Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

Ratchadaporn Kanawong†, Tayo Obafemi-Ajayi†, Tao Ma‡, Dong Xu†, Shao Li‡*, Ye Duan†*

†
Department of Computer Science and Informatics Institute, University of Missouri, Columbia, MO, USA
‡MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST and Department of Automation,
Tsinghua University, Beijing 100084, China.
*Co-Correspondence Authors. Emails: shaoli@mail.tsinghua.edu.cn, and duanye@missouri.edu

Abstract 
ZHENG (Traditional Chinese Medicine syndrome) is an integral and essential part of Traditional Chinese Medicine
(TCM) theory. It is defined as the TCM theoretical abstraction of the symptom profiles of individual patients. ZHENG
is used as a guideline in TCM disease classification. For example, patients suffering from gastritis may be classified as
Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue
appearance is known to be a valuable diagnostic tool for determining ZHENG in patients. In this work, we explore new
modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We
propose a novel color space based feature set, which can be extracted from tongue images of clinical patients to build an
automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and
coating to determine ZHENG, such as Cold or Hot ZHENG, and to diagnose different stomach disorders including
gastritis, we propose using machine learning techniques to establish the relationship between the tongue image features
and ZHENG by learning through examples. The experimental results obtained over a set of 263gastritis patients, most
of whom suffering Cold or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent
performance of our proposed system.

I. Introduction 
Traditional Chinese Medicine (TCM) has a long history in the treatment of various diseases in East Asian countries
and is also a complementary and alternative medical system in Western countries. TCM takes a holistic approach to
medicine with emphasis on the integrity of the human body and the close relationship between a human and its social
and natural environment [1]. TCM applies different therapeutic methods to enhance the body’s resistance to diseases
and prevention. TCM diagnosis is based on the information obtained from four diagnostic processes, i.e., looking,
listening and smelling, asking, and touching. The most common tasks are taking the pulse and inspecting the tongue
[2]. For thousands of years, Chinese medical practitioners have diagnosed the health status of a patients’ internal organs
by inspecting the tongue, especially the patterns on the tongue’s surface. The tongue mirrors the viscera. The changes of
tongue can objectively manifest the states of a disease, which can help differentiate syndromes, establish treatment
methods, prescribe herbs and determine prognosis of disease.
ZHENG (TCM syndrome) is an integral and essential part of TCM theory. It is a characteristic profile of all clinical
manifestations that can be identified by a TCM practitioner. ZHENG is an outcome after analyzing all symptoms and
signs (tongue appearance and pulse feeling included). All diagnostic and therapeutic methods in TCM are based on the
differentiation of ZHENG, and this concept is as ancient as TCM in China [3]. ZHENG is not simply an assemblage of
disease symptoms but rather can be viewed as the TCM theoretical abstraction of the symptom profiles of individual
patients. As noted in the abstract, ZHENG is also used as a guideline in TCM disease classification. For example,
patients suffering from the same disease may be grouped into different ZHENGs, whereas different diseases may
be grouped as the same ZHENG. The Cold ZHENG(Cold syndrome) and the Hot ZHENG (Cot syndrome)are
the two key statuses of ZHENG[3]. Other ZHENGs include Shen-Yang-Xu ZHENG (Kidney-Yang deficiency
syndrome), Shen-Xu ZHENG (Kidney deficiency syndrome), and Xue-Yu ZHENG (Blood Stasis syndrome)[4].
In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised
machine learning algorithms. Using an automated tongue-image diagnosis system, we extract objective features from
tongue images of clinical patients and analyze the relationship with their corresponding ZHENG data and disease
prognosis (specifically stomach disorders, i.e., gastritis) obtained from clinical practitioners. We propose a system that
learns from the clinical practitioner’s subjective data on how to classify a patient’s health status by extracting
 meeaningful feattures from ton ngue images using a rich set of featurees based on color
c space models.
m Our premise
p is thaat
Chhinese medicaal practitionerrs usually obsserve the tonggue color andd coating to determine
d ZH
HENG such ass Hot or Coldd
ZHHENG, and too diagnose diifferent stomach disorderss including gastritis.
g Hencce, we propose using macchine learningg
tecchniques to establish
e the relationship between thee tongue imaage features and the ZHE ENG by learrning throughh
exaamples. We area also interrested in the correlation between the HotH and Coldd patterns obsserved in ZHENG gastritis
pattients and their correspondding symptomm profiles.
V
Various typess of features have been explored for tongue
t featurre extraction and tongue analysis,
a inclluding texturee
[5],color[6], [7]], [8], shape [9], spectrum
m [8], etc. A systematic toongue feature set, comprising of a combination oof
geoometric featu ures (size, sh
hape, etc.), crracks, and texxtures, was later
l proposeed by Zhangg et al.[10].Coomputer-aidedd
tonngue analysis systems baseed on these tyypes of featurees have also been
b developeed [11] [12]. Our
O goal is too provide a seet
of objective feaatures that cann be extracteed from patients’ tongue images,
i basedd on the knowwledge of ZHHENG, whichh
impproves accuraacy of an objjective clinical diagnosis. Our proposeed tongue feaature set is baased on an exxtensive coloor
moodel.
T paper is organized as follows: In Section
This S II, wee provide a TC
CM descriptivve view of the physiology of the tonguee
whhile in Sectioon III, we describe our coolor model feeature set. An A overview of o the proposed feature extraction
e andd
leaarning framew work is presennted in Sectioon IV. Our experimental
e results and analysis
a in a tongue
t imagee dataset from
m
gasstritis patientss with Cold ZHENG
Z and Hot ZHENG G are discusseed in Section V before draawing our conclusions andd
prooposing planss for future woork in Sectionn VI.

