Automated detection of uninformative frames in pulmonary optical endomicroscopy (OEM)

Antonios Perperidis, Ahsan Akram, Yoann Altmann, Paul McCool, Jody Westerfield, David Wilson, Kevin Dhaliwal, Stephen McLaughlin

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Significance: Optical endomicroscopy (OEM) is a novel real-time imaging technology that provides endoscopic images at a microscopic level. The nature of OEM data, as acquired in clinical use, gives rise to the presence of uninformative frames (i.e. pure-noise and motion-artefacts). Uninformative frames can comprise a considerable proportion (up to >25%) of a dataset, increasing the resources required for analysing the data (both manually and automatically), as well as diluting the results of any automated quantification analysis. Objective: There is therefore a need to automatically detect and remove as many of these uninformative frames as possible while keeping frames with structural information intact. Methods: This paper employs Gray Level Co-occurrence Matrix texture measures and detection theory to identify and remove such frames. The detection of pure-noise frames and motion artefacts is treated as two independent problems. Results: Pulmonary OEM frame sequences of the distal lung are employed for the development and assessment of the approach. The proposed approach identifies and removes uninformative frames with a sensitivity of 93% and a specificity of 92.6%. Conclusion: The detection algorithm is accurate and robust in pulmonary OEM frame sequences. Conditional to appropriate model refinement, the algorithms can become applicable in other organs.
Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
Early online date10 Mar 2016
Publication statusE-pub ahead of print - 10 Mar 2016


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