Multi-class classification of pulmonary endomicroscopic images

Mohammad Rami Koujan, Ahsan Akram, Paul McCool, Jody Westerfeld, David Wilson, Kevin Dhaliwal, Stephen McLaughlin, Antonios Perperidis

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

3 Citations (Scopus)

Abstract

Optical endomicroscopy (OEM) is an emerging medical imaging tool capable of providing in-vivo, in-situ optical biopsies. Clinical pulmonary OEM procedures generate data containing a multitude of frames, making their manual analysis a highly subjective and laborious task. It is therefore essential to automatically classify the images into clinically relevant classes to aid reaching a fast and reliable diagnosis. This paper proposes a methodology to automatically classify OEM images of the distal lung. Due to their diagnostic relevance, three classification tasks are targeted: (i) differentiating between alveolar images containing predominantly elastin from those flooded with cells, (ii) separating normal from abnormal elastin frames, and (iii) multi-class classification amongst normal, abnormal, and cell frames. Local Binary Patterns along with a Support Vector Machine classifier, and One-Versus-All Error Correcting Output Codes strategy for the multi-class classification case, are employed obtaining accuracy of 92.2%, 95.2%, 90.1% for the tasks (i), (ii), (iii), respectively.

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI)
PublisherIEEE
Pages1574-1577
Number of pages4
ISBN (Electronic)9781538636367
DOIs
Publication statusPublished - 24 May 2018

Publication series

NameInternational Symposium on Biomedical Imaging
PublisherIEEE
ISSN (Electronic)1945-8452

Keywords

  • Distal lung
  • Frame classification
  • Optical endomicroscopy
  • Texture analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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