume measurements and three-dimensional display from MR images. Radiology. 1988;169:851–864
  12. Zhao WD, Guan S, Zhou KR, Li H, Peng WJ, Tang F, et al. In vivo detection of metabolic changes by 1H-MRS in the DEN-induced hepatocellular carcinoma in wistar rat. J Cancer Res Clin Oncol. 2005;131:597–602
  13. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybernet. 1973;3:610–621
  14. Fukuda H, Ebara M, Kobayashi A, Sugiura N, Yoshikawa M, Saisho H, et al. An image analyzing system using an artificial neural network for evaluating the parenchymal echo pattern of cirrhotic liver and chronic hepatitis. IEEE Trans Biomed Eng. 1998;45:396–400

PII: S0895-6111(09)00045-7

doi: 10.1016/j.compmedimag.2009.04.005

Computerized Medical Imaging and Graphics
Volume 33, Issue 8 , Pages 588-592 , December 2009