# Intelligent Method for Dipstick Urinalysis Using Smartphone Camera

R. Hari Ginardi, Ahmad Saikhu, Riyanarto Sarno, Dwi Sunaryono, Ali Sofyan Kholimi, Ratna Tiara Shanty

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**To cite this version:**

R. Hari Ginardi, Ahmad Saikhu, Riyanarto Sarno, Dwi Sunaryono, Ali Sofyan Kholimi, et al., Intelligent Method for Dipstick Urinalysis Using Smartphone Camera. 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia), Apr 2014, Bali, Indonesia. pp.66-77, 10.1007/978-3-642-55032-4_7. hal-01397146

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Distributed under a Creative Commons Attribution 4.0 International License
Intelligent Method for Dipstick Urinalysis Using
Smartphone Camera

R V Hari Ginardi, Ahmad Saikhu, Riyanarto Sarno, Dwi Sunaryono, Ali Sofyan Kho-
limi, Ratna Nur Tiara Shanty

Department of Informatics, Institut Teknologi Sepuluh Nopember (ITS) Surabaya

hari@its.ac.id

**Abstract.** This paper introduces an intelligent method for helping people to maintain their healthy by doing a self urinalysis utilising a smartphone camera. A color sensing method using a smartphone camera is designed to determine the value of a reagent strip in a urinalysis dipstick. In the dipstick urinalysis, a color change in each reagent strip is examined. This color change is a result of the reaction of dipstick to the chemical contents of urine including pH, Protein, Glucose, Ketones, Leucocyte, Nitrite, Bilirubine, and Urobilinogen. Performing dis stick urinalysis can be done in almost any places even on the very remote area where medical laboratory cannot be found, and it is much easier and cheaper than medical lab visit.

The proposed intelligent
method includes a framework
for color acquisition using a
smartphone camera which

covers the color management
system, color correction,
color matching, and
quantification. The usage of
RGB and CIELAB color
space is discussed in the color
management part. An
automated color constancy
approach is introduced to
provide a better color
correction, and a step-wise
linear interpolation is
introduced to better estimate
the urinalysis value.

To implement this
proposed method, a closed
acquisition box does not
required. Disptick capturing
can be done directly with a
smartphone camera in almost
any normal lighting
condition.

**Keywords:** color sensing, disstick urinalysis, color constancy,

1 Introduction

Many methods have been applied to
reduce or control the blood sugar for
diabetes mellitus (DM) patients. A
periodic postprandial glucose test and
fasting plasma glucose test are among
the most commonly practiced. Those
require the patient to visit a medical
laboratory. The test itself requires a
blood sample from patient which is taken
using a needle. Besides these blood
glucose tests, an examination of the
concentration of glucose in patient urine
is performed through a method called
urinalysis.

Urinalysis has been largely used as a
method to determine the presence of
chemical composition in urine.
Currently, urinalysis can be performed
using urine dipstick reagents. It can be
done by placing the dipstick into a col-
lected sample of urine and visually com-
paring the reactive color of each reagent
with the dipstick color chart based on
their color similarities. This manual in-
terpretation has its limitation, including
differences in a perception of color, dif-
ferences in the lighting condition [1] and
limitation on categorical or semi-
quantitative assessment only [2].

Dipstick reader is a device used by
most laboratories to interpret the dipstick
results automatically. This device can
minimize errors caused by lighting con-
dition and differences in color percep-
tion, significantly [3]. However, its price
is expensive for individual treatment.
Considering the possibility of most
smartphones to capture color image
using its digital camera, a smartphone
could be an alternative device to interpret urine dipstick results.

Automatic dipstick interpretation us-
ing a smartphone camera requires a
method for color acquisition and a me-
thod of color similarity measurement to
determine the corresponding chemical
content concentration. Incosistencies
may occur in lighting condition, focus,
and angle of view [4]. A color constancy

technique is used to approximate the
original color of the captured image
[5],[6]. It enables consistent color
acquisition.

In this research, a framework for in-
telligent dipstick color acquisition using
a smartphone is proposed. We analyse
the color constancy measurement for
image acquisition using a smartphone
camera in a daily lighting condition
without a closed box, and choose the
optimal color constancy method to
provide the color correction. An ap-
proach for smoother color quantification
is done by introducing a stepwise linear
interpolation [7]. It gives a better approx-
imation to determine the reagent score of
urine substances with quantitative as-
sessment such as glucose.

