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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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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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