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Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation

  • Sulaiman Vesal
  • Nishant Ravikumar
  • Stephan Ellman
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and variation in the intensity distribution. In this paper, we evaluated the performance of three unsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesion segmentation, namely, Gaussian Mixture Model clustering, K-means clustering and a markercontrolled Watershed transformation based method. All methods were applied on breast MRI slices following selection of regions of interest (ROIs) by an expert radiologist and evaluated on 106 subjects’ images, which include 59 malignant and 47 benign lesions. Segmentation accuracy was evaluated by comparing our results with ground truth masks, using the Dice similarity coefficient (DSC), Jaccard index (JI), Hausdorff distance and precision-recall metrics. The results indicate that the marker-controlled Watershed transformation outperformed all other algorithms investigated.

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Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Sulaiman Vesal
    • 1
  • Nishant Ravikumar
    • 1
  • Stephan Ellman
    • 2
  • Andreas Maier
    • 1
  1. 1.Fakultät für Pattern RecognitionFAU Erlangen-NürnbergErlangenDeutschland
  2. 2.Radiologisches InstitutUniversitätsklinikum ErlangenErlangenDeutschland

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