Automatic Detection of Ringworm using Local Binary Pattern (LBP)

Abstract

In this paper we present a novel approach for automatic recognition of ring
worm skin disease based on LBP (Local Binary Pattern) feature extracted from
the affected skin images. The proposed method is evaluated by extensive
experiments on the skin images collected from internet. The dataset is tested
using three different classifiers i.e. Bayesian, MLP and SVM. Experimental
results show that the proposed methodology efficiently discriminates between a
ring worm skin and a normal skin. It is a low cost technique and does not
require any special imaging devices.

Full text

Page 1

Automatic Detection of Ringworm using Local Binary
Pattern (LBP)


Srimanta Kundu Nibaran Das*
Jadavpur University, Kolkata-32 Jadavpur University, Kolkata-32
srimantacse@yahoo.co.in nibaran@cse.jdvu.ac.in
*corresponding author


ABSTRACT
In this paper we present a novel approach for automatic recognition
of ring worm skin disease based on LBP (Local Binary Pattern)
feature extracted from the affected skin images. The proposed
method is evaluated by extensive experiments on the skin images
collected from internet. The dataset is tested using three different
classifiers i.e. Bayesian, MLP and SVM. Experimental results show
that the proposed methodology efficiently discriminates between a
ring worm skin and a normal skin. It is a low cost technique and
does not require any special imaging devices.
Index Terms— LBP, Ring worm, Bayesian, MLP and SVM.


Jadavpur University, Kolkata-32
mitanasipuri@gmail.com
Mita Nasipuri

1.
INTRODUCTION

Computer aided detection/diagnosis (CAD) [1] of medical
diseases is an active research field which includes analysis of digital
images of affected regions. A successful CAD system provides
useful information for the clinician‟s diagnostic support. But the
development of proper CAD system is a challenging mission.
Despite research efforts from different frontline research groups
over the last century, it has remained an active research avenue due
to the diversity as well as high complexity of the problems. Though
some systems have been developed to address several diseases, but
those are not sufficient enough. Most of the researches are going on
to identify different types of cardiac diseases, cancer etc which have
alarming effect on human existence. But pitifully very few
mentioned have been found to address skin related problems. Elif
Derya Ubeyli et al. worked on automatic detection of Erythro-
squamas disease. They used k-mean clustering [2] and neuro-fuzzy
inference [3]system for that purpose. Automatic detection of poultry
skin worm was implemented by Du Z, Jeong et al. They used the
concept of band selection of hyper spectral images [4] to implement
that. Automatic cancer detection was implemented by H.B.kerle et
al. using vector quantization for segmentation method. [5] Infrared
hyper spectral reflectance imaging was used by Welling wang et al.
for detection of sour skin disease in Vidalia sweet onions [6].
But still identification of proper skin disease is challenging enough
due to symptom similarities with that of others. Among different
skin diseases, Dermatophytosis or Ringworm, a clinical condition, is
caused by fungal infection. The funguses form a ring-shaped rash
outside the body and remain alive during the infection stage. There
are several worms involved to develop a ring worm. In general,
around twenty percent of the population is affected with ring worm
at any given moment. The disease is very much common among
sports persons and the persons living in a worm, humid climate
having direct contact with active lesions on someone else or having

weakened immunity system (due to having diabetes, leukemia, or
AIDS). Misdiagnosis and improper treatment of it may lead to tinea
incognito where funguses are spread out by far without any control.
Therefore, it is very important to identify the ring worm at an early
stage. But to the best of our knowledge, there is no CAD system yet
developed for recognition of ringworm automatically. From this
motivation, we have developed an automatic computer aided ring
worm detection scheme based texture patterns of different region of
skin. Hence, it is important to identify a set of discriminative
features strong enough to represent the proper textures of the skins.
In the present work we have introduced Local Binary Pattern (LBP)
[7] based texture features for recognition of ringworm. The texture
features of the skin are computed from the digital image of the skin
using LBP method. On the basis of these features, decision is made
whether the skin is affected with ringworm or not. We have
evaluated the disease based on the acquired images where the
presence of ring worm is positive or negative. For this purpose we
have used three different types of classifiers namely Bayesian
classifier, Multi Layer Perceptron (MLP) and Support Vector
Machine (SVM). And final decision is taken using majority voting
schemes.
The paper is organized as follows: Section 2 describes the LBP
and LBP histogram feature, and different classifiers. The experiment
setup and the result part are elaborately described in the section 3
and section 4 respectively. Section 5 gives the conclusion.

2.
PRESENT WORK

2.1 Local Binary Patterns(LBP)


LBP is a very powerful feature for texture classification. It was
first described by T. Ojala et al. [8][9]. Due to its gray scale and
rotation invariance property, this feature has been used successfully
in different domains of computer vision like face detection, facial
expression recognition, brain MR image analysis etc.
This operator is invariant against any monotonic transformation
of the gray scale of an image.LBP value of a particular pixel is
always calculated by considering the pixel property of its
neighbourhoods and this is the significance of using the term
“Local”. It is described later how this feature can be defined using
„0‟ and „1‟ only and ultimately form a 0-1 pattern, that‟s why it is
called “Binary Pattern”.
LBP can be defined by using a texture T in a local
neighbourhood of a pixel and the joint distribution of the gray levels
of P neighbourhood image pixels. Here the parameter used in the
pixel value may be its intensity value directly or it may be some
other feature like gradient of that image pixel as described in the

Page 2

paper [10].Normally all the description will be given here just by
considering the gray level of intensity of the pixels.

