A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images.
A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images.
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DOI：
10.1155/2017/9580385
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发表时间：
2017
影响因子：
--
通讯作者：
Miaou SG
中科院分区：
文献类型：
--
作者：
Chen YM;Miaou SG
在医学界，非侵入性医疗程序通常比侵入性医疗程序更可取。通过眼睑结膜检查贫血是一种方便的非侵入性检查方法。该过程可以自动进行，以降低医疗成本。提出了一种基于卡尔曼滤波(KF)和回归分析的贫血检测方法。传统的KF经常用于时间相关的应用中。在这里，我们针对医学应用中的时间无关数据对传统的KF进行了改进。我们简单地计算眼睑结膜图像红色分量的平均值作为识别特征，并使用惩罚回归算法找到一条非线性曲线，该曲线最符合特征值和相应的血红蛋白(Hb)浓度的数据。为了评估建议的方法和几种相关的方法，我们提出了一个风险评估方案，其中整个Hb谱被划分为贫血的高风险、低风险和可疑区间。可疑区间包含区分贫血和非贫血的Hb阈值，例如11 g/dL。可疑样本是落在可疑区间内的样本。为了进行贫血筛查，我们希望尽可能少一些可疑样本。实验结果表明，对于本文所考虑的所有方法，改进的KF都显著减少了可疑样本的数量。
Noninvasive medical procedures are usually preferable to their invasive counterparts in the medical community. Anemia examining through the palpebral conjunctiva is a convenient noninvasive procedure. The procedure can be automated to reduce the medical cost. We propose an anemia examining approach by using a Kalman filter (KF) and a regression method. The traditional KF is often used in time-dependent applications. Here, we modified the traditional KF for the time-independent data in medical applications. We simply compute the mean value of the red component of the palpebral conjunctiva image as our recognition feature and use a penalty regression algorithm to find a nonlinear curve that best fits the data of feature values and the corresponding levels of hemoglobin (Hb) concentration. To evaluate the proposed approach and several relevant approaches, we propose a risk evaluation scheme, where the entire Hb spectrum is divided into high-risk, low-risk, and doubtful intervals for anemia. The doubtful interval contains the Hb threshold, say 11 g/dL, separating anemia and nonanemia. A suspect sample is the sample falling in the doubtful interval. For the anemia screening purpose, we would like to have as less suspect samples as possible. The experimental results show that the modified KF reduces the number of suspect samples significantly for all the approaches considered here.
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