II. Tonguee Diagnosiis in TCM 
T
TCM believess that the tongue has manyy relationshipps and connecctions in the human body,, both to the meridians
m andd
thee internal orgaans. It is therrefore very usseful and impportant duringg inspection for
f confirming TCM diagnnosis as it cann
preesent strong visual
v indicato ors of a personn's overall phhysical and mental harmonny or disharmoony. In TCM, the tongue is
divvided into tonngue tip, tongu ue margins, tongue
t center and tongue root.
r Figure 1a
1 shows eacch part of thhe tongue and d
its correspond dence to diffferent interrnal organs according to TCM while w Figure 1b illustraates how wee
geoometrically obtain an approximatio
a on of these regions from m the tongu ue image. Thhe tongue tipp reflects thee
patthological chhanges in thee heart and lungs,l while the bilaterall sides of the tongue refflect that of the liver andd
galllbladders. Thhe pathologicaal changes in the spleen annd stomach arre mirrored byy the center ofo tongue, whhile changes inn
thee kidneys, inteestines and blladder sectionn correspond to t the tongue root.

(a)Organ layout of tonggue regions (b)G

Geometrical layout of tongue reggions.
Figure
F 1: Tongu
ue areas and theeir correspondeence to internal organs in TCM
M

I this paperr, we focus on

In n the patientts with stomaach disorders, gastritis. Hence,
H we arre interested in extractingg
feaatures not jusst from entirre tongue imaage but also specifically from
f the mid ddle region, as
a this corressponds to thee
stoomach organ n, according to TCM. We W extract thee middle recctangular reggion, illustraated in Figurre 1b, as ourr
app proximationn for the tong
gue middle reegion.
 The practitioner examines the general and local shape as well as the color of the tongue and its coating. According to
TCM, the normal tongue is pale red with thin white coating. Some signs of imbalance or pathology are red body, yellow
coating, or thick coating like mozzarella cheese, etc. Some characteristic changes occur in the tongue in some particular
diseases. Most tongue attributes are on the tongue surface. A TCM doctor looks at several attributes of tongue body:
color, moisture, size, shape, and coating. These signs reveal not only overall states of health, but correlate to specific
organ functions and disharmonies, especially in the digestive system.
The two main characteristics of the tongue in TCM ZHENG diagnosis are the color and the coating. The color of the
patient’s tongue color provides information about his/her health status. For example[13] dark red color can indicate
inflammation or ulceration, while a white tongue indicates cold attack, mucus deposits, or a weakness in the blood
leading to such conditions as anemia [12]. Moreover, a yellow tongue points out a disorder of the liver and gallbladder,
and blue or purple implies stagnation of blood circulation and a serious weakening of the part of the digestive system
that corresponds to the area of the tongue where the color appears.
The coating on the tongue is discriminated by not only its presence but also its color. The color could be yellow, white
and other colors. However, the color in image is not the exact true color of the tongue. To properly identify the color of
the tongue coating, we applied the specular component technique presented in our prior work on tongue detection and
analysis [2]. Figure 2 illustrates different tongue images of patients and their corresponding ZHENG class.

(a) Hot ZHENG (b) Cold ZHENG (c) Normal

Figure 2: Tongue images of patients with different ZHENG classification. “Normal” represents a healthy person.

III. Tongue Feature Extraction and Classification Framework  
A. Feature Extraction for Tongue Image Analysis 
Our goal is to compute a set of objective features from each tongue image j that can be fed into our learning
system so that we can predict not only the color and coating on the tongue, but also different ZHENGs of the gastritis
patients. These features are designed to capture different color characteristics of the tongue. While a single feature may
not be very discriminative, our premise is that the aggregation of these features will be discriminative. We leave it to the
learning algorithm to determine the weight/contribution of each feature in the final classification.
Most color spaces are represented in tuples of number, normally three or four color components. Color components
determine the position of the color in the color space used. There are many color spaces defined for different purposes.
We designed a set of 25 features that span the entire color space model. They can be grouped under eight categories:
RGB, HSV, YIQ, Y’CbCr, XYZ, L*a*b*, CIE Luv, and CMYK.
In this section, we first describe in detail how we compute each feature per ith pixel in the image. Then, we explain
how each feature per pixel is aggregated to obtain per tongue image j.
RGB 
RGB is an additive color system, based on trichromatic theory in which red, green, and blue light components are
added together to produce a specific pigment. The RGB model encodes the intensity of red, green and blue,
respectively . , , for each pixel is an unsigned integer between 0 and 255. Each RGB feature
1, … , 3 represents the normalized intensity value of the red, green, and blue component respectively of the i‐th
pixel in the image. We denote the normalized value of each component as , , . Thus,
; ; .
 All the remaining color space model features described in our feature set derive their value from the RGB feature set.
HSV 
HSV color space represents color using a 3-tuple set of hue, saturation, and value. It separates the luminance
component of the color from chrominance information. The HSV model , , is obtained by a linear
transformation of thenormalized RGB color space { , , .
For each pixel , let max , , represent the maximum value of the pixel’s RGB triple set while
min , , , the minimum value of the set. We also denote the difference between maximum and minimum values of
each RGB tuple by ∆ . The HSV components , , are computed from RGB color space { , , as
follows:

0, 0
∆
,

0 , ∆ 0
,
6. ∆
1
2 . ,
∆ 6
1
4 . ,
∆ 6
Thus, the HSV features are ; ; .
YIQ 
The YIQ color model is the television transmission color space for a digital standard. The Y component represents the
perceived luminance, while I and Q components are the color information. I character is referred to “in-phase” term and
Q letter stands for “quadrature”. I and Q can place color in a graph representing I as X axis and Q as Y axis. The YIQ
system takes advantage of human color perceiver characteristics [15], [16].
The YIQ model , , is obtained by a linear transformation of the normalized RGB color space { , , as
follows:
0.299 0.587 0.114
0.596 0.274 0.322 .
0.211 0.523 0.312
The , , values are each normalized to obtain , , 0,1 . Thus, the YIQ features are ;
; .
Y’CbCr 
Like YIQ, Y’CbCr is the television transmission color spaces but it is in analogue spaces for the NTSC system.
YCbCr color space detaches RGB into the luma component, the blue-difference and red-difference chroma components.
The transformation equation from RGB (un-normalized) modeltoYCbCr is defined as:
0.299 0.587 0.114
0.169 0.331 0.500
0.500 0.419 0.081
Similar to the YIQ features, the , , values are each normalized to obtain , , 0,1 . Thus the
YIQ features are ; ; .
 
