Evaluation of color space transformations in separating anemia vs control subjects Open Access

Shan, Chenlu (Spring 2021)

Permanent URL: https://etd.library.emory.edu/concern/etds/b2773w843?locale=en


Anemia detection requires invasive blood testing with the gold standard hemoglobin level by a complete blood count (CBC). This procedure involves phlebotomy, which could lead to certain adverse effects. We consider a recently proposed non-invasive method for monitoring anemia with a fingernail image taken from patients [1]. Non-invasive anemia detection involves procedure of extracting color data from patients’ fingernail bed smartphone images. Usually, color data are extracted in RGB (Red,Green,Blue) color space. Since it is unknown that which color space transformation is most useful in discriminating anemia population vs healthy population, our work focused on determining which transformation of RGB (Red,Green,Blue) data would produce the best separation among anemic and control subjects. Several metrics are used to access the separation between anemia and healthy groups based on within sum of squares and between sum of squares Metric 1:Ratio of within to between variation). It is shown that HSI (Hue,Saturation,Intensity) color space perform the best in comparison to all other color spaces (Metric 1: 1.02 (0.82-1.35) for HSI, Metric 1: 0.76 (0.56-1.05) for RGB). The HSI color space also produced the least mean squared errors in predicting anemia status using Lasso Regression method - (MSE for HSI: 0.085).

We conclude the HSI color space has the best discriminative power in separating anemia vs control and the future prediction models or clustering models should focus on the analysis with HSI color space. 

Table of Contents

1 Introduction 1

2 Methodology 4

2.1 Data Sources 4

2.2.1 Univariate Case 5

2.2.2 Multivariate Case 6

2.2.3 Color Space Transformation 9

2.2.4 Statistical Analysis 11

3 Results 15

4 Discussion 21

Reference 22

Appendix 25

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