Automated Detection of Uninformative Frames in Pulmonary Optical Endomicroscopy

• Published in: IEEE transactions on biomedical engineering 2017-01, Vol.64 (1), p.87-98
• Main Authors: Perperidis, Antonios, Akram, Ahsan, Altmann, Yoann, McCool, Paul, Westerfeld, Jody, Wilson, David, Dhaliwal, Kevin, McLaughlin, Stephen
• Format: Article
• Language: English
• Subjects: Algorithms; Artefacts; Automation; Data acquisition; Data processing; Distal lung imaging; Endoscopy - methods; Fiber Optic Technology - methods; fibered confocal fluorescent microscopy (FCFM); Frames; frames detection; Humans; Image acquisition; image analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Lung - cytology; Lungs; Microscopy; Microscopy, Confocal - methods; Microscopy, Fluorescence - methods; Noise; optical endomicroscopy (OEM); Optical fiber sensors; Optical fibers; Optical imaging; Organs; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; texture analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2016.2538084

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.