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An automated gland segmentation and classification method in prostate biopsies: an image source-independent approach

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Abstract

The aim of this paper is to introduce an image source-independent automated method for segmentation and classification of prostate glands. This research focuses on light microscopic images of the samples from different laboratories using the same staining method. Color information in the image is highly dependent on the source and the conditions under which the image has been taken. The proposed method can be used to analyze images with color variations. Color information is used for the segmentation of tissue structures and Delaunay triangulation is used for gland segmentation. The proposed method uses triangulation to find the basic structure of any shaped and sized gland and to prevent misclassification of gland components. The proposed approach classifies the nuclei circumscribing the glands to single and multilayered. Other features used in the classification are the amount of nuclei and the area of the gland. The number of layers can be used for determining the malignancy of the tissue sample. In most cases, a single-layered gland is malignant and multilayered is benign. This segmentation approach is different than what has been previously used in the literature. In this paper, the glands are classified to four different categories: single layered, multilayered, rejected or nonclassified. This approach distinguishes majority of single and multilayered glands from each other.

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References

  1. de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational geometry: algorithms and applications. Springer, Berlin (2008)

    Book  Google Scholar 

  2. Delahunt, B., Miller, R.J., Srigley, J.R., Evans, A.J., Samaratunga, H.: Gleason grading: past, present and future. Histopathol 60, 75–86 (2012)

  3. DeMarzo, A.M., Nelson, W.G., Isaacs, W.B., Epstein, J.I.: Pathological and molecular aspects of prostate cancer. Lancet 361, 955–964 (2003)

  4. Di Ruberto, C., Dempster, A.: Circularity measures based on mathematical morphology. Electron. Lett. 36, 1691–1693 (2000)

    Article  Google Scholar 

  5. Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans. Biomed. Eng. 59, 1205–1218 (2012)

    Article  Google Scholar 

  6. Fairchild, M.D.: Color appearance models. Wiley, Chichester (2006)

    Google Scholar 

  7. Gonzalez, R.C., Woods, R.E.: Digital image processing. Pearson Prentice Hall, New Jersey (2008)

    Google Scholar 

  8. Kiernan, J.A.: Histological and histochemical methods: theory and practice. Scion, Bloxham (2008)

  9. Lopez, C.M., Agaian, S., Sanchez, I., Almuntashri, A., Zinalabdin, O., Al Rikabi, A.: Exploration of efficacy of gland morphology and architectural features in prostate cancer Gleason grading. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2849–2854 (2012)

  10. Naik, S., Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: 5th IEEE International Symposium on Biomedical Imaging: from Nano to Macro, ISBI 2008, pp. 284–287 (2008)

  11. Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recognit. Lett.33, 951–961 (2012)

  12. Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.H., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39, 355–368 (1987)

  13. Pritchard, D.H.: US color television fundamentals—a review. IEEE Trans. Consum. Electron. 23, 467–478 (1977)

    Article  Google Scholar 

  14. Rashid, S., Falzi, L., Boag, A., Siemens, R., Abolmaesumi, P., Salcudean, S.E.: Separation of benign and malignant glands in prostatic adenocarcinoma. In: MICCAI 2013, Part III, LNCS 8151, pp. 461–468 (2013)

  15. Watt, A., Policarpo, F.: Computer image. Addison-Wesley, Harlow (1998)

    Google Scholar 

  16. Xu, J., Janowczyk, A., Chandran, S., Madabhushi, A.: A high-throughput active contour scheme for segmentation of histopathological imagery. Med. Image Anal. 15, 851–862 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing, University of Eastern Finland, Kuopio, Finland

    Jouni Pääkkönen, Niina Päivinen & Matti Nykänen

  2. Department of Pathology Medical School, University of Tampere, Tampere, Finland

    Timo Paavonen

  3. Fimlab Laboratories Ltd, Tampere, Finland

    Timo Paavonen

Authors
  1. Jouni Pääkkönen
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  2. Niina Päivinen
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  3. Matti Nykänen
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  4. Timo Paavonen
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Correspondence to Jouni Pääkkönen.

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Pääkkönen, J., Päivinen, N., Nykänen, M. et al. An automated gland segmentation and classification method in prostate biopsies: an image source-independent approach. Machine Vision and Applications 26, 103–113 (2015). https://doi.org/10.1007/s00138-014-0650-1

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  • DOI: https://doi.org/10.1007/s00138-014-0650-1

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