Abstract
Prostate cancer is the most diagnosed form of cancer, but survival rates are relatively high with sufficiently early diagnosis. Current computer-aided image-based cancer detection methods face notable challenges including noise in MRI images, variability between different MRI modalities, weak contrast, and non-homogeneous texture patterns, making it difficult for diagnosticians to identify tumour candidates. We propose a novel saliency-based method for identifying suspicious regions in multi-parametric MR prostate images based on statistical texture distinctiveness. In this approach, a sparse texture model is learned via expectation maximization from features derived from multi-parametric MR prostate images, and the statistical texture distinctiveness-based saliency based on this model is used to identify suspicious regions. The proposed method was evaluated using real clinical prostate MRI data, and results demonstrate a clear improvement in suspicious region detection relative to the state-of-art method.
Keywords
- Computer-aided prostate cancer detection
- Multi-Parametric Magnetic Resonance Imaging (MP-MRI)
- Texture-based saliency
- Statistical textural distinctiveness
A.G. Chung—This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. The study was also funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada and the Ontario Ministry of Economic Development and Innovation.
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References
Aitkin, M., Rubin, D.B.: Estimation and hypothesis testing in finite mixture models. J. Roy. Stat. Soc. Ser. B (Methodological) 47, 67–75 (1985)
Andriole, G.L., Crawford, E.D., Grubb, R.L., Buys, S.S., Chia, D., Church, T.R., Fouad, M.N., Gelmann, E.P., Kvale, P.A., Reding, D.J., Weissfeld, J.L., Yokochi, L.A., O’Brien, B., Clapp, J.D., Rathmell, J.M., Riley, T.L., Hayes, R.B., Kramer, B.S., Izmirlian, G., Miller, A.B., Pinsky, P.F., Prorok, P.C., Gohagan, J.K., Berg, C.D.: Mortality results from a randomized prostate-cancer screening trial. N. Engl. J. Med. 360(13), 1310–1319 (2009)
Barentsz, J.O., Richenberg, J., Clements, R., Choyke, P., Verma, S., Villeirs, G., Rouviere, O., Logager, V., Fütterer, J.J.: ESUR prostate MR guidelines 2012. Eur. Radiol. 22(4), 746–757 (2012)
Cameron, A., Modhafar, A., Khalvati, F., Lui, D., Shafiee, M.J., Wong, A., Haider, M.: Multiparametric MRI prostate cancer analysis via a hybrid morphological-textural model. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3357–3360 (2014)
Canadian Cancer Society: Prostate cancer statistics (2014)
Haider, M.A., van der Kwast, T.H., Tanguay, J., Evans, A.J., Hashmi, A.T., Lockwood, G., Trachtenberg, J.: Combined T2-weighted and diffusion-weighted MRI for localization of prostate cancer. AJR Am. J. Roentgenol. 189(2), 323–328 (2007)
Khalvati, F.: Automated consensus contour building for prostate MRI. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5534–5537, August 2014
Khalvati, F., Modhafar, A., Cameron, A., Wong, A., Haider, M.A.: A multi-parametric diffusion magnetic resonance imaging texture feature model for prostate cancer analysis. In: MICCAI 2014 Workshop on Computational Diffusion MRI, pp. 1–10. Medical Image Computing and Computer Assisted Intervention (2014)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)
Loeb, S., Vellekoop, A., Ahmed, H.U., Catto, J., Emberton, M., Nam, R., Rosario, D.J., Scattoni, V., Lotan, Y.: Systematic review of complications of prostate biopsy. Eur. Urol. 64(6), 876–892 (2013)
Röthke, M., Blondin, D., Schlemmer, H.P., Franiel, T.: PI-RADS classification: structured reporting for MRI of the prostate. RöFo : Fortschritte auf dem Gebiete der Röntgenstrahlen und der Nuklearmedizin 185(3), 253–261 (2013)
Scharfenberger, C., Wong, A., Fergani, K., Zelek, J.S., Clausi, D.A.: Statistical textural distinctiveness for salient region detection in natural images. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 979–986 (2013)
Schröder, F.H., Hugosson, J., Roobol, M.J., Tammela, T.L.J., Ciatto, S., Nelen, V., Kwiatkowski, M., Lujan, M., Lilja, H., Zappa, M., Denis, L.J., Recker, F., Berenguer, A., Määttänen, L., Bangma, C.H., Aus, G., Villers, A., Rebillard, X., van der Kwast, T., Blijenberg, B.G., Moss, S.M., de Koning, H.J., Auvinen, A.: Screening and prostate-cancer mortality in a randomized European study. N. Engl. J. Med. 360(13), 1320–1328 (2009)
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Chung, A.G., Scharfenberger, C., Khalvati, F., Wong, A., Haider, M.A. (2015). Statistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_40
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DOI: https://doi.org/10.1007/978-3-319-20801-5_40
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