基于数字图像处理和人工智能的睑结膜数字图像血红蛋白水平估算算法

Guillermo Moreno, Abdigal Camargo, Luis Ayala, Mirko Zimic, C. del Carpio
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引用次数: 0

摘要

贫血是影响世界大部分人口的常见问题,尤其是在贫困国家。这项工作旨在为得不到充分服务的人群提供更方便的远程诊断工具。我们的建议包括利用眼睑结膜图像和中档手机捕捉的校准标签,实施估算血红蛋白水平的算法。我们提出了三种算法:一种用于校准标签分割,另一种用于睑结膜分割,最后一种用于根据前一种算法分割的图像估算血红蛋白水平。实验使用了儿童眼睑图像和校准贴纸数据集。使用 SLIC-GAT 模型估计血红蛋白水平的 L1 标准误差为 0.72 g/dL。总之,考虑到图像来源是一台中端商用相机,与现有方法相比,这些分割和回归方法的整合提高了估算精度。所提出的方法具有在低收入农村人口中进行大规模筛查的潜力,因为它是非侵入性的,而且其简便性使受过基本培训的社区卫生工作者也能进行测试。因此,该工具可为防治儿童贫血做出重大贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Algorithm for the Estimation of Hemoglobin Level from Digital Images of Palpebral Conjunctiva Based in Digital Image Processing and Artificial Intelligence
Anemia is a common problem that affects a significant part of the world’s population, especially in impoverished countries. This work aims to improve the accessibility of remote diagnostic tools for underserved populations. Our proposal involves implementing algorithms to estimate hemoglobin levels using images of the eyelid conjunctiva and a calibration label captured with a mid-range cell phone. We propose three algorithms: one for calibration label segmentation, another for palpebral conjunctiva segmentation, and the last one for estimating hemoglobin levels based on the segmented images from the previous algorithms. Experiments were performed using a data set of children’s eyelid images and calibration stickers. An L1 norm error of 0.72 g/dL was achieved using the SLIC-GAT model to estimate the hemoglobin level. In conclusion, the integration of these segmentation and regression methods improved the estimation accuracy compared to current approaches, considering that the source of the images was a mid-range commercial camera. The proposed method has the potential for mass screening in low-income rural populations as it is non-invasive, and its simplicity makes it feasible for community health workers with basic training to perform the test. Therefore, this tool could contribute significantly to efforts aimed at combating childhood anemia.
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