Advertisement

Prostate Tumor Identification in Ultrasound Images

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)

Abstract

There are various medical imaging instruments used for diagnosing prostatic diseases. Ultrasound imaging is the most widely used tool in clinical diagnosis. Urologist outlines the prostate and diagnoses lesions based on his/her experiences. This diagnostic process is subjective and heuristic. Active contour model (ACM) has been successfully applied to outline the prostate contour. However, application of ACM in outlining the contour needs to give the initial contour points manually. In this paper, an automatic prostate tumor identification system is proposed. The sequential floating forward selection (SFFS) is applied to select significant features. A support vector machine (SVM) with radial basis kernel function is used for prostate tumor identification. Experimental results showed that the proposed method achieved higher accuracy than those of other methods.

Keywords

prostate tumor feature selection support vector machine 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines, 2.91. ed: Citeseer (2001)Google Scholar
  2. 2.
    Maroulis, D.E., Savelonas, M.A., Iakovidis, D.K., Karkanis, S.A., Dimitropoulos, N.: Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images. IEEE Trans. Infor. Tech. in Biomed. 11, 537–543 (2007)CrossRefGoogle Scholar
  3. 3.
    Chen, D.R., Chang, R.F., Wu, W.J., Moon, W.K., Wu, W.L.: 3-D breast ultrasound segmentation using active contour model. Ultrasound in Medicine and Biology 29, 1017–1026 (2003)CrossRefGoogle Scholar
  4. 4.
    Sahba, F., Tizhoosh, H.R., Salama, M.M.: Application of Reinforcement Learning for Segmentation of Transrectal Ultrasound Images. BMC Medical Imaging, 1471–2342 (2008)Google Scholar
  5. 5.
    Laws, K.: Textured image segmentation. University of southern california, Los Angeles Image Processing Institute (1980)Google Scholar
  6. 6.
    Betrouni, N., Vermandel, M., Pasquier, D., Maouche, S., Rousseau, J.: Segmentation of Abdominal Ultrasound Images of the Prostate Using a Priori Information and an Adapted Noise Filter. Computerized Medical Imaging and Graphics, 43–51 (2005)Google Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. (1992)Google Scholar
  8. 8.
    Mohamed, S.S., Salama, M.M.: Spectral Clustering for TRUS Images. BioMedical Engineering OnLine (2007)Google Scholar
  9. 9.
    Zhang, Y., Sankar, R., Qian, W.: Boundary delineation in transrectal ultrasound image for prostate cancer. Comp. Biol. Med., 1591–1599 (2007)Google Scholar
  10. 10.
    Chum, Y.D., Seo, S.Y.: Image Retrieval using BDIP and BVLC Moments. IEEE Transactions on Circuits and Systems for Video Tech. 13, 951–957 (2003)CrossRefGoogle Scholar
  11. 11.
    Chen, E.L., Chung, P.C., Chen, C.L., Tsai, H.M., Chang, C.I.: An automatic diagnostic system for CT liver image classification. IEEE Trans. on Biomedical Engineering 45, 783–794 (1998)CrossRefGoogle Scholar
  12. 12.
    Chang, C.Y., Tsai, Y.S., Wu, I.L.: Integrating Validation Incremental Neural Network And Radial-Basis Function Neural Network For Segmenting Prostate In Ultrasound Images. International Journal of Innovative Computing, Information and Control 7, 3035–3046 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Yunlin University of Science and TechnologyYunlinTaiwan
  2. 2.Department of UrologyNational Cheng Kung University HospitalTainanTaiwan

Personalised recommendations