Detection of Prostate Abnormality within the Peripheral Zone using Local Peak Information

Andrik Rampun, Paul Malcolm, Reyer Zwiggelaar


In this paper, a fully automatic method is proposed for the detection of prostate cancer within the peripheral zone. The method starts by filtering noise in the original image followed by feature extraction and smoothing which is based on the Discrete Cosine Transform. Next, we identify the peripheral zone area using a quadratic equation and divide it into left and right regions. Subsequently, peak detection is performed on both regions. Finally, we calculate the percentage similarity and Ochiai coefficients to decide whether abnormality occurs. The initial evaluation of the proposed method is based on 90 prostate MRI images from 25 patients and 82.2% (sensitivity/specificity: 0.81/0.84) of the slices were classified correctly with 8.9% false negative and false positive results.


  1. Abreu, R., Zoeteweij, P., Golsteijn, R., and van Gemund, A. J. (2009). A practical evaluation of spectrum-based fault localization. Journal of Systems and Software, 82(11):1780 - 1792. ¡ce:title¿SI: fTAICg fPARTg 2007 and fMUTATIONg 2007¡/ce:title¿.
  2. Abreu, R., Zoeteweij, P., and Van Gemund, A. J. C. (2006). An evaluation of similarity coefficients for software fault localization. In Dependable Computing, 2006.
  3. Aus, G., Hermansson, C. G., Hugosson, J., and Pedersen, K. V. (2008). Transrectal ultrasound examination of the prostate: complications and acceptance by patients. British journal of urology, 71(4):457-459.
  4. Bast, R. C., Kufe, D. W., Pollock, R. E., Weichselbaum, R. R., Holland, J. F., Frei, E., Halvorsen, R. A., and Thompson, W. M. (2000). Imaging neoplasms of the abdomen and pelvis.
  5. Brawer, M. K. (1991). Prostate specic antigen: A review. Acta Oncologica., 30(2):161-168.
  6. Cabeen, K. and Gent, P. (2012). pression and the Discrete Cosine 11-August-2013.
  7. Castaneda, B., An, L., Wu, S., Baxter, L. L., Yao, J. L., Joseph, J. V., Hoyt, K., Strang, J., Rubens, D., and Parker, K. J. (2009). Prostate cancer detection using crawling wave sonoelastography. In Proc. SPIE 7265, Medical Imaging 2009: Ultrasonic Imaging and Signal Processing.
  8. Choi, Y. F., Kim, F. K., Kim, N., Kim, K. W., Choi, E. K., and Cho, K.-S. (2007). Functional MR imaging of prostate cancer. RadioGraphics, 27(1):63-68.
  9. Clausi, D. A. (2002). An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sensing, 28(1):45-62.
  10. Edge, S. B., Byrd, D. R., Compton, C., Fritz, A. G., Greene, F. L., and Trotti, A. (2010). AJCC Cancer Staging Manual (7th Edition). Springer., Chicago, US.
  11. Ekstrom, P. M. (1984). Digital Image Processing Techniques. Academic Press, Inc., London, UK.
  12. Engelbrecht, M. R., Puech, P., Colin, P., Akin, O. Lemaitre, L., and Villers, A. (2010). Multimodality magnetic resonance imaging of prostate cancer. Journal of endourology society, 24(5):677-684.
  13. Ftterer, J. J., Heijmink, S. W. T. P. J., Scheenen, T. W. J., Veltman, J., Huisman, H. J., Vos, P., de Kaa, C. A. H., Witjes, J. A., Krabbe, P. F. M., Heerschap, A., and Barentsz, J. O. (2006). Prostate cancer localization with dynamic contrast-enhanced mr imaging and proton mr spectroscopic imaging. Radiology, 241(2):449-458. PMID: 16966484.
  14. Garcia, D. (2010). Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics and Data Analysis, 54(4):1167-1178.
  15. Garnick, M. B., MacDonald, A., Glass, R., and Leighton, S. (2012). Harvard Medical School 2012 Annual Report on Prostate Diseases. Harvard Medical School, Harvard , US.
  16. Ginat, D. T., Destounis, S. V., Barr, R. G., Castaneda, B., Strang, J. G., and Rubens, D. J. (2009). Us elastography of breast and prostate lesions1. Radiographics, 29(7):2007-2016.
  17. Girouin, N., Mège-Lechevallier, F., Senes, A. T., Bissery, A., Rabilloud, M., Maréchal, J., Colombel, M., Lyonnet, D., and Rouvière, O. (2007). Prostate dynamic contrast-enhanced mri with simple visual diagnostic criteria: is it reasonable? European radiology, 17(6):1498-1509.
  18. Halpern, E. J., Cochlin, D. L., and Goldberg, B. (2002). Imaging of the Prostate. Martin Dunitz Ltd., London, UK.
  19. Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6):610-621.
  20. Hardle, W. (1991). Smoothing Techniques With Implementation in S. Springer-Verlag., Louvain-La-Neuve, Belgium.
  21. Hofer, B. and Wotawa, F. (2012). Spectrum enhanced dynamic slicing for better fault localization. In ECAI, pages 420-425.
  22. Instruments (2013). Wavelet-based peak detection. Accessed 24-July-2013.
  23. Jain, A. K. and Karu, K. (1996). Learning texture discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18:195-205.
  24. Jones, J. A. and Harrold, M. J. (2005). Empirical evaluation of the tarantula automatic fault-localization technique. In Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering, ASE 7805, pages 273-282, New York, NY, USA. ACM.
  25. Kenny, T. (2012). Prostate cancer. Accessed 12-August-2013.
