Robust video-frame classification for bronchoscopy

Matthew I. McTaggart, William E. Higgins

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

3 Scopus citations


During bronchoscopy, a physician uses the endobronchial video to help navigate and observe the inner airways of a patient's lungs for lung cancer assessment. After the procedure is completed, the video typically contains a significant number of uninformative frames. A video frame is uninformative when it is too dark, too blurry, or indistinguishable due to a build-up of mucus, blood, or water within the airways. We develop a robust and automatic system, consisting of two distinct approaches, to classify each frame in an endobronchial video sequence as informative or uninformative. Our first approach, referred as the Classifier Approach, focuses on using image-processing techniques and a support vector machine, while our second approach, the Deep-Learning Approach, draws upon a convolutional neural network for video frame classification. Using the Classifier Approach, we achieved an accuracy of 78.8%, a sensitivity of 93.9%, and a specificity of 62.8%. The Deep-Learning Approach, gave slightly improved performance, with an accuracy of 87.3%, a sensitivity of 87.1%, and a specificity of 87.6%.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
ISBN (Electronic)9781510625495
StatePublished - 2019
EventMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, United States
Duration: Feb 17 2019Feb 19 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging
  • Biomaterials


Dive into the research topics of 'Robust video-frame classification for bronchoscopy'. Together they form a unique fingerprint.

Cite this