Defocus-aware Dirichlet particle filter for stable endoscopic video frame recognition

A computer-aided system for colorectal endoscopy could provide endoscopists with important helpful diagnostic support during examinations. A straightforward means of providing an objective diagnosis in real time might be for using classifiers to identify individual parts of every endoscopic video frame, but the results could be highly unstable due to out-of-focus frames. To address this problem, we propose a defocus-aware Dirichlet particle filter (D-DPF) that combines a particle filter with a Dirichlet distribution and defocus information.

We develop a particle filter with a Dirichlet distribution that represents the state transition and likelihood of each video frame. We also incorporate additional defocus information by using isolated pixel ratios to sample from a Rayleigh distribution.

We tested the performance of the proposed method using synthetic and real endoscopic videos with a frame-wise classifier trained on 1671 images of colorectal endoscopy. Two synthetic videos comprising 600 frames were used for comparisons with a Kalman filter and D-DPF without defocus information, and D-DPF was shown to be more robust against the instability of frame-wise classification results. Computation time was approximately 88ms/frame, which is sufficient for real-time applications. We applied our method to 33 endoscopic videos and showed that the proposed method can effectively smoothen highly unstable probability curves under actual defocus of the endoscopic videos.

The proposed D-DPF is a useful tool for smoothing unstable results of frame-wise classification of endoscopic videos to support real-time diagnosis during endoscopic examinations.