Adaptive H-extrema for automatic immunogold particle detection

Guillaume Thibault, Kristiina Iljin, Christopher Arthur, Izhak Shafran, Joe Gray

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Quantifying concentrations of target molecules near cellular structures, within cells or tissues, requires identifying the gold particles in immunogold labelled images. In this paper, we address the problem of automatically detect them accurately and reliably across multiple scales and in noisy conditions. For this purpose, we introduce a new contrast filter, based on an adaptive version of the H-extrema algorithm. The filtered images are simplified with a geodesic reconstruction to precisely segment the candidates. Once the images are segmented, we extract classical features and then classify using the majority vote of multiple classifiers. We characterize our algorithm on a pilot data and present results that demonstrate its effectiveness.

Original languageEnglish (US)
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings
Number of pages8
EditionPART 2
StatePublished - 2013
Event18th Iberoamerican Congress on Pattern Recognition, CIARP 2013 - Havana, Cuba
Duration: Nov 20 2013Nov 23 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8259 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other18th Iberoamerican Congress on Pattern Recognition, CIARP 2013


  • Adaptive H-extrema
  • Immunogold particle detection
  • Mathematical morphology
  • Pattern recognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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