2 Related Works

2.1 Color Space

A color space is a mathematical model
to specify and visualize colors. Several
color spaces have been defined namely
RGB, CIEXYZ, CIELAB, HSV, and
CMYK.

Color perception by human visual is
defined by three basic attributes. Those
are hue, saturation, and brightness [8].
Hue defines the differences in specific
tone of color; such as red, green blue, or
yellow; brightness defines the level of
lightness or darkness of a color, while
saturation describes the purity of a color.

RGB is a standard color space used by
electronic imaging devices such as moni-
tor, camera, and scanner [9]. It consists
of red, green, and blue components
which are represented in a three-
dimensional cube. Each component has a
value ranged from 0 to 255 [10]. RGB
does not represent how the human visual interprets colors [11].

CIELAB is a color space defined by CIE (Comission Internationale de l'Ec-lairage/International Commission on Illumination) in 1976. It is used in most of digital image processing, because it describes the representation of human visual interprets the color.

## 2.2 Color Constancy

Digital color images which are taken by using a digital camera, is highly dependent on three things, they are illuminants, sensor sensitivity, and reflectance. It is represented by the following Equation 1.

$$fc(x) = m(x) \int_{\omega} I(\lambda) \rho_{c}(\lambda) S(x, \lambda) d\lambda, \quad (1)$$

where $f_c$ is the captured color, $I$ is illuminant, $\lambda$ is wavelength, $\rho_c$ is camera sensitivity, and $S$ is the ability for reflecting lightness from the object,

Assuming the sensitivity $\rho_c$ and the wavelength $\lambda$ in different types of camera are constant, those variables can be neglected, therefore the differences in color acquisition will be highly dependent on the varieties of illumination.

Color constancy is a process to restore an image into a certain illumination value even if it is captured in different illumination conditions [6]. Color constancy plays significant roles when color recognition or quantification is required in an image acquisition with varieties of illumination such as colorimetric analysis[12][13][14], and it is also applied in dipstick urine analysis.

In general, color constancy process is begun with an evaluation of lighting source color and its intensity to determine the required color correction. The result is an image in which its color is corrected as it was captured with a white source color.

White Patch Retinex is one of the color constancy method based on the amount of illumination. It is done by using white color as a ground truth. This color is used to adjust the level of illumination [6]. It ignores the differences on reflectance value of the object. Other methods are uniform, non-uniform, and learning method. Uniform method is capable for image enhancement purpose.

Based on its complexity, Gijsenij[6] divided color constancy algorithms into three methods, namely statistical method, gamut method, and machine learning. Statistical method has a lower computing complexity, but it is less accurate in correcting image with high variance such as real photos.

Another method, Grey-World, is a uniform [11] and statistical method. This method failed to correct an image with one dominant color such as leaf color image [11]. Gerson [15] improves the Grey-World by applying a segmentation first. An illumination correction is performed differently for each segment.

Grey World and White Patch uses low level of RGB values of an image. Van de Weijer, et all [16] proposed the usage of high level RGB which is derived from low level RGB Worlds, which is then named Grey Edge algorithm. However, Grey Edge is not optimal for images with one dominant color.
## 2.3 Color Distance Measurement

Color similarity can be done by measuring the distance between points on a geometry space. Several techniques to calculate distances on a geometry space are Euclidean distance, angle vector distance, Canberra distance, and Minkowski. However, Canberra distance and Minkowski distance perform poorly in color similarity for an image retrieval research [17].

Vector angle distance and Euclidean distance are mostly used for measuring color distance. Hue and saturation differences can be quantified well using Vector angle distance. While Euclidean distance quantifies well illumination intensity. Since Euclidean distance has the lower computational complexity, we prefer this technique as a first step to determine the color similarity.

# 3 Methodology

## 3.1 Proposed System

Our proposed system is shown in Fig.1. A test dipstick is captured by using a smartphone camera. Due to the lighting condition and the characteristic of the camera sensor, the color in the produced digital image might be different with the original color of tested dipstick. Therefore, color constancy measurement is needed to approximate its original color and perform an automated color correction. For each image capture, the whole dipstick is captured, containing a series of ten or more reagent color. To separate each reagent color into an individual image file, a cropping process is required. Those reagent color can be organized in a RGB color space or CIELAB color space.

