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Fully Automatic Multispectral MR Image Segmentation of Prostate Gland Based on the Fuzzy C-Means Clustering Algorithm

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Multidisciplinary Approaches to Neural Computing

Abstract

Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is proposed. This approach enables prostate segmentation and automatic gland volume calculation. Segmentation trials have been performed on a dataset composed of 7 patients affected by prostate cancer, using both area-based and distance-based metrics for its evaluation. The achieved experimental results are encouraging, showing good segmentation accuracy.

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References

  1. Klein, S., van der Heide, U.A., Lips, I.M., van Vulpen, M., Staring, M., Pluim, J.P.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med. Phys. 35(4), 1407–1417 (2008). doi:10.1118/1.2842076

  2. Rouvière, O., Lyonnet, D., Raudrant, A., Colin-Pangaud, C., Chapelon, J.Y., Bouvier, R., Dubernard, J.M., Gelet, A.: MRI appearance of prostate following transrectal HIFU ablation of localized cancer. Eur. Urol. 40, 265–274 (2001). doi:10.1159/000049786

  3. Villeirs, G.M., De Meerleer, G.O.: Magnetic resonance imaging (MRI) anatomy of the prostate and application of MRI in radiotherapy planning. Eur. J. Radiol. 63(3), 361–368 (2007). doi:10.1016/j.ejrad.2007.06.030

  4. Ghose, S., Oliver, A., Martí, R., Lladó, X., Vilanova, J.C., Freixenet, J., Mitra, J., Sidibé, D., Meriaudeau, F.: A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput. Meth. Prog. Bio. 108(1), 262–287 (2012). doi:10.1016/j.cmpb.2012.04.006

  5. Chilali, O., Ouzzane, A., Diaf, M., Betrouni, N.: A survey of prostate modeling for image analysis. Comput. Biol. Med. 53, 190–202 (2014). doi:10.1016/j.compbiomed.2014.07.019

  6. Lemaître, G., Martí, 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). doi:10.1016/j.compbiomed.2015.02.009

  7. Rosenkrantz, A.B., Lim, R.P., Haghighi, M., Somberg, M.B., Babb, J.S., Taneja, S.S.: Comparison of interreader reproducibility of the prostate imaging reporting and data system and likert scales for evaluation of multiparametric prostate MRI. Am. J. Roentgenol. 201(4), W612–W618 (2013). doi:10.2214/AJR.12.10173

  8. Caivano, R., Cirillo, P., Balestra, A., Lotumolo, A., Fortunato, G., Macarini, L., Zandolino, A., Vita, G., Cammarota, A.: Prostate cancer in magnetic resonance imaging: diagnostic utilities of spectroscopic sequences. J. Med. Imag. Radiat. On. 56(6), 606–616 (2012). doi:10.1111/j.1754-9485.2012.02449.x

    Article  Google Scholar 

  9. Rouvière, O., Hartman, R.P., Lyonnet, D.: Prostate MR imaging at high-field strength: Evolution or revolution? Eur. Radiol. 16(2), 276–284 (2006). doi:10.1007/s00330-005-2893-8

    Article  Google Scholar 

  10. Vincent, G., Guillard, G., Bowes, M.: Fully automatic segmentation of the prostate using active appearance models. In: Medical Image Computing and Computer Assisted Intervention (MICCAI) Grand Challenge: Prostate MR Image Segmentation 2012, Nice, France, 7 p. (2012)

    Google Scholar 

  11. Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., et al.: Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Med. Image Anal. 18(2), 359–373 (2014). doi:10.1016/j.media.2013.12.002

    Article  Google Scholar 

  12. Litjens, G., Debats, O., van de Ven, W., Karssemeijer, N., Huisman, H.: A pattern recognition approach to zonal segmentation of the prostate on MRI. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), p. 413–420. Springer, Berlin Heidelberg. (2012). doi:10.1007/978-3-642-33418-4_51

