Automatic Breast Cancer Diagnosis Based on K-Means Clustering and Adaptive Thresholding Hybrid Segmentation

  • Paweł Filipczuk
  • Marek Kowal
  • Andrzej Obuchowicz
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 102)


The paper presents k-means based hybrid segmentation method for breast cancer diagnosis problem. It is part of the computer system to support diagnosis based on microscope images of the fine needle biopsy. The system assumes distinguishing malignant from benign cases. Described method is an alternative to the previously presented algorithms based on fuzzy c-means clustering and competitive neural networks. However, it uses similar idea of combining clustering in RGB space with adaptive thresholding. At first, thresholding reveals objects on background. Then image is clustered with k-means algorithm to distinguish nuclei from red blood cells and other objects. Correct segmentation is crucial to obtain good quality features measurements and consequently successful diagnosis. The system of malignancy classification was tested on a set of real case medical images with promising results.


Breast Cancer Image Segmentation Breast Cancer Diagnosis Fine Needle Biopsy Sequential Forward Selection 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paweł Filipczuk
    • 1
  • Marek Kowal
    • 1
  • Andrzej Obuchowicz
    • 1
  1. 1.Institute of Control and Computation EngineeringUniveristy of Zielona GóraPoland

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