Quantification is done by matching the tested color with its corresponding color chart based on their color similarity and retrieve the related chemical value. For qualitative or semi-quantitative urine analysis, a color similarity approach is adequate, however a further analysis is required for an absolute value determination. Concerning the stepped value in the color chart, a stepwise linear interpolation method is proposed. This interpolation method can estimate a more precise measurement value.

The result from above color quantification is used by the health monitoring system application. A prototype of this application has been built in an Android platform.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Process Flow of The Proposed System</figcaption></figure>

## 3.2 Color Constancy

The proper color constancy method is obtained by evaluating the existing methods. Evaluation is done by measuring the distribution of colors on each reagent in a specific color space.
A statistical range of color distribution in a reagent image is chosen to evaluate how the result is spread or fold-up. Range is used rather than variance since it provides a wider result [18] and it is more sensitive to outlier.

White Patch Color Constancy method is done by getting the maximum value of color from each color component (R, G, and B) as described in Equation 2.

$$K_{max} = \max(\max(R), \max(G), \max(B)) \quad (2)$$

After obtaining the maximum value, the ratio of each color component is calculated, as described in Equation 3.

$$R_c = \frac{\max(C)}{K_{max}}, \quad (3)$$

where C is color of R,G,B color space.

After getting the ratio, the value range of each color component will be widened according to the ratio as described by Equation 4, in order to get the original color.

$$I(X) = R_c \times F_c(X) \quad (4)$$

As an improvement method from White-Patch, Grey-World [19] has a similar approach. Firstly it calculates statistical mean of each color element. An inversion is required as shown in Equation 5, where C is a color from RGB color space.

$$K(C) = \frac{1}{\text{avg}(\text{avg}(C))} \quad (5)$$

The following Equation 6 shows the calculation of maximum value K from K(C).

$$K_{max} = \max(K(R), K(G), K(B)) \quad (6)$$

The result of Kmax is used to find a ratio for scaling factor as given in Equation 7. This Equation is actually an inverse of Equation 3.

$$R_c = \frac{K_{max}}{\max(C)} \quad (7)$$

Furthermore, as in White-Patch, this ratio value Rc is used in Equation 4. The pattern similarity between White-Patch and Grey-World shows that both methods are equivalent. They are originating from the same equation as shown in Equation 8, where p is equal to 1 for Grey-World and p is equal to $\infty$ for White-Patch.

$$I(X) = \sum (F_c(X))^p \frac{1}{p} \quad (8)$$

## 3.3 Quantitative Assessment

By its nature, reagents in dipstick urinalysis provides result in a stepped values. Glucose reagent for example, has a specific color for value 0, 2.8, 5.5, 14, 28 and $>= 55$.

Comparing the color in the tested dipstick to the series of color in the dipstick color chart and finding its similarity will select one of the closest color as the quantification result. This
method is used in most dipstick reader device.

To improve the accuracy of dipstick color interpretation, a curve fitting approach is considered. This curve is used to estimate the value if the tested color lies between two reference colors in the color chart as shown in the following Fig. 2.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Dipstick Color Interpretation</figcaption></figure>

## 3.4 Stepwise Linear Interpolation

A stepwise linear interpolation is used to estimate the value between two given values. This interpolation is applied for each pair of neighboring color in the color chart. The distance between two colors in the color chart and the distance between a tested color and a color chart is evaluated in a cartesian coordinate system with three-elements of color space are used as the coordinate system. From the previous research[7], a CIELAB color space is chosen, therefore its color component L, a, b are mapped into a cartesian coordinate system. X, Y axis defines the level of a reagent, and the value of color component, respectively. Assessing the score of the tested reagent is described as follows:

1. Using euclidean distance, calculate the distance between two adjacent reference data, and the distance between a test data and each reference data using Equation 9, as shown in Fig. 3.

$$d = \sqrt{(\Delta L)^2 + (\Delta a)^2 + (\Delta b)^2}, \quad (9)$$

where $\Delta L = L_{test} - L_{ref}$, $\Delta a = a_{test} - a_{ref}$, $\Delta b = b_{test} - b_{ref}$

2. Find a node $n$ in reference data that has the shortest distance to test data. The value of $n$ represents the location of the selected data.

a. If $n-1$ is equal to 0, then the node is located on the first level. Assign the node number $n$ as node "B" and the node number $n+1$ as node "C". Go to step 3.

b. If $n+1$ is more than the numbers of reference data, then the node located is on the last level. Assign the node number $n$ as node "C" and the node number $n-1$ as node "B". Go to step 3.

c. Otherwise, this node is between two nodes. Continue to step 4.