 
 XYZ 
Brightness and chromaticity are two principal components of color that interact with human vision. XYZ are
developed under CIE XYZ color space [14]. The XYZ values can be obtained by a linear transformation of the gamma
corrected value of the RGB normalized color space { , , .
The gamma-corrected function is defined as:

t 0.04045
12.92
γ .
t
1
0.055. Thus, XYZ model consisting of { , " , componentsis given by

0.4124 0.3576 0.1805

" 0.2126 0.7152 0.0722
0.0193 0.1192 0.9505
The { , " , values are each normalized to obtain , ", 0,1 . Thus the XYZ features are defined as
; "; .
L*a*b* 
CIE L*a*b* color space is a nonlinear transformation of the CIE XYZ color space [17]. CIE L*a*b* try to imitate the
logarithmic response of the human eye. The L* component is designed to match closely with human perception of
lightness. The other two components describe the chroma.
The forward transformation of CIE XYZ color space to CIE L*a*b* is computed as follows:
"
116 16

"
500
 
"
200
 
/
,

and denotes the D65 white point given by {0.950456, 1.0, 1.088754}.
The L*a*b* values { , , } are normalized as , , 0,1 . Hence, the CIE L*a*b* color features are
given by ; ; .
CIE Luv 
CIE Luv, or L*u*v*, is color space computed from the transformation of the CIE XYZ color space by International
Commission on Illumination (CIE) in order to perceptual uniformity [17]. Similar to CIE L*a*b*, the D65white point is
referred by .

29 " " 6
" 3 2 2 29
/
"
116 16
2
 " 4
13
15 " 3

" 9 "
13
15 " 3

"
where 0.2009, 0.4610,under the standard luminance C. The normalized , , values are denoted
by " , , 0,1 . Therefore, "
; ; .
CMYK 
The CMYK color space is a subtractive color system mainly used in the printing industry [14]. The components
consist of cyan, magenta, yellow, and neutral black. It is a common way to translate RGB display on monitors to
CMYK values for printing.
Let max , , represent the maximum value of the pixel’s RGB triple set. The CMYK color space, denoted
by , , , can be computed from the RGB model as follows:
1

Thus, the CMYK features are computed as: ; ; ; .

Aggregate Operators for the feature vectors 
To train our classification model using this set of features, we need to combine the features per pixel into one
composite feature vector per tongue image (or region)j. We aggregate the pixel features using two different
statistical averages (mean and median) and the standard deviation values. We derive five variations of feature vectors
for our automated tongue ZHENG classification system using the following operators: mean, median ( ), standard
deviation ( ), “mean plus standard deviation” ( , ), and “median plus standard deviation” ( , ).
Let N denote the number of pixels in a given tongue image (or region)j. The mean feature vector is denoted by
, where is given by
∑
, 1, … , 25.

The median feature vector, denoted by , is computed as ,

1, … , 25.Standard deviation depicts the margin of difference between a given feature value and its average value among
all the pixels in the given region. Thus, the standard deviation feature vector is denoted by ,where is
given by

∑
, 1, … , 25.

The “mean plus standard deviation” denoted by , , is a concatenation of the mean feature vector and the
standard deviation feature vector. Similarly, the “median plus standard deviation” feature vector, denoted by
, , is a concatenation of the median feature vector and the standard deviation feature vector. Thus, the total
number of features in both concatenated feature vectors is 50 each.
 B. Supervised Learning Algorithms for ZHENG Classification 
We apply three different supervised learning algorithms (AdaBoost, Support Vector Machine, Multi-layer Perceptron
Network) to build classification models for training and evaluating the proposed automated tongue based diagnosis
system. Each model has its strength and weakness, which we describe briefly below. We empirically evaluate their
performance over our dataset.

AdaBoost 
An ensemble of classifiers is a set of classifiers whose individual predictions are combined in some way (typically by
voting) to classify new examples. Boosting is a type of ensemble classifier which generates a set of weak classifiers
using instances drawn from an iteratively updated distribution of the data, where in each iteration the probability of
incorrectly classified examples is increased and the probability of the correctly classified examples is decreased. The
ensemble classifier is a weighted majority vote of the sequence of classifiers produced.
The AdaBoost algorithm [18] trains a weak or base learning algorithm repeatedly in a series of roundt 1, … , T.
Given a training set , ,.., , where belongs to some domain X and 1, 1 (the corresponding binary
class labels), we denote the weight of i-th example in round t by . Initially, all weights are set equally and
so , . For each round t, a weak learner is trained using the current distribution . When we obtain a weak
hypothesis with error ~ . ,if 1/2 , we end training; otherwise, we set and
update as:

, where is a normalization factor.

The final hypothesis is given by ∑ .

 
Support Vector Machine  
The Support Vector Machine (SVM) [19] is one of the best-known general purpose learning algorithms. The goal of
the SVM is to produce a model which predicts target values of data instances in the testing set given a vector of feature
attributes. It attempts to maximize the margin of separation between the support vectors of each class and minimize the
error in case the data is non-linearly separable. The SVM classifiers usually perform well in high-dimensional spaces,
avoid over-fitting and have good generalization capabilities.
For a given a training set , ,.., , the SVM model for an instance can be written as [20]:
∑ , ,
where is the kernel function used (polynomial kernel in this work), is the Lagrange multiplier and is a constant.
In our work, we utilize the Sequential Minimal Optimization (SMO) algorithm [21], which gives an efficient way of
solving the dual problem of the support vector machine optimization problem.
 
