Fuzzy Clustering and Adaptive Thresholding Based Segmentation Method for Breast Cancer Diagnosis

  • Paweł Filipczuk
  • Marek Kowal
  • Andrzej Obuchowicz
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


The paper provides a preview of some work in progress on the computer system to support breast cancer diagnosis. The approach is based on microscope images of the FNB (Fine Needle Biopsy) and assumes distinguishing malignant from benign cases. Research is focused on two different problems. The first is segmentation and extraction of morphometric parameters of nuclei present on cytological images. The second concentrates on breast cancer classification using selected features. Studies in both areas are conducted in parallel. This work is mainly devoted to the problem of image segmentation in order to obtain good quality features measurements. Correct segmentation is crucial for successful diagnosis. The paper describes hybrid segmentation algorithm based on fuzzy clustering and adaptive thresholding. The automatic system of malignancy classification was applied on a set of medical images with promising results.


Breast Cancer Image Segmentation Segmentation Method Fuzzy Cluster Membership Degree 
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 EngineeringUniversity of Zielona GóraPoland

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