Perceptually Lossless Surgical Telementoring System Based on Non-Parametric Segmentation
Bandwidth constraint is one of the significant concerns of surgical telementoring, especially in rural areas. HighEfficiency Video Coding (H.265/HEVC) based video compression techniques have shown promising results for telementoring applications. However, there is a tradeoff between the quality of video received by the remote surgeon and the bandwidth resources required for video transmission. In order to efficiently compress and transmit real-time surgical videos, a hybrid lossless-lossy approach is proposed where surgical incision region (location of surgery) is coded in high quality while the background (non-incision) region is coded in medium to low quality depending on the nature of the region. The surgical incision region is detected based on an efficient color and location-based non-parametric segmentation approach. This approach takes explicitly into account the physiological nature of the human visual system and efficiently encodes the video by providing good overall visual impact in the location of surgery. The results of the proposed approach are shown in terms of video quality metrics such as Bjontegaard delta bitrate (BD-BR), Bjontegaard delta peak signal-to-noise ratio (BD-PSNR), and structural similarity index measurement (SSIM). Experimental results showed that in comparison with default full-frame HEVC encoding, the proposed surgical incision region based encoding achieved an average BD-BR reduction of 77.5% at high-quality settings (QP in range of 0 to 20 in surgical incision region and an increasing QP in skin and background region). The average gain in BD-PSNR of the proposed algorithm was 6.99 dB in surgical incision region at high-quality setting, and the average SSIM index came out to be 0.9926 which is only 0.006% less than the default full-frame HEVC coding. Based on these results, the proposed encoding algorithm can be considered as an efficient and effective solution for surgical telementoring systems for limited bandwidth networks.
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Document Type: Research Article
Publication date: March 1, 2019
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- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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