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Related Experiment Video

Updated: Feb 27, 2026

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment
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Imprint cytology-based breast malignancy screening: an efficient nuclei segmentation technique.

M Saha1, I Arun2, S Agarwal2

  • 1School of Medical Science & Technology, Indian Institute of Technology, Kharagpur, India.

Journal of Microscopy
|June 28, 2017
PubMed
Summary

This study introduces an automated method using Lagrange interpolation and superpixels for breast cancer screening via imprint cytology. The developed tool achieved 98% segmentation and 99% classification accuracy for nuclei analysis.

Keywords:
Breast cancerLagrange's interpolationimprint cytologyrandom forestsuperpixels

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Biomedical Engineering

Background:

  • Imprint cytology (IC) is a rapid, affordable method for breast malignancy screening.
  • Pathologists examine H&E stained nuclei shape variations for diagnosis.
  • Accurate segmentation of overlapping nuclei is crucial for reliable analysis.

Purpose of the Study:

  • To develop an automated and efficient segmentation algorithm for breast cell nuclei.
  • To integrate Lagrange's interpolation and superpixels for delineating overlapped nuclei.
  • To design a computer-assisted tool for breast cancer (BC) screening using IC.

Main Methods:

  • Image preprocessing using gamma correction.
  • Single nuclei segmentation via histogram thresholding and morphological operations.
  • Overlapping nuclei segmentation using concave point detection, Lagrange's interpolation, and superpixels.

Main Results:

  • Extracted 16 significant nuclear shape and texture features (p < 0.05).
  • Achieved 98% segmentation accuracy and 99% classification accuracy on 120 IC images (approx. 12,000 nuclei).
  • Performance metrics: Jaccard index (94%), correlation coefficient (95%), Dice coefficient (97%), Hausdorff distance (43%).

Conclusions:

  • The proposed methodology offers an accurate and efficient automated approach for breast cancer screening.
  • The computer-assisted tool can aid pathologists in confirmatory diagnosis.
  • The approach enhances understanding of malignant nuclei morphology.