Machine Vision and Applications

, Volume 23, Issue 4, pp 623–638

Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection

  • Tiago Esteves
  • Pedro Quelhas
  • Ana Maria Mendonça
  • Aurélio Campilho
Special Issue Paper

DOI: 10.1007/s00138-012-0407-7

Cite this article as:
Esteves, T., Quelhas, P., Mendonça, A.M. et al. Machine Vision and Applications (2012) 23: 623. doi:10.1007/s00138-012-0407-7

Abstract

Computational methods used in microscopy cell image analysis have largely augmented the impact of imaging techniques, becoming fundamental for biological research. The understanding of cell regulation processes is very important in biology, and in particular confocal fluorescence imaging plays a relevant role for the in vivo observation of cells. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cells. While the classic approach for automatic cell analysis is to use image segmentation, for in vivo confocal fluorescence microscopy images of plants, such approach is neither trivial nor is it robust to image quality variations. To analyze plant cells in in vivo confocal fluorescence microscopy images with robustness and increased performance, we propose the use of local convergence filters (LCF). These filters are based in gradient convergence and as such can handle illumination variations, noise and low contrast. We apply a range of existing convergence filters for cell nuclei analysis of the Arabidopsis thaliana plant root tip. To further increase contrast invariance, we present an augmentation to local convergence approaches based on image phase information. Through the use of convergence index filters we improved the results for cell nuclei detection and shape estimation when compared with baseline approaches. Using phase congruency information we were able to further increase performance by 11% for nuclei detection accuracy and 4% for shape adaptation. Shape regularization was also applied, but with no significant gain, which indicates shape estimation was good for the applied filters.

Keywords

Cell segmentationShape estimationLocal image filteringConvergence filters

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Tiago Esteves
    • 1
  • Pedro Quelhas
    • 1
  • Ana Maria Mendonça
    • 1
    • 2
  • Aurélio Campilho
    • 1
    • 2
  1. 1.INEB-Instituto de Engenharia BiomédicaOportoPortugal
  2. 2.Departamento de Engenharia Electrotécnica e Computadores, Faculdade de EngenhariaUniversidade do PortoOportoPortugal