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Information Technologies in Biomedicine, Volume 3 pp 295–306Cite as

A Machine Learning Approach to Identify Prostate Cancer Areas in Complex Histological Images

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 283))

Abstract

Separating benign glands, and cancer areas from stroma is one of the vital steps towards automated grading of prostate cancer in digital images of H&E preparations. In this work we present a novel tool that utilizes a supervised classification of histograms of staining components in hematoxylin and eosin images to delineate areas of benign and cancer glands. Using high resolution images of whole slide prostatectomies we compared several image classification schemes which included intensity histograms, histograms of oriented gradients, and their concatenations to the manual annotations of tissues by a pathologist, and showed that joint intensity histograms of hematoxylin and eosin components performed with the highest accuracy.

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Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90048, USA

    Sadri Salman, Yung-Tien Chu & Arkadiusz Gertych

  2. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, USA

    Zhaoxuan Ma & Beatrice Knudsen

  3. Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, USA

    Sambit Mohanty, Sanica Bhele, Beatrice Knudsen & Arkadiusz Gertych

  4. Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA

    Arkadiusz Gertych

Authors
  1. Sadri Salman
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  2. Zhaoxuan Ma
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  3. Sambit Mohanty
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  4. Sanica Bhele
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  5. Yung-Tien Chu
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  6. Beatrice Knudsen
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  7. Arkadiusz Gertych
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Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Ewa Piętka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

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© 2014 Springer International Publishing Switzerland

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Cite this paper

Salman, S. et al. (2014). A Machine Learning Approach to Identify Prostate Cancer Areas in Complex Histological Images. In: Piętka, E., Kawa, J., Wieclawek, W. (eds) Information Technologies in Biomedicine, Volume 3. Advances in Intelligent Systems and Computing, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-319-06593-9_26

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  • DOI: https://doi.org/10.1007/978-3-319-06593-9_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06592-2

  • Online ISBN: 978-3-319-06593-9

  • eBook Packages: EngineeringEngineering (R0)

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