A novel bronchoscopic video enhancement and tissue segmentation method based on Eulerian video magnification

A crucial tool for the early diagnosis of serious respiratory illness problems including lung cancer is bronchoscopy. However, the bronchoscopic video acquisition environment is constrained and unevenly illuminated, making it difficult for doctors to precisely identify intact human blood vessels and other tissue features with the naked eye. It has been demonstrated that using Eulerian video magnification techniques significantly enhances subtle changes in the video that are hardly perceptible to the human eye. We propose a novel Eulerian video linear enhancement framework for bronchoscopic video enhancement. Bronchoscopy videos are recorded and processed using Laplacian pyramid decomposition and temporal filtering algorithms to obtain the frequency bands of interest, the magnification factors of the respective bands are obtained by the constructed gain curve equations, and the magnified sub -bands are subjected to correlated noise denoising by blockmatching and 3D filtering methods with exact transform-domain variance, finally we reconstruct the video. A novel framework for vessel recognition segmentation of bronchoscopic videos is proposed, thus realizing an end -to -end processing program to meet the needs of respiratory physicians. The proposed method enables remarkable enhance tissue features in bronchoscopic video and has a good effect of suppressing noise and artifacts generated during the enhancement. Tiny vessels that were previously challenging to see using the naked eye could currently be observed, and the pulsing signal of blood vessels is significantly enhanced. The experimental results demonstrate that our method is superior to the earlier method in terms of visual quality, quantitative evaluation and subjective evaluation.