  26. Lahdenoja, O., Laiho, M., and Paasio, A. (2005). Local binary pattern feature vector extraction with cnn. In 9th International Workshop on Cellular Neural Networks and Their Applications, pages 202-205. IEEE Cat.
  27. Latha, I., Reichenbach, S. E., and Tao, Q. (2011). Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography. RadioGraphics, 1218(38):6792-6798.
  28. Litjens, G. J. S., Vos, P. C., Barentsz, J. O., Karssemeijer, N., and Huisman, H. J. (2011). Automatic computer aided detection of abnormalities in Multi-Parametric prostate MRI. In Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis.
  29. Llobet, R., Juan, C., Cortes, P., Juan, A., and Toselli, A. (2007). Computer-aided detection of prostate cancer. International Journal of Medical Informatics, 76(7):547-556.
  30. MathWorks (2013).
  31. Documentation center: findpeaks. prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI. Physics in Medicine and Biology, 55:1719-1734.
  32. Watson, A. B. (1993). Dctune: A technique for visual optimization of dct quantization matrices for individual images. Society for Information Display Digest of Technical Papers, XXIV:946-949.
  33. Wong, A. and Scharcanski, J. (2011). Fisher #x2013;tippett region-merging approach to transrectal ultrasound prostate lesion segmentation. Information Technology in Biomedicine, IEEE Transactions on, 15(6):900- 907.
  34. Yue, J. C. and Clayton, M. K. (2005). A similarity measure based on species proportions. Communications in Statistics - Theory and Methods, 34(11):2123-2131.
  35. Zheng, D., Zhao, Y., and Wang, J. (2004). Feature extraction using a gabor filter family. In Proceedings of the 6th IASTED International Conference on Signal and Image Processing. The International Association of Science and Technology for Development.
  36. Zhu, S., Wu, J., and Xia, G. (2010). Top-k cosine similarity interesting pairs search. In Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
  37. Accessed 23-August-2013.
  38. McAllister, H. C. and Bellittiere, D. (1996). The sum of two vectors. Accessed 19-August-2013.
  39. Ochiai, A. (1957). Zoo geographical studies on the solenoid fishes found Japan and its neighboring regions.ii. Bull.Japan.Soc.Sci.Fisheries., 22(9):526-530.
  40. PCUK (2013). Prostate cancer facts and figures. Accessed 11-August-2013.
  41. Rampun, A., Chen, Z., and Zwiggelaar, R. (2013a). Detection and localisation of prostate abnormalities. In 3rd International Conference on Computational & Mathematical Biomedical Engineering, CMBE 13.
  42. Rampun, A., Strange, H., and Zwiggelaar, R. (2013b). Texture segmentation using different orientations of GLCM features. In 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications, MIRAGE 13.
  43. Reinsberg, S. A., Payne, G. S., Riches, S. F., Ashley, S., Brewster, J. M., Morgan, V. A., and deSouza, N. M. (2007). Combined use of Diffusion-Weighted MRI and 1H MR Spectroscopy to increase accuracy in prostate cancer detection. American Journal of Roentgenology, 188(1):1122-1129.
  44. Roehl, K. A., Antenor, J. A. V., and Catalona, W. J. (2002). Serial biopsy results in prostate cancer screening study. British journal of urology, 167(6):2435- 2439.
  45. Shirley, A. and Brewster, S. (2011). Expert review: The digital rectal examination. Journal of Clinical Examination, 11:1-12.
  46. Soh, L. and Tsatsoulis, C. (1999). Texture analysis of sar sea ice imagery using gray level co-occurrence matrices. Geoscience and Remote Sensing, IEEE Transactions on, 37(2):780-795.
  47. Sung, Y. S., Kwon, H.-J., Park, B. W., Cho, G., Lee, C. K., Cho, K.-S., and Kim, J. K. (2011). Prostate cancer detection on dynamic contrast-enhanced mri: Computer-aided diagnosis versus single perfusion parameter maps. American Journal of Roentgenology, 197(5):1122-1129.
  48. Taneja, S. S. (2004). Imaging in the diagnosis and management of prostate cancer. Reviews in Urology, 6(3):101.
  49. Tempany, C. and Franco, F. (2012). Prostate MRI: Update and current roles. Applied Radiology, 41(3):17-22.
  50. Tidy, C. (2013). Prostate specific antigen (PSA) test. Accessed 12-August-2013.
  51. Tiwari, P., Madabhushi, A., and Rosen, M. (2007). A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). In Medical Image Computing and Computer Assisted Intervention (MICCAI), pages 278-286.
  52. Vos, P. C., Hambrock, T., Barentsz, J., and Huisman, H. (2010). Computer-assisted analysis of peripheral zone

Paper Citation

in Harvard Style

Rampun A., Malcolm P. and Zwiggelaar R. (2014). Detection of Prostate Abnormality within the Peripheral Zone using Local Peak Information . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 510-519. DOI: 10.5220/0004762905100519

in Bibtex Style

author={Andrik Rampun and Paul Malcolm and Reyer Zwiggelaar},
title={Detection of Prostate Abnormality within the Peripheral Zone using Local Peak Information},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Detection of Prostate Abnormality within the Peripheral Zone using Local Peak Information
SN - 978-989-758-018-5
AU - Rampun A.
AU - Malcolm P.
AU - Zwiggelaar R.
PY - 2014
SP - 510
EP - 519
DO - 10.5220/0004762905100519