Cytoplasm segmentation on cervical cell images using graph cut-based approach

Biomed Mater Eng. 2014;24(1):1125-31. doi: 10.3233/BME-130912.

Abstract

This paper proposes a method to segment the cytoplasm in cervical cell images using graph cut-based algorithm. First, the A* channel in CIE LAB color space is extracted for contrast enhancement. Then, in order to effectively extract cytoplasm boundaries when image histograms present non-bimodal distribution, Otsu multiple thresholding is performed on the contrast enhanced image to generate initial segments, based on which the segments are refined by the multi-way graph cut method. We use 21 cervical cell images with non-ideal imaging condition to evaluate cytoplasm segmentation performance. The proposed method achieved a 93% accuracy which outperformed state-of-the-art works.

Keywords: A* channel; Cervical cell; cytoplasm segmentation; graph cut.

MeSH terms

  • Algorithms
  • Automation
  • Cell Nucleus / metabolism
  • Cervix Uteri / cytology
  • Cervix Uteri / pathology*
  • Contrast Media / chemistry
  • Cytoplasm / metabolism*
  • Cytoplasm / pathology
  • Female
  • Humans
  • Image Enhancement
  • Image Processing, Computer-Assisted
  • Programming Languages
  • Software
  • Uterine Cervical Neoplasms / diagnosis*
  • Uterine Cervical Neoplasms / pathology

Substances

  • Contrast Media