Multilayer Perceptron Networks 
The Multilayer Perceptron Network (MLP) [22]is a feed-forward neural network with one or more layers that are
hidden from the input and output nodes. Neural networks have the ability to learn complex data structures and
approximate any continuous mapping [23]. The model of each neuron in the network includes a nonlinear activation
function that is differentiable such as the sigmoid. The units each perform a biased weighted sum of their inputs and
pass this activation level through the transfer function to produce their output given by
,
where is the synaptic vector, is the input vector, is the bias constant, and is the transpose operator. For K-class
classification, the MLP uses back propagation to implement nonlinear discriminants. There are K outputs with softmax
as the output nonlinearity.
 C. Dataset Labeling and Preprocessing 
Our proposed system relies on a labeled dataset, to effectively build an automated tongue-based ZHENG
classification system. Our dataset is comprised of tongue images from 263gastritis patients and a control group of
48healthyvolunteers. Most of the gastritis patients have been classified as Hot or Cold ZHENG and are identified with a
color label (yellow or white) based on the color of the coating of their tongue, as determined by their Chinese doctors.
The doctors also carry out a detailed profile of the ZHENG symptoms for each patient based on clinical evaluations.
The list of the main symptom profile terms is summarized in Table 1.
Table 1: Symptom profile terms of Cold ZHENG and Hot ZHENG
Subjects Terms (keywords)
Cold-ZHENG Cold (chill, coldness), hot diet/drink preferred, desires warm environment, pale flushing of
related face, not thirsty, no bad mouth breath, no acidic saliva, clear urine, loose stool, high and
symptoms short pitch voice, and feeling cold at limbs.
Hot-ZHENG Fever (heat, hot), cold diet/drink preferred, desires cold environment, red flushing of face,
related thirsty, obvious bad mouth breath, acidic saliva, yellow urine, hard stool, constipation, and
symptoms feeling hot at limbs.

We are also interested in the relationship between TCM diagnosis and Western medicine diagnosis; hence,for a subset
of the patients, we are provided with their corresponding Western medical gastritis pathology. They are grouped into
two categories: superficial vs. atrophic. In Western medicine, the doctors are also interested in knowing whether the
Helicobacter Pylori (HP) bacterium found in the stomach is present (positive) or absent (negative) in the patients with
chronic gastritis. Thus, we are provided with that information for a subset of the patients. It was not feasibleto obtain all
the different information collected per patient. Table 2 summaries the population of each subset for four different labels
(ZHENG, Coating, Pathology, and HP).
Table 2: Data Label Summary for the Gastritis Patients
Data Labels Population
ZHENG: Hot/Cold 132/68
Coating: Yellow/White 147/67
Pathology: Superficial/Atrophic 84/144
HP bacterium: Positive/Negative 72/167

IV. Results and Analysis 
A. Experimental Setup 
In this section, we evaluated the performance of our proposed ZHENG classification system using the three
classification models (AdaBoost, SVM, and MLP) described in section III-B. We compared the performance of training
the classifier models using the set of features extracted from the entire tongue image vs. the middle tongue region only.
As mentioned in Section II, in TCM, it is believed that the middle tongue region provides discriminant information for
diagnosing stomach disorders. Hence, we extract features from the middle tongue region, as described in Figure
1b, to evaluate the performance compared to extracting features from the entire tongue region. In training and
testing our classification models, we employ a 3-fold cross-validation strategy. This implies that the data is split into
three sets; one set is used for testing and the remaining two sets are used for training. The experiment is repeated with
each of the three sets used for testing. The average accuracy of the tests over the three sets is taken as the performance
measure. For each classification model, we varied the parameters to optimize its performance. We also compare the
results obtained using the five different variations of the feature vector (Mean = , Median = , standard
deviation= , mean + standard deviation= , , and median + standard deviation= , ), as described in
section III-A.We also apply Information Gain attribute evaluation on the feature vectors to quantify and rank the
significance of individual features. Lastly, we apply the Best First feature selection algorithm to select the
‘significant’ features before training the classifiers to compare the performance of training the classifiers with
the whole feature set against selected features.
The performance metrics used are the Classification Accuracy (CA) and the average F-measure. CA is defined as the
percentage of correctly classified instances over the entire set of instances classified. In our dataset, as described in
Table 2, for each data label, the population of both classes (which we denote by , ) is not uniformly distributed.
Hence, evaluating the performance of our classifiers using simply the classification accuracy does not paint an accurate
picture of the discriminative power of the classifier. Since the dataset distribution is skewed, we can achieve a high
accuracy but very poor performance in discriminating between both classes. Thus, we judge our classifiers using the
average F-measure obtained for both binary classes. The F-measure combines precision and recall. It measures how well
an algorithm can predict an instance belonging to a particular class. Let TP represent True Positive, which we define as
the number of instances that are correctly classified as for a given test set while TN denotes True Negative, the
equivalent for instances. Let FP represent False Positive, which we define as the number of instances that are
incorrectly classified as for a given test set while FN denotes False Negative, the equivalent for instances.
Precision TP/ andRecall TP/ . Thus, the F-measure is defined as:
2 · Recall·Precision
F‐measure
Recall Precision
For both binary classes , , let | |, | | denote the total number of instances belonging to class and
, respectively, then the average F-measure is defined as:
| | · F‐measure | | · F‐measure
‐measure
| | | |

In all the tables illustrating the different experimental results, we highlight the best ‐measure obtained along with
the corresponding Classification Accuracy of the classifier.

