Patch-based sparse representation for bacterial detection

A. K. Eldaly, Yoann Altmann, Ahsan Akram, Antonios Perperidis, Kevin Dhaliwal, Stephen McLaughlin

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

4 Citations (Scopus)
35 Downloads (Pure)


In this paper, we propose an unsupervised approach for bacterial de- tection in optical endomicroscopy images. This approach splits each image into a set of overlapping patches and assumes that observed intensities are linear combinations of the actual intensity values as- sociated with background image structures, corrupted by additive Gaussian noise and potentially by a sparse outlier term modelling anomalies (which are considered to be candidate bacteria). The ac- tual intensity term representing background structures is modelled as a linear combination of a few atoms drawn from a dictionary which is learned from bacteria-free data and then fixed while analyzing new images. The bacteria detection task is formulated as a minimization problem and an alternating direction method of multipliers (ADMM) is then used to estimate the unknown parameters. Simulations con- ducted using two ex vivo lung datasets show good detection and cor- relation performance between bacteria counts identified by a trained clinician and those of the proposed method.
Original languageEnglish
Title of host publication2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, April 8-11, 2019
Number of pages5
ISBN (Electronic)978-1-5386-3641-1
ISBN (Print)978-1-5386-3642-8
Publication statusPublished - 2019


  • Admm
  • Anomaly detection
  • Bacteria detection
  • Optical microscopy
  • Patch-based methods
  • Sparse representation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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