A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images.

Yi-Ming Chen,Shaou-Gang Miaou

doi:10.1155/2017/9580385

Abstract

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.

Highlights

According to WHO [1], anemia is a prevalent health problem affecting an estimate of 2 billion people in many parts of the world, especially where dietary iron deficiency, malaria, and hookworm infections are common
There are a total of 100 images in which 40 of them are labeled as anemia samples and 60 of them are labeled as nonanemia samples according to the threshold set at 11 g/dL [13, 14]
We proposed a combining approach consisting of the modified Kalman filtering and penalty regression for noninvasive anemia detection

Summary

Introduction

According to WHO [1], anemia is a prevalent health problem affecting an estimate of 2 billion people in many parts of the world, especially where dietary iron deficiency, malaria, and hookworm infections are common. Blood test is the most common way for the anemia assessment based on the level of hemoglobin (Hb) concentration. The entire procedure of blood test may take more than an hour, which limits the number of patients that could be examined in a day and makes the large-scale anemia screening difficult. The traditional way of drawing blood for anemia assessment is time consuming and costly and not appropriate for everyone. An efficient way to screen out anemia patients for essentially anyone is desirable

Methods

Results

Conclusion