  13. Gao, Y., Sandhu, R., Fichtinger, G., Tannenbaum, A.R.: A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE T. Med. Imaging 29(10), 1781–1794 (2010). doi:10.1109/TMI.2010.2052065

    Article  Google Scholar 

  14. Martin, S., Daanen, V., Troccaz, J.: Atlas-based prostate segmentation using an hybrid registration. Int. J. Comput. Assist. Radiol. Surg. 3(6), 485–492 (2008). doi:10.1007/s11548-008-0247-0

    Article  Google Scholar 

  15. Martin, S., Troccaz, J., Daanen, V.: Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model. Med. Phys. 37(4), 1579–1590 (2010). doi:10.1118/1.3315367

    Article  Google Scholar 

  16. Choi, Y.J., Kim, J.K., Kim, N., Kim, K.W., Choi, E.K., Cho, K.S.: Functional MR imaging of prostate cancer. Radiographics 27(1), 63–75 (2007). doi:10.1148/rg.271065078

    Article  Google Scholar 

  17. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE T. Med. Imaging 22(8), 986–1004 (2003). doi:10.1109/TMI.2003.815867

    Article  MATH  Google Scholar 

  18. Mattes, D., Haynor, D.R., Vesselle, H., Lewellen, T., Eubank, W.: Non-rigid multimodality image registration. In: Medical Imaging 2001: Image Processing, 1609, Proceedings of SPIE 4322, pp. 1609–1620 (2001). doi:10.1117/12.431046

  19. Styner, M., Brechbuhler, C., Szckely, G., Gerig, G.: Parametric estimate of intensity inhomogeneities applied to MRI. IEEE T. Med. Imaging 19(3), 153–165 (2000). doi:10.1109/42.845174

    Article  Google Scholar 

  20. Czerwinski, R.N., Jones, D.L., O’Brien, W.D.: Line and boundary detection in speckle images. IEEE T. Image Process. 7(12), 1700–1714 (1998). doi:10.1109/83.730381

    Article  Google Scholar 

  21. Xiao, C.Y., Zhang, S., Cheng, S., Chen, Y.Z.: A novel method for speckle reduction and edge enhancement in ultrasonic images. In: Electronic Imaging and Multimedia Technology IV, 469. Proceedings of SPIE 5637, 28 February, 2005. doi:10.1117/12.575389

  22. Lagendijk, J.J.W., Raaymakers, B.W., Van den Berg, C.A.T., Moerland, M.A., Philippens, M.E., van Vulpen, M.: MR guidance in radiotherapy. Phys. Med. Biol. 59, R349–R369 (2014). doi:10.1088/0031-9155/59/21/R349

    Article  Google Scholar 

  23. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy C-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984). doi:10.1016/0098-3004(84)90020-7

    Article  Google Scholar 

  24. Militello, C., Vitabile, S., Rundo, L., Russo, G., Midiri, M., Gilardi, M.C.: A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation. Comput. Biol. Med. 62, 277–292 (2015). doi:10.1016/j.compbiomed.2015.04.030

    Article  Google Scholar 

  25. Fenster, A., Chiu, B.: Evaluation of segmentation algorithms for medical imaging. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 7186–7189 (2005). doi:10.1109/IEMBS.2005.1616166

  26. Rundo, L., Militello, C., Vitabile, S., Russo, G., Pisciotta, P., Marletta, F., Ippolito, M., D’Arrigo, C., Midiri, M., Gilardi, M.C.: Semi-automatic brain lesion segmentation in gamma knife treatments using an unsupervised fuzzy c-means clustering technique. In: Bassis, S., Esposito, A., Morabito, F.C., Pasero, E. (eds.) Advances in Neural Networks: Computational Intelligence for ICT, Smart Innovation, Systems and Technologies, vol. 54, pp. 15–26, Springer International Publishing (2016). doi:10.1007/978-3-319-33747-0_2

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Rundo, L. et al. (2018). Fully Automatic Multispectral MR Image Segmentation of Prostate Gland Based on the Fuzzy C-Means Clustering Algorithm. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-56904-8_3

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