Skip to main content
Springer Nature Link
Log in

3D Texton Based Prostate Cancer Detection Using Multiparametric Magnetic Resonance Imaging

  • Conference paper
  • First Online:
Medical Image Understanding and Analysis (MIUA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 723))

Included in the following conference series:

  • 3239 Accesses

Abstract

Multiparametric magnetic resonance imaging (mp-MRI) has shown its potential in prostate cancer detection. In this study, we investigate the application of 3D texton based prostate cancer detection using T2-weighted (T2W) MRI, dynamic contrast-enhanced (DCE) MRI and apparent diffusion coefficient (ADC) maps. For the T2W and ADC modalities, the traditional texton based approach is adopted, i.e., for each voxel, a texton histogram is extracted as the feature to perform the classification. For the DCE data, we present a new method, where the textons are extracted from each series and for each voxel, the corresponding textons across all series are used as features. A random forest classifier is applied for classifying all voxels into benign or malignant. The evaluation is conducted by performing a receiver operating characteristics (ROC) analysis and computing the area under the curve (AUC). The experiments on the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) database demonstrate that the texton based approach using mp-MRI data obtains excellent performance in prostate cancer detection and produces \(88.3\%\) accuracy, whereas the accuracy produced by an intensity based approach is \(79.8\%\).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/I2Cvb/mp-mri-prostate.

References

  1. Ferlay, J., Shin, H.R., Bray, F., Forman, D., Mathers, C., Parkin, D.M.: Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer 127(12), 2893–2917 (2008)

    Article  Google Scholar 

  2. Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 63(1), 11–30 (2013)

    Article  Google Scholar 

  3. Etzioni, R., Penson, D.F., Legler, J.M., Di Tommaso, D., Boer, R., Gann, P.H., Feuer, E.J.: Overdiagnosis due to prostate-specific antigen screening: lessons from US prostate cancer incidence trends. J. Natl Cancer Inst. 94(13), 981–990 (2002)

    Article  Google Scholar 

  4. Chou, R., Croswell, J.M., Dana, T., Bougatsos, C., Blazina, I., Fu, R., Gleitsmann, K., Koenig, H.C., Lam, C., Maltz, A., Rugge, J.B.: Screening for prostate cancer: a review of the evidence for the US preventive services task force. Ann. Intern. Med. 155(11), 762–771 (2011)

    Article  Google Scholar 

  5. Schröder, F.H., Carter, H.B., Wolters, T., van den Bergh, R.C., Gosselaar, C., Bangma, C.H., Roobol, M.J.: Early detection of prostate cancer in 2007: part 1: PSA and PSA kinetics. Eur. Urol. 53(3), 468–477 (2007)

    Article  Google Scholar 

  6. Delpierre, C., Lamy, S., Kelly-Irving, M., Molini, F., Velten, M., Tretarre, B., Woronoff, A.S., Buemi, A., Laptre-Ledoux, B., Bara, S., Guizard, A.V.: Life expectancy estimates as a key factor in over-treatment: the case of prostate cancer. Cancer Epidemiol. 37(4), 462–468 (2013)

    Article  Google Scholar 

  7. Delongchamps, N.B., Peyromaure, M., Schull, A., Beuvon, F., Bouazza, N., Flam, T., Zerbib, M., Muradyan, N., Legman, P., Cornud, F.: Prebiopsy magnetic resonance imaging and prostate cancer detection: comparison of random and targeted biopsies. J. Urol. 189(2), 493–499 (2013)

    Article  Google Scholar 

  8. Vos, P.C., Barentsz, J.O., Karssemeijer, N., Huisman, H.J.: Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis. Phys. Med. Biol. 57(6), 1527 (2012)

    Article  Google Scholar 

  9. Viswanath, S., Bloch, B.N., Chappelow, J., Patel, P., Rofsky, N., Lenkinski, R., Genega, E., Madabhushi, A.: Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI. In: Proceedings of SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, p. 79630U (2011)

    Google Scholar 

  10. Tiwari, P., Kurhanewicz, J., Madabhushi, A.: Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS. Med. Image Anal. 17(2), 219–235 (2013)

    Article  Google Scholar 

  11. Trigui, R., Mitran, J., Walker, P.M., Sellami, L., Hamida, A.B.: Automatic classification and localization of prostate cancer using multi-parametric MRI/MRS. Biomed. Sig. Process. Control 31, 189–198 (2017)

    Article  Google Scholar 

  12. Lemaître, G., Mart, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F.: Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput. Biol. Med. 60, 8–31 (2015)

    Article  Google Scholar 

  13. Zhang, L., Fisher, M., Wang, W.: Retinal vessel segmentation using Gabor filter and textons. In: Medical Image Understanding and Analysis, pp. 155–160 (2014)

    Google Scholar 

  14. Gangeh, M.J., Srensen, L., Shaker, S.B., Kamel, M.S., De Bruijne, M., Loog, M.: A texton-based approach for the classification of lung parenchyma in CT images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 595–602 (2010)

    Google Scholar 

  15. Julesz, B.: A theory of preattentive texture discrimination based on first-order statistics of textons. Biol. Cybern. 41(2), 131–138 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  16. Rampun, A., Tiddeman, B., Zwiggelaar, R., Malcolm, P.: Computer aided diagnosis of prostate cancer: a texton based approach. Med. Phys. 43(10), 5412–5425 (2016)

    Article  Google Scholar 

  17. Lemaître, G., Dastjerdi, M.R., Massich, J., Vilanova, J.C., Walker, P.M., Freixenet, J., Meyer-Baese, A., Mriaudeau, F., Mart, R.: Normalization of T2W-MRI prostate images using Rician a priori. In: SPIE Medical Imaging 2016: Computer-Aided Diagnosis, p. 9785 (2016)

    Google Scholar 

  18. Lemaître, G.: Computer-aided diagnosis for prostate cancer using multi-parametric magnetic resonance imaging. Doctoral dissertation, Universite de Bourgogne; Universitat de Girona (2016)

    Google Scholar 

  19. Nyúl, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)

    Article  Google Scholar 

  20. Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X.: Applied logistic regression, vol. 398. Wiley, Hoboken (2013)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK

    Liping Wang & Reyer Zwiggelaar

Authors
  1. Liping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reyer Zwiggelaar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liping Wang .

Editor information

Editors and Affiliations

  1. Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom

    María Valdés Hernández

  2. University of León, León, Spain

    Víctor González-Castro

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wang, L., Zwiggelaar, R. (2017). 3D Texton Based Prostate Cancer Detection Using Multiparametric Magnetic Resonance Imaging. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60964-5_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60963-8

  • Online ISBN: 978-3-319-60964-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics