Algorithms for Automatic Recognition of Non-informative Frames in Video Recordings of Bronchoscopic Procedures
The video recordings of endoscopic procedures performed within respiratory tract include both frames of adequate and inadequate quality for the assessment by the endoscopist. The frames of inadequate quality were called by some authors blurred or “non-informative”. The fraction of blurred frames within video recording of bronchofiberoscopy may be considerable and it varies from case to case. Therefore, the function of automatic exclusion of “non-informative” frames would bring substantial benefits in terms of the volume of the archived video recordings of bronchofiberoscopic procedures. Furthermore, it could also save the time of users accessing medical video library established with archived resources. In this paper, the authors have proposed, tested and compared several algorithms for detecting blurred video frames. The main focus of this paper is to compare various, independent algorithms for automatic recognition of “non-informative” frames in video recordings of bronchoscopic procedures. The results demonstrated in the paper show that the proposed methods achieve F-measure, sensitivity, specificity and accuracy of at least 87% or higher.
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