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Segmentation of phase contrast microscopy images based on multi-scale local Basic Image Features histograms.

N Jaccard1, N Szita2, L D Griffin1

  • 1Department of Computer Science, University College London, London, UK.

Computer Methods in Biomechanics and Biomedical Engineering. Imaging & Visualization
|August 18, 2017
PubMed
Summary

Automated analysis of phase contrast microscopy (PCM) images is now feasible. A new trainable method effectively segments cells and distinguishes cell types, offering rapid and accurate results for biological research.

Keywords:
Basic Image Featureslocal feature histogramsphase contrast microscopyrandom forestsegmentationtrainable segmentation

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Area of Science:

  • Cell biology
  • Biomedical imaging
  • Computational analysis

Background:

  • Phase contrast microscopy (PCM) is crucial for observing live cells.
  • Manual analysis of PCM images is subjective and time-consuming.
  • Automated analysis is hindered by low contrast and image artifacts.

Purpose of the Study:

  • To develop a trainable, automated method for segmenting and analyzing PCM images.
  • To overcome challenges of low contrast and artifacts in PCM image analysis.
  • To enable accurate cell type discrimination using PCM.

Main Methods:

  • A pixel-wise segmentation approach using multi-scale Basic Image Features local histograms.
  • Classification of image structures and symmetries using random decision trees.
  • Validation for cell/background segmentation and differentiation of two cell types.

Main Results:

  • Achieved performance comparable to state-of-the-art specialized algorithms.
  • Demonstrated the method's general applicability across different segmentation tasks.
  • Exhibited low processing times (<4s for 1280x960 images), suitable for batch and interactive use.

Conclusions:

  • The proposed trainable segmentation method significantly advances automated PCM image analysis.
  • This approach offers a robust and efficient solution for cell segmentation and discrimination.
  • The method's speed and accuracy support its application in diverse biological and biomedical research settings.