B. Classification Results based on Tongue Coating and ZHENG for Gastritis Patients 
The experimental results presented in this section analyze the discrimination among the gastritis patients based on
their tongue coating color and ZHENG category. Table 3 summarizes the results obtained using our proposed color
space feature vector to train the classifiers to automatically classify the color of the coating of a gastritis patient’s tongue
as yellow or white. We can observe from Table 3 that the combination of the median and standard deviation feature
values ( , yield the best result for both the entire tongue region and the middle tongue region only. The
results for both regions are also very comparable.
When using the entire tongue region, the top three significant features for the color coating classification, ranked by
the Information Gain attribute, were , , , which denote the standard deviation of Q chroma (YIQ
model), the median of Cr component (YCbCr) and the standard deviation of Green Channel (RGB), respectively. For
the middle tongue region only, the top three were: , , which denotes the standard deviation of Q
chroma (YIQ model), the standard deviation of u component (L*u*v*), and the median of the Hue (HSV). It is also
interesting to observe that out of the top ten significant features using the entire region vs. the middle tongue region,
they both have six of those features in common.
The result obtained on ZHENG classification between the Hot and Cold groups is shown in Table 4. For the ZHENG
classification, using the standard deviation feature values ( performs best when dealing with the entire tongue region
while the , feature vector is the top performer for the middle tongue region only.
For ZHENG Classification between Hot and Cold Syndromes for gastritis patients, when using the entire tongue
region, only one feature was considered significant by the Information Gain attribute: i.e,which is the standard
deviation of Q chroma (YIQ model). For the middle tongue region, the most important feature is , the standard
deviation of u component (L*u*v*). Even though the noteworthy feature in the entire tongue area and the middle tongue
area is not the same, both Q component in YIQ color space and u component in L*u*v* color space show the difference
from green to red in chromaticity diagram.
Table 5 summarizes the results obtained when we train different classifiers to detect the presence of the HP bacteria in
a gastritis patient using the color feature vector. The classification result obtained in learning the pathology groups of
the patients (superficial vs. atrophic) is shown in Table 6. Both cases are not very strong, which illustrates a weak
correlation between the western medicine diagnosis and the tongue information utilized by Chinese medical
practitioners. No feature was identified as significant in either case.
Tables 7 – 10 illustrate how experimental results reflect the analysis of the classification between two pathology types
of gastritis patients according to ZHENG category. Table 7 summarizes the results obtained using our proposed color
space feature vector to train the classifiers to automatically classify between Superficial group and Atrophic group for
patients labeled as Cold ZHENG. The results obtained on classification between Superficial group and Atrophic group
for Hot ZHENG patients is shown in Table 8. We can observe from Table 7 that the feature vector performed best
for the entire tongue region while the , feature vector yielded the best result for the middle tongue region.
Similarly, from Table 8 we can observe that for the Hot ZHENG patients, for the middle tongue region, the
, feature vector also performed best. However, , feature vectorperforms best when dealing with the
entire tongue region.
When using the entire tongue region, the top three significant features for the pathology classification between
Superficial and Atrophic in Cold ZHENG, ranked by the Information Gain attribute, were , , which denote
the standard deviation of Q chroma (YIQ model), the standard deviation of Value component (HSV) and the standard
deviation of Red Channel (RGB), respectively.
In Table 8, when using the entire tongue region, the top three significant features for the pathology classification
between Superficial and Atrophic in Hot Syndrome, ranked by the Information Gain attribute, were , ,
which denote the mean of Cyan Ink (CMYK model), the mean of Black Ink (CMYK model), and the mean of Blue
Channel (RGB) respectively. For the middle tongue region only, the top three were: , , which denote
the standard deviation of Cyan Ink (CMYK model), the standard deviation of Black Ink (CMYK model) and the median
of Black Ink (CMYK model)
The next set of experimental results focus on training our classifier using our proposed color space feature vector to
discriminate Hot ZHENG from Cold ZHENG in each pathology group. Table 9 summarizes the results obtained to train
the classifiers to automatically classify between Hot and Cold ZHENG for superficial gastritis patients Table 10 reflects
the results for gastritis patients. We can observe from Table 9that both , and , feature vectors perform
the best for both the entire tongue region and the middle tongue region. From results in Table 10, using the standard
deviation feature values ( , ) performs best when dealing with the entire tongue region while the ( , )
feature vector is the top performer for the middle tongue region.
When using the entire tongue region, the top three significant features for the ZHENG classification between Hot
Syndrome and Cold Syndrome in the patients who are superficial, ranked by the Information Gain attribute,
, , , which denotes the standard deviation of Q chroma (YIQ model), the median of Blue Channel
(RGB) and the median of the blue sensitivity Z component respectively. For the middle tongue region only, the top
three weremedF24, σF19, andmedF5which denote the median of Yellow Ink (CMYK), the standard deviation of lightness
component (Luv model), and the median of saturation (HSV). It is also interesting to observe that by comparing the set
of the top five significant features using the entire region vs. the set from the middle tongue region, they both have the
yellow ink (CMYK) in common.
When using the entire tongue region, there is only one significant feature difference for the ZHENG classification
between Hot Syndrome and Cold Syndrome in patients who are atrophic, ranked by the Information Gain attribute,
which denotes the standard deviation of Q chroma (YIQ model). For the middle tongue region only, there were two
significant features: , which denote the mean of the blue sensitivity Z component (XYZ) and the mean of the
Blue channel (RGB).
Table 3: Tongue Coating Color Classification: Yellow vs. White for Gastritis Patients
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.681 69.16 0.757 76.64 0.752 76.17 0.761 77.57 0.796 80.84 0.773 78.04

, 0.743 74.77 0.792 79.44 0.774 77.57 0.764 76.64 0.799 80.37 0.767 77.10

0.758 76.64 0.728 74.30 0.724 72.90 0.735 74.77 0.789 79.44 0.766 77.10

, 0.763 76.64 0.801 80.37 0.767 77.10 0.781 78.50 0.775 77.10 0.811 81.31

0.747 75.70 0.797 79.91 0.783 78.50 0.747 74.77 0.777 77.57 0.783 78.97

Table 4: ZHENG Classification between Hot and Cold Syndromes for Gastritis Patients
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.618 63.50 0.716 71.50 0.710 71.00 0.622 63.50 0.710 70.50 0.663 67.00

, 0.750 75.00 0.680 67.50 0.723 72.00 0.664 68.00 0.735 73.50 0.740 74.00

0.647 65.50 0.649 64.50 0.676 68.00 0.684 71.00 0.661 67.00 0.690 69.00

, 0.738 74.50 0.665 66.00 0.726 72.50 0.685 70.00 0.708 72.00 0.761 76.00

0.763 76.50 0.709 71.00 0.709 71.00 0.676 69.00 0.704 70.00 0.719 72.00

Table 5: Detection of Presence of HP Bacteria (Positive vs. Negative) in Gastritis Patients.

Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.679 71.97 0.681 68.20 0.673 68.20 0.696 71.97 0.686 70.29 0.682 70.29

, 0.644 66.11 0.680 67.78 0.713 71.97 0.632 64.85 0.681 68.20 0.681 67.78

0.655 67.78 0.666 67.36 0.666 67.78 0.699 71.55 0.644 69.04 0.676 68.20

, 0.655 67.78 0.686 68.20 0.695 69.87 0.633 65.27 0.631 64.44 0.684 68.20

0.661 68.20 0.695 71.13 0.702 70.29 0.594 61.09 0.669 66.95 0.649 65.27
 Table 6: Classification between Superficial and Atrophic Pathology of the Gastritis Patients.
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.604 63.16 0.642 64.47 0.627 63.16 0.658 66.67 0.631 63.16 0.622 62.72

, 0.633 65.35 0.662 65.79 0.702 71.05 0.604 61.40 0.630 63.60 0.621 62.28

0.633 64.47 0.601 62.72 0.640 64.04 0.623 65.79 0.632 63.16 0.623 62.28

, 0.657 66.23 0.660 65.79 0.697 69.74 0.613 62.72 0.645 64.47 0.663 66.23

0.637 64.91 0.697 70.18 0.659 66.23 0.631 64.04 0.629 63.16 0.639 64.47

Table 7: Tongue Classification between Superficial and Atrophic in Cold Syndrome Patients
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.579 58.33 0.658 66.67 0.633 63.33 0.651 65.00 0.639 65.00 0.633 63.33

, 0.716 71.67 0.647 65.00 0.680 68.33 0.643 65.00 0.649 65.00 0.662 66.67

0.600 60.00 0.714 71.67 0.733 73.33 0.633 63.33 0.613 66.67 0.633 63.33

, 0.717 71.67 0.698 70.00 0.700 70.00 0.684 68.33 0.598 60.00 0.667 66.67

0.701 70.00 0.761 76.67 0.745 75.00 0.579 58.33 0.598 60.00 0.601 60.00

Table 8: Tongue Classification between Superficial and Atrophic in Hot Syndrome Patients
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.768 77.06 0.755 75.23 0.735 73.39 0.710 71.56 0.735 76.15 0.680 67.89

, 0.741 74.31 0.845 84.40 0.764 76.15 0.680 68.81 0.777 77.06 0.780 77.98

0.718 72.48 0.708 72.48 0.718 71.56 0.686 68.81 0.706 70.64 0.736 73.39

, 0.715 71.56 0.817 81.65 0.815 81.65 0.672 67.89 0.774 77.06 0.808 80.73

0.770 77.06 0.818 81.65 0.817 81.65 0.675 67.89 0.792 78.90 0.781 77.98
 Table 9: Tongue Classification between Hot Syndrome and Cold Syndrome in Superficial Patients
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.583 59.68 0.773 77.42 0.705 70.97 0.705 70.97 0.773 77.42 0.726 72.58

, 0.740 74.19 0.839 83.87 0.765 77.42 0.690 69.35 0.839 83.87 0.757 75.81

0.628 62.90 0.740 74.19 0.743 74.19 0.675 67.74 0.710 70.97 0.658 66.13

, 0.774 77.42 0.839 83.87 0.755 75.81 0.774 77.42 0.839 83.87 0.774 77.42

0.834 83.87 0.757 75.81 0.838 83.87 0.819 82.26 0.791 79.03 0.750 75.81

Table 10: Tongue Classification between Hot Syndrome and Cold Syndrome in Atrophic Patients
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.539 55.14 0.642 63.55 0.645 64.49 0.572 58.88 0.762 75.70 0.615 61.68

