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Cell-graph mining for breast tissue modeling and classification.

Cagatay Bilgin1, Cigdem Demir, Chandandeep Nagi

  • 1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
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This study introduces hierarchical cell graphs for automated breast cancer diagnosis from histopathology images. This graph-based method achieves 81.8% accuracy, outperforming other image analysis techniques.

Area of Science:

  • Computational pathology
  • Medical image analysis
  • Graph theory applications

Background:

  • Automated cancer diagnosis is crucial for effective treatment.
  • Histopathological image analysis presents challenges in accurate tissue characterization.

Purpose of the Study:

  • To develop and evaluate a novel graph theoretical technique for automated cancer diagnosis in breast tissues.
  • To compare the performance of hierarchical cell graphs against existing methods.

Main Methods:

  • Histopathological images of breast tissues were segmented using the k-means algorithm.
  • Cell-graphs were generated using cell coordinates and matrix components.
  • Quantitative metrics were computed from cell-graphs to serve as features for machine learning.

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Main Results:

  • The hierarchical cell graph approach achieved an accuracy of 81.8%.
  • This significantly outperformed intensity-based features (61.0%), Delaunay triangulation (54.1%), and a previous technique (75.9%).

Conclusions:

  • Hierarchical cell graphs offer a robust and accurate method for automated breast cancer diagnosis.
  • Graph theoretical approaches show significant potential in computational pathology.