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.
- Optical Endomicroscopy (OEM)
- Fibered Confocal Fluorescent Microscopy (FCFM)
- distal lung imaging
- image analysis
- texture analysis
- frames detection
Perperidis, A., Akram, A., Altmann, Y., McCool, P., Westerfeld, J., Wilson, D., Dhaliwal, K., & McLaughlin, S. (2017). Automated detection of uninformative frames in pulmonary optical endomicroscopy (OEM). IEEE Transactions on Biomedical Engineering, 64(1), 87-98. https://doi.org/10.1109/TBME.2016.2538084