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 analyzing 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 Cooccurrence Matrix texture measures and detection theory to identify and remove such frames. The detection of pure-noise and motion-artefacts frames 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.
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