3. Check if the distance between test data to node "B" is longer than the distance between node "B" and node "C", OR the distance between test data and node "C" is longer than the distance between node "B" and node "C", this test data is beyond the color chart range. Skip the next step and mark the test data as an error.

4. Calculate the distance of test data to node $n-1$ and the distance of test data to $n+1$. Find the shortest one and note the node.

5. If the resulted node is $n-1$, check whether the distance between test data and node $n-1$ is smaller than the distance between node $n-1$ and $n$. If it is true, denote $n-1$ as "B" and denote $n$ as "C".

6. If the resulted node is $n+1$ check whether the distance between test data to node $n+1$ is smaller than the distance between node $n+1$ and $n$. If it is true, denote $n+1$ as "B" and denote $n$ as "C".
data and node *n+1* is smaller than the distance between node *n+1* and *n*. If it is true, denote *n+1* as “C” and denote *n* as “B”.

7. If both of two conditions on step 5 and 6 above are not fulfilled, this test data is beyond the color chart range. Skip the next step and mark the test data as an error.

8. As shown in Fig. 4, the relationship between test data, B, and C is evaluated using trigonometry model to project the current test node position into its relative position between node B and C. Equation 6 is used for this evaluation model.

9. The tested reagent score can be assessed using Equation 11.

$$Cx = \frac{AB^2 - BC^2 - AC^2}{-2BC} \qquad (10)$$

$$\left( \frac{Bx}{Bc} \times (\text{ScoreofC} - \text{ScoreofB}) \right) + \text{ScoreofB} \qquad (11)$$

<figure><img src="image_5.png" /><figcaption>Fig. 3. Test Data and Reference Data Distances Calculation</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. The Relationship between Test Data, B, and C</figcaption></figure>

# 4 Results and Analysis

## 4.1 Color Constancy

As shown in Table II, White Patch technique able to reduce the range of inconsistency up to 63% of red component, 88% of green component, and 59% of blue component.

<figure><img src="image_7.png" /><figcaption>Fig. 5. Color Constancy using White Patch</figcaption></figure>

<table><caption>TABLE II. COLOR CONSTANCY RESULT USING WHITE PATCH</caption><thead><tr><th></th><th>R</th><th>G</th><th>B</th></tr></thead><tbody><tr><td>Original</td><td>51</td><td>51</td><td>49</td></tr><tr><td>Grey World</td><td>50</td><td>52</td><td>53</td></tr><tr><td>White Patch*</td><td>19*</td><td>6*</td><td>20*</td></tr><tr><td>Shades of Grey</td><td>51</td><td>52</td><td>50</td></tr><tr><td>Grey Edge</td><td>98</td><td>99</td><td>39</td></tr><tr><td>Gamut Mapping X-Derivation</td><td>93</td><td>105</td><td>46</td></tr><tr><td>Gamut Mapping Y-Derivation</td><td>86</td><td>113</td><td>45</td></tr><tr><td>Gamut Mapping 1st Order</td><td>104</td><td>111</td><td>40</td></tr></tbody></table>

## 4.2 Color Quantification Result for Glucose Reagent

A glucose estimation is chosen for color interpretation using our approach.
A series of test data and reference data considered as the real color from reagent is prepared in Table III. The reagent score assessment in ten samples is shown in Table IV. We provide quantitative assessment resulted from our approach, assessment by dipstick reader, and interpretation by human visual. The interpretation results produced by dipstick reader and human visual are in categorical values only, therefore the results of quantitative assessment cannot be compared exactly. Furthermore, we provide qualitative assessment to be evaluated.

Outlier data might occur from wrongly captured reagent or out-of-range colors as shown in Table V, and it should be detected. We evaluate the assessment using our approach by comparing with the assessment using Euclidean distance and human visual. The system is able to determine the score of glucose for the first type of real data and recognise the rest as unwanted or outlier colors.