, 0.662 67.29 0.681 69.16 0.698 70.09 0.638 64.49 0.702 69.16 0.685 68.22

0.612 61.68 0.646 63.55 0.666 66.36 0.611 62.62 0.606 62.62 0.638 64.49

, 0.704 71.03 0.657 64.49 0.677 68.22 0.604 60.75 0.701 69.16 0.703 70.09

0.696 70.09 0.691 68.22 0.734 73.83 0.650 64.49 0.675 66.36 0.645 63.55

C. Classification Results for Gastritis Patients vs. Control Group 
The experimental results presented in this section analyze the discrimination between the gastritis patients and control
group. Table 11 summarizes the results obtained using our proposed color space feature vector to train the classifiers to
automatically classify patients with coating on tongue vs. healthy patients with normal tongue (without coating). We can
observe from Table 11 that the , feature vector yields the best result for the entire tongue region whilefor the
middle tongue region, it was the feature vector.
When using the entire tongue region, the top three significant features for distinguishing between normal tongue and
tongue with coating, ranked by the Information Gain attribute, were , , which denote the standard
deviation of Red Channel (RGB), the standard deviation of Value component (HSV) and the standard deviation of
Black Ink (CMYK)respectively. For the middle tongue region only, there were only two significant features:
, which denote the standard deviation of lightness component (L*a*b) and the standard deviation of a*
component (L*a*b*). It is also interesting to observe that by comparing the set of the top 10 significant features using
the entire region vs. the set from the middle tongue region, they both have the lightness and a* component (L*a*b*) in
common.
The results obtained from the classification between the normal group and the entire set of patients with ZHENG
syndrome is shown in Table 12. The , feature vector performs best when dealing with the entire tongue region
while the , feature vector is the top performer for the middle tongue region.
When using the entire tongue region, the top three significant features for the classification between the normal group
and the gastritis group, ranked by the Information Gain attribute, were , , which denote the standard
 deviation of Red Channel (RGB), the standard deviation of Value component (HSV) and the standard deviation of
Black Ink (CMYK)respectively. For the middle tongue region only, the top three were: , , which
denote the median of Red Channel (RGB), the median of Value component (HSV) and the standard deviation of
lightness component (L*a*b*).
Table 13 and 14 show the results of training our classifiers to discriminate between the normal group and the hot
ZHENG patients only, and then normal group vs. cold ZHENG patients only. Table 13 illustrates the results for normal
vs. hot ZHENG. We can observe that the feature vector performs best both for the entire tongue region and the
middle tongue region. From Table 14, when only the normal vs. cold ZHENG patients is considered, the same feature
vector, , , performs best for both cases, however considering only the middle tongue region outperforms using
the entire tongue region.
When using the entire tongue region, the top three significant features for the classification between the normal group
and the gastritis patients with Hot Syndrome, ranked by the Information Gain attribute, were , , which
denote the standard deviation of Red Channel (RGB), the standard deviation of Value component (HSV) and the
standard deviation of Black Ink (CMYK),respectively. For the middle tongue region only, there were only two
significant features: , which denote the standard deviation of lightness component (L*a*b) and the standard
deviation of a* component (L*a*b*). When the set of the top ten significant features using the entire region vs. the set
from the middle tongue region are compared, they both have the lightness and a* component (L*a*b*) in common.
When using the entire tongue region, the top three significant features for the classification between the normal group
and the gastritis patients with Cold Syndrome, ranked by the Information Gain attribute, were , , which
denotethe standard deviation of Black Ink (CMYK), the standard deviation of Cyan Ink (CMYK) and the standard
deviation of Red Channel (RGB) respectively. For the middle tongue region only, the top three were: , ,
which denote the standard deviation of lightness component (L*a*b), the mean of Cyan Ink (CMYK), and the standard
deviation of a* component (L*a*b*).
When using the entire tongue region, the top three significant features for the classification between the normal group
and the superficial group, ranked by the Information Gain attribute, were , , which denote the standard
deviation of Red Channel (RGB), the standard deviation of Value component (HSV) and the standard deviation of
Black Ink (CMYK)respectively. For the middle tongue region, the top three were: , , which
denote the median of Q chromatic component (YIQ), the median of Red Channel (RGB), and the median of Value
component (HSV).
When using the entire tongue region, the top three significant features for the classification between the normal group
and the atrophic group, ranked by the Information Gain attribute, were , , which denote the mean of
Black Ink (CMYK model), the mean of Cyan Ink (CMYK model), and the mean of Red Channel (RGB) respectively.
For the middle tongue region, the top three were: , , which denote the median of red sensitivity X
component (XYZ), the standard deviation of lightness (L*a*b*), and the standard deviation of Cyan Ink (CMYK).

Table 11: Classification between Normal Tongue and Tongue with Coating
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.803 82.82 0.831 82.44 0.795 80.53 0.771 78.63 0.774 77.48 0.764 75.95

, 0.829 83.59 0.851 85.11 0.848 85.50 0.812 81.68 0.814 81.68 0.816 82.44

0.785 80.53 0.803 83.21 0.814 83.21 0.776 80.53 0.791 78.63 0.784 79.39

, 0.814 83.21 0.835 83.59 0.861 86.26 0.817 83.59 0.823 82.06 0.824 82.44

0.818 83.21 0.839 83.59 0.851 85.11 0.837 84.73 0.786 79.39 0.818 82.44
 Table 12: Tongue Classification between Normal Group and ZHENG Gastritis Group
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.765 78.63 0.809 80.24 0.784 78.63 0.781 79.44 0.770 76.61 0.762 76.61

, 0.836 84.68 0.852 84.68 0.857 85.89 0.820 82.66 0.798 80.65 0.826 82.26

0.756 77.82 0.795 81.45 0.784 78.63 0.772 78.23 0.817 81.45 0.785 78.63

, 0.802 81.45 0.845 84.27 0.844 84.68 0.779 79.44 0.837 83.47 0.869 87.10

0.826 83.47 0.849 84.68 0.843 84.27 0.799 81.05 0.780 77.02 0.833 83.87

Table 13: Tongue Classification between Normal Group and Hot ZHENG
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.671 70.00 0.781 77.78 0.708 72.22 0.741 75.00 0.773 77.22 0.755 76.11

, 0.804 80.56 0.792 79.44 0.816 81.67 0.780 78.89 0.764 77.22 0.799 79.44

0.721 72.78 0.711 72.22 0.739 75.00 0.727 73.89 0.739 73.33 0.744 74.44

, 0.796 80.00 0.814 82.78 0.797 80.00 0.781 79.44 0.752 75.00 0.798 79.44

0.768 77.22 0.828 82.22 0.826 82.78 0.736 75.00 0.766 77.22 0.805 80.56

Table 14: Tongue Classification between Normal Group and Cold ZHENG
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.690 68.97 0.759 75.86 0.676 68.10 0.714 71.55 0.741 74.14 0.731 73.28

, 0.742 74.14 0.785 78.45 0.748 75.00 0.826 82.76 0.759 75.86 0.750 75.00

0.686 68.97 0.745 75.00 0.757 75.86 0.672 67.24 0.750 75.00 0.742 74.14

, 0.759 75.86 0.774 77.59 0.734 73.28 0.768 76.72 0.733 73.28 0.811 81.03

0.741 74.14 0.733 73.28 0.734 73.28 0.679 68.10 0.723 72.41 0.708 70.69
 Table 15: Tongue Classification between Normal Group and Superficial Patients
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.655 65.91 0.737 74.24 0.754 75.76 0.694 69.70 0.687 68.18 0.704 70.45

, 0.679 68.18 0.751 75.00 0.774 77.27 0.749 75.00 0.744 74.24 0.719 71.97

0.675 67.42 0.737 74.24 0.737 73.48 0.733 73.48 0.677 67.42 0.739 73.48

, 0.695 70.45 0.759 75.76 0.811 81.06 0.749 75.00 0.762 75.76 0.726 72.73

0.687 68.94 0.735 74.24 0.706 70.45 0.726 72.73 0.742 74.24 0.749 75.00

Table 16: Tongue Classification between Normal Group and Atrophic Patients
Entire Tongue Middle Tongue
AdaBoost SVM MLP AdaBoost SVM MLP
Feature
Vector F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA F-meas CA
0.733 75.52 0.803 80.21 0.781 79.17 0.754 77.08 0.770 78.13 0.699 70.83

