使用时空相互依赖和纹理线索的盲腹腔镜视频质量评估器

Sria Biswas, Rohini Palanisamy
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引用次数: 0

摘要

腹腔镜视频的质量评估是确保准确诊断和手术精度的关键。传统的质量评估方法通常单独关注空间或纹理特征,限制了它们在处理运动模糊、噪声、散焦模糊、光照不均匀和烟雾等复合失真方面的有效性。为了解决这个问题,利用时空相互依赖性和纹理特征提供了一种更全面的方法来复制人类视觉系统,以提高视频质量评估的鲁棒性。本文介绍了盲腹腔镜视频质量评估器(BLVQE),该工具对空间、时间和纹理特征之间的统计相关性进行建模。为此,使用从公共数据库获取的腹腔镜视频来估计亮度和运动矢量图,然后使用二元广义高斯分布对其进行分析,以捕获时空相互依赖性。使用统计能量度量进一步量化场景纹理复杂性。这些特征向量用于LSTM框架的端到端训练,用于帧质量预测。模型的训练和验证损失曲线在50次左右达到饱和,表明预测能力较强。与其他最先进的方法相比,BLVQE预测显示出与主观得分的高度相关性,表现出稳健和有竞争力的表现。消融研究强调了单个特征元素的贡献,证实了所选特征的优越性。这些发现增强了对影响视频质量的空间、时间和纹理变化的理解,并强调了在准确估计腹腔镜视频诊断质量方面联合依赖的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BLVQE: Blind Laparoscopic Video Quality Evaluator using spatio-temporal interdependency and textural cues
Quality assessment of laparoscopic videos is critical for ensuring accurate diagnostics and surgical precision. Traditional quality assessment methods typically focus on either spatial or textural features independently, limiting their effectiveness in handling composite distortions like motion blur, noise, defocus blur, uneven illumination, and smoke. To address this, leveraging spatio-temporal interdependencies and textural features offers a more comprehensive approach in replicating the human visual system to improve the robustness of video quality assessment. This work introduces Blind Laparoscopic Video Quality Evaluator (BLVQE) that models the statistical interdependencies between spatial, temporal and texture features. For this, laparoscopic videos obtained from a public database are used to estimate the Luminance and motion vector maps, which are then analyzed using bivariate generalized Gaussian distribution to capture spatio-temporal interdependency. Scene texture complexity is further quantified using statistical energy measures. These feature vectors are used for end-to-end training of an LSTM framework for frame quality predictions. The training and validation loss curves of the model saturate around 50 epochs, indicating prediction proficiency. BLVQE predictions show a high correlation with subjective scores exhibiting robust and competitive performance against other state-of-the-art methods. Ablation studies highlight the contribution of individual feature elements, confirming the superiority of the selected features. These findings enhance the understanding of the spatial, temporal and textural variations that influence video quality and highlight the potential of joint dependencies in accurately estimating the diagnostic quality of laparoscopic videos.
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来源期刊
Biomedical engineering advances
Biomedical engineering advances Bioengineering, Biomedical Engineering
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