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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 115–123Cite as

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Structure and Context in Prostatic Gland Segmentation and Classification

Structure and Context in Prostatic Gland Segmentation and Classification

  • Kien Nguyen19,
  • Anindya Sarkar20 &
  • Anil K. Jain19 
  • Conference paper
  • 5987 Accesses

  • 33 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

A novel gland segmentation and classification scheme applied to an H&E histology image of the prostate tissue is proposed. For gland segmentation, we associate appropriate nuclei objects with each lumen object to create a gland segment. We further extract 22 features to describe the structural information and contextual information for each segment. These features are used to classify a gland segment into one of the three classes: artifact, normal gland and cancer gland. On a dataset of 48 images at 5× magnification (which includes 525 artifacts, 931 normal glands and 1,375 cancer glands), we achieved the following classification accuracies: 93% for artifacts v. true glands; 79% for normal v. cancer glands, and 77% for discriminating all three classes. The proposed method outperforms state of the art methods in terms of segmentation and classification accuracies and computational efficiency.

Keywords

  • Contextual Information
  • Segmentation Result
  • Jaccard Index
  • Median Absolute Deviation
  • Prostate Cancer Detection

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

www.cse.msu.edu/~nguye231/GlandSegClass.html

Description: The code for gland segmentation and feature extraction, the details of the experiments (PPMM-SF method, PPMM-SCF method, three methods to estimate p(y|x) mentioned in the paper)

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References

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Author information

Authors and Affiliations

  1. Michigan State Unversity, East Lansing, MI, 48824, USA

    Kien Nguyen & Anil K. Jain

  2. Ventana Medical Systems, Inc., Sunnyvale, CA, 94085, USA

    Anindya Sarkar

Authors
  1. Kien Nguyen
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  2. Anindya Sarkar
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  3. Anil K. Jain
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Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Project Team Asclepios, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139,, Cambridge,, MA, USA

    Polina Golland

  3. Information and Communication, Nagoya University, 464-8603, Headquarters, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Nguyen, K., Sarkar, A., Jain, A.K. (2012). Structure and Context in Prostatic Gland Segmentation and Classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-33415-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33414-6

  • Online ISBN: 978-3-642-33415-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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