Texture descriptors for classifying sparse, irregularly sampled optical endomicroscopy images

Oleksii Leonovych, Mohammad Rami Koujan, Ahsan Akram, Jody Westerfeld, David Wilson, Kevin Dhaliwal, Stephen McLaughlin, Antonios Perperidis*

*Corresponding author for this work

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

2 Citations (Scopus)
46 Downloads (Pure)


Optical endomicroscopy (OEM) is a novel real-time imaging technology that provides endoscopic images at the microscopic level. Clinical OEM procedures generate large datasets making their post procedural analysis a subjective and laborious task. There has been effort to automatically classify OEM frame sequences into relevant classes in aid of a fast and reliable diagnosis. Most existing classification approaches adopt established texture metrics, such as Local Binary Patterns (LBPs) derived from the regularly sampled grid images. However, due to the nature of image transmission through coherent fibre bundles, raw OEM data are sparsely and irregularly sampled, post-processed to a regularly sampled grid image format. This paper adapts Local Binary Patterns, a commonly used image texture descriptor, taking into consideration the sparse, irregular sampling imposed by the imaging fibre bundle on OEM images. The performance of Sparse Irregular Local Binary Patterns (SILBP) is assessed in conjunction with widely used classifiers, including Support Vector Machines, Random Forests and Linear Discriminant Analysis, for the detection of uninformative frames (i.e. noise and motion-artefacts) within pulmonary OEM frame sequences. Uninformative frames can comprise a considerable proportion of a dataset, increasing the resources required to analyse the data and impacting on any automated quantification analysis. SILBPs achieve comparable performance to the optimal LBPs (>92% overall accuracy), while employing <13% of the associated data.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
Subtitle of host publicationMIUA 2018
EditorsMark Nixon, Sasan Mahmoodi, Reyer Zwiggelaar
Number of pages12
ISBN (Electronic)9783319959214
ISBN (Print)9783319959207
Publication statusPublished - 21 Aug 2018
Event22nd Conference on Medical Image Understanding and Analysis 2018 - Southampton, United Kingdom
Duration: 9 Jul 201811 Jul 2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference22nd Conference on Medical Image Understanding and Analysis 2018
Abbreviated titleMIUA 2018
Country/TerritoryUnited Kingdom


  • Frame classification
  • Irregular sampling
  • Local binary patterns
  • Optical endomicroscopy
  • Texture analysis

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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