### 4.3 Reagent Score Assessment Using Proposed Approach

In this section, a test data as shown in Table III is used as an example to be measured by the formula resulted from our algorithm. The score is assessed and described by Equation 12 and 13. The trigonometry model is illustrated in Fig. 6. Proportional position determination of the tested data is shown in Fig. 7.

$$cx = \frac{21.35^2 - 42.94^2 - 22.83^2}{-2 \times 42.94} = 22.23 \qquad (12)$$

Score of Glucose =

$$\left(\frac{20.704}{42.94} \times (2.8 - 0)\right) + 0 = 1.3502 \qquad (13)$$

<figure><img src="image_8.png" /><figcaption>Fig. 6. The Relationship between Test Data, Reference Data 1, and 2.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Proportional position determination of the tested data</figcaption></figure>

<table><caption>TABLE III. INTERPRETATION RESULT OF GLUCOSE REAGENT</caption><thead><tr><th>Level (mmol/L)</th><th>Reference Data</th></tr></thead><tbody><tr><td>- (0)</td><td></td></tr><tr><td>± (2.8)</td><td></td></tr><tr><td>+(5.5)</td><td></td></tr><tr><td>++ (14)</td><td></td></tr><tr><td>+++ (28)</td><td></td></tr><tr><td>++++ (≥ 55)</td><td></td></tr><tr><td>Test Data</td><td></td></tr><tr><td>Visual</td><td>- (0)</td></tr><tr><td>Dipstick Reader</td><td>- (0)</td></tr><tr><td>Quantitative</td><td>1.35</td></tr></tbody></table>
<table><caption>TABLE IV. SCORE ASSESSMENT COMPARISON IN SEVERAL SAMPLES</caption><thead><tr><td>Sample</td><td>Quantitative</td><td>Qualitative</td><td>Dipstick Reader</td><td>Visual</td></tr></thead><tbody><tr><td>1</td><td>1.35</td><td>- (0)</td><td>- (0)</td><td>- (0)</td></tr><tr><td>2</td><td>1.16</td><td>- (0)</td><td>- (0)</td><td>- (0)</td></tr><tr><td>3</td><td>1.24</td><td>- (0)</td><td>- (0)</td><td>- (0)</td></tr><tr><td>4</td><td>1.20</td><td>- (0)</td><td>- (0)</td><td>- (0)</td></tr><tr><td>5</td><td>1.32</td><td>- (0)</td><td>- (0)</td><td>- (0)</td></tr><tr><td>6</td><td>26.73</td><td>+++ (28)</td><td>+++ (28)</td><td>+++ (28)</td></tr><tr><td>7</td><td>42.12</td><td>++++ (≥ 55)</td><td>++++ (≥ 55)</td><td>++++ (≥ 55)</td></tr><tr><td>8</td><td>36.39</td><td>++++ (≥ 55)</td><td>++++ (≥ 55)</td><td>++++ (≥ 55)</td></tr><tr><td>9</td><td>2.23</td><td>± (2.8)</td><td>± (2.8)</td><td>± (2.8)</td></tr><tr><td>10</td><td>1.52</td><td>- (0)</td><td>- (0)</td><td>- (0)</td></tr></tbody></table>

<table><caption>TABLE V. INTERPRETATION COMPARISON
RESULT OF OUTLIER DATA</caption><thead><tr><th>Level (mmol/L)</th><th>Reference Data</th></tr></thead><tbody><tr><td>1</td><td></td></tr><tr><td>2</td><td></td></tr><tr><td>3</td><td></td></tr><tr><td>4</td><td></td></tr><tr><td>5</td><td></td></tr><tr><td>6</td><td></td></tr><tr><td>Test Data</td><td></td></tr><tr><td>Stepwise Linear Interpolation</td><td>Outlier</td></tr><tr><td>Visual</td><td>Outlier</td></tr><tr><td>Euclidean Distance</td><td>1</td></tr></tbody></table>

## 5 Conclusion

Using the proposed intelligent method for dipstick urin analysis, a self monitoring of blood sugar of diabetes mellitus patient can be done without examining the patient blood. This approach helps people in remote areas which have limited access to the hospital or medical laboratory to maintain their health. Even more, this approach does not require any physical intervention compare to blood test, makes it painless alternative for blood sugar control. With the highly availability and cheaper dipstick reagent strips, the operational cost of this method is considered cheaper than blood test.

The accuracy of the color quantification using this digital approach is very high makes the automatic color acquisition using smartphone is possible, even in the various lighting conditions.

The further improvement is a challenge to improve the color constancy performance with an automatic color calibration or using a relative calibration technique.
Acknowledgment

This research is supported by Japan
Indonesia Corporation Agency (JICA)-
Predicts phase-2, group B2-6 and a
research grant from the Ministry of
Education and Culture, The Republic of
Indonesia

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