, 0.736 73.96 0.772 78.13 0.837 83.85 0.798 80.73 0.782 78.65 0.802 80.21

0.726 73.96 0.754 77.08 0.751 75.52 0.726 75.52 0.749 74.48 0.753 75.52

, 0.738 74.48 0.816 82.29 0.818 81.77 0.751 75.52 0.792 78.65 0.848 84.90

0.761 77.08 0.787 79.69 0.799 80.21 0.772 78.13 0.798 80.21 0.791 79.69

D. Analysis of Classification Results 
From the experimental results presented in Sections IV-B and IV-C, we can draw the following conclusions. Firstly,
concerning the performance of the different classification models, we observe that the MLP and SVM models usually
outperformed the AdaBoost model. The Multi-layer Perceptron neural network seems most adequate for learning the
complex relationships between the color features of the tongue images and the ZHENG/coating classes. However, both
the MLP and SVM models have many parameters to consider and optimize while the AdaBoost is a much simpler
model. In the AdaBoost model, we use a decision tree as our base weak-learner and vary the number of classifiers to
optimize its performance.
Secondly, we observe that when making discriminations within the gastritis patients group (hot vs. cold ZHENG,
yellow vs. white coating, etc.); it was more profitable to apply the feature vectors on the entire tongue image. When
classifying the normal groups vs. the ZHENG groupings, usually, it improved classifier performance to apply the
feature vectors to the middle tongue regions only.
Thirdly, we also observe that from the evaluation of the variations of the feature vectors used, taking into account
both the average and the standard deviation usually resulted in an excellent performance. It seemed like the mean
outperformed the median slightly, overall, i.e. , . In a few cases, simply considering variation of the spread of the
values over the region ( ) yielded the best performance. Thus, we can conclude that when deriving a feature vector
for the tongue image, the mean (or median) as well as the standard deviation (which takes into account the variation of
the spread on the region) is very important.
Lastly, we observe that though we were not able to effectively discriminate between the pathology groups (superficial
vs. atrophic and also the presence of the HP bacterium using our color space feature vectors, we were able to classify
them much better when we took into account the ZHENG classes. This further strengthens the notion that our proposed
 color space feature vectors are able to discriminate between the hot and cold ZHENG patients in addition to discerning a
ZHENG patient from a non-ZHENG (healthy) patient.

E. Applying Feature Selection Algorithm 
The classification results presented in Sections IV-B and C were obtained using the whole feature set. For each
experiment carried out on the entire tongue region, we also applied Information Gain Attribute evaluation to
rank the significance of the features. In this section, we apply feature selection algorithm (Best First) to select
only a subset of features, which are deemed significant, before training the classifiers. Our goal is to see if this
would yield a better result than using the whole feature set. The Best First algorithm searches the space of
attribute subsets by greedy hill climbing augmented with a backtracking facility.
The summary of the results obtained is shown in Table 17.The normal group refers to the healthy (non-
ZHENG) control group. We present the best classification result obtained for each experiment based on using
the five variations of the feature vectors ( , , , , , , ) and the three different
classification models (Adaboost, SVM, and MLP). As we can observe from Table 17, using the whole feature set
to train the classifiers yielded a better result in all cases except for the Atrophic Patients (Hot vs. Cold ZHENG)
experiment. Thus, we can conclude the overall, using the aggregate of the proposed feature sets is more
discriminative even though some features are more significant than others.

Table 17: Comparison between using Selected features vs. Whole feature set for Classification
Classification Experiment Type Feature Selection Whole Feature
F-measure Accuracy F-measure Accuracy
Coating (Yellow vs. White) 0.764 77.10% 0.801 80.37%
ZHENG (Hot vs. Cold) 0.642 65.00% 0.763 76.50%
HP Bacteria (Positive vs. Negative) 0.636 72.38% 0.713 71.97%
Gastritis patients (Superficial vs. Atrophic) 0.656 68.42% 0.702 71.05%
Cold ZHENG Patients (Superficial vs. Atrophic) 0.750 75.00% 0.761 76.67%
Hot ZHENG Patients (Superficial vs. Atrophic) 0.776 77.98% 0.845 84.40%
Superficial Patients (Hot vs. Cold ZHENG) 0.807 80.65% 0.839 83.87%
Atrophic Patients (Hot vs. Cold ZHENG) 0.782 78.50% 0.734 73.83%
Normal Tongue vs. Tongue with Coating 0.833 85.88% 0.861 86.26%
Normal group vs. ZHENG patients 0.834 84.68% 0.857 85.89%
Normal group vs. Hot ZHENG 0.808 81.11% 0.828 82.22%
Normal group vs. Cold ZHENG 0.750 75.00% 0.785 78.45%
Normal group vs. Superficial Patients 0.765 76.52% 0.811 81.06%
Normal group vs. Atrophic Patients 0.762 78.13% 0.837 83.85%

V. Conclusion and Future Work 
In this paper, we propose a novel color space based feature set for use in the clinical characterization of ZHENG using
various supervised machine learning algorithms. Using an automated tongue-image diagnosis system, we extract these
objective features from tongue images of clinical patients and analyze the relationship with their corresponding ZHENG
 data and disease prognosis (specifically gastritis) obtained from clinical practitioners. Given that TCM practitioners
usually observe the tongue color and coating to determine ZHENG (such as Cold or Hot ZHENG) and to diagnose
different stomach disorders including gastritis. We propose using machine learning techniques to establish the
relationship between the tongue image features and ZHENG by learning through examples.
The experimental results obtained demonstrate an excellent performance of our proposed system. Our future work
will focus on improving the performance of our system by exploring additional tongue image features that can be
extracted to further strengthen our classification models.We plan to explore ways to improve our methodology to
more accurately classify the ZHENGs such as including a preprocessing step of coating separation prior to the
feature extraction phase.Lastly, we plan to evaluate the classification of the other ZHENG types mentioned in
Section I.

Acknowledgements 
SL and TM are supported in part by the NSFC (No. 90709013).

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