So texture T can be defined in the following way

T=t (nc, n0, n1, n2… nP-1); (1)

Where nc denotes the gray level of the centered pixel of the local
neighbourhood for which the LBP will be calculated. np
(i=0,1,2,…,P-1) corresponds to the gray values of equally spaced
pixels on a circle of radius R (R>0) that form a circularly symmetric
neighbour set. Here „P‟ is called angular resolution and „R‟ is called
spatial resolution.
If the coordinate of nc is (0, 0), then the coordinate point of ni is
given by

(-Rsin (2∏i/P), Rcos (2∏i/P))









Fig 1: Circularly symmetric neighbour sets (P:
angular resolution, R: spatial resolution)

Fig. 1 illustrates circularly symmetric neighbour sets for
various (P, R). From the equation (1) gray scale invariance can be
achieved by doing the following steps.

T=t (nc, n0, n1, n2… nP-1);
T=t (nc, n0- nc, n1- nc, n2- nc … nP-1- nc);

T≈t (nc) t (n0- nc, n1- nc, n2- nc … nP-1- nc);

T≈ t ((n0- nc), (n1- nc), (n2- nc) … (nP-1- nc)); step 3

In the first step the gray level of the centered pixel is subtracted from
all the pixels of neighbourhood without losing its own gray level
information. Since (ni -nc) is independent of the value of nc , we can
write the second expression as the product of two terms as shown in
the third expression above. We ignore the term t(nc), as shown in
step3, since gray level changes between the central pixel and the
local neighbourhood pixels are sufficient to represent a texture
pattern.
Now if a constant region is found in any portion of an image, the
differences are zero in all directions,whereas, for a spot, the
differences are high in all directions [9].
Signed differences (ni - nc) are not affected by changes in mean
luminance because if a pixel is affected by noise then there is a high
chance that its neighbourhood pixels will be affected by it more or
less in same manner. Therefore subtracting this value indirectly
removes the noise effect a little bit. Hence, the joint difference
distribution is invariant against gray-scale shifts. We achieve
invariance with respect to gray scale by considering just the signs
(either „+‟ or „–„and „0‟) of the differences instead of their exact
values: Hence it can be written as follows:

T:≈ t (sign(n0-nc),sign( n1- nc ),sign( n2- nc ) …sign( nP-1- nc));

Where sign(x) = 0
if x≤0
= 1 if x>0



step 1
step 2

So one binary pattern is calculated for a particular pixel and from
this texture definition we can very easily calculate the numeric value
(decimal) of LBP by the following formula:

LBPP,R =



Rotation Invariance:


Normally there are 2P values for a P-bit LBP pattern. For
example if we use P=4 then there are 16 possible LBPs we can get
ranging from 0000 to 1111.
If an image is rotated, then the neighbourhood pixels will be same
but the relative positions will be different, hence we will get
different LBP for the same pixel. So this operator is not rotation
invariant.


sign (ni-nc)2i)

(a) An image rotated anti clock wise by q0






b) Change in LBP due to this rotation
Fig 2: Example of rotation

For example, from the Fig 2 we can clearly see that LBP value for
the previous Pattern is (11001011)2 (starting from NW corner)
and after rotating it 900 anti-clockwise LBP value turns (00101111)2
So to make it rotation invariant we will again define the LBP
operator is defined in the following way.

LBP ri
P, R

Where ROR(x,i) performs a circular bit-wise „i‟ times right shift on
the P bit number x. In terms of image pixels, this simply
corresponds to rotating the neighbour set clockwise so as to get
maximum number of zeros in the beginning of the binary string [9]
„min‟ operator will just take the minimum decimal values from
different patterns. Instead of using min we may use „max‟ (just
opposite of the min) operator. Then also our goal of making it a
rotation invariant will be achieved. This operator is also known as
LBPROT as designed in the paper [7].
Uniform Pattern:

Sometimes this rotation invariant feature cannot give good
discrimination [9]. Again among this rotation invariant patterns
some patterns are dominant and are fundamental properties of
texture. We call these fundamental patterns “uniform” as they have
= min {ROR (LBPP, R ,i)} | i= 0,1, ,P-1

Page 3

one thing in common, namely, uniform circular structure that
contains very few spatial transitions.
A binary pattern is called “uniform” if it contains at most 2 spatial
transitions (bitwise 0/1 changes). Based on this uniformity concept,
a new LBP value ( LBPriu
bit values of a rotation invariant binary pattern if it is uniform, or a
miscellaneous label P+1 can be assigned if it is non-uniform.
The uniform property is defined as follows:

LBP riu2


= (P+1)
Otherwise Where

U (LBPP,R)=|sign(nP-1-nc)-sign(n0-nc)|
+


2.2 LBP Histogram

In statistical analysis, a histogram is a representation of the
distribution of different parameters in an event. This idea has been
extended to image analysis. For example, in case of digital images, a
colour histogram represents the number of pixels corresponding to
each colour / intensity value that spans the image's colour space.
Similarly we can store the LBP information of a particular
image in histogram form and then analyze that histogram using
some statistical operators for example Chi-square, Log statistic etc
or we can apply some soft computing tools on it like SVM, MLP etc.
If we just consider the normal LBP then it will have 2P no of bins in
the histogram. If LBP with uniform property is used then there will
be P+2 bins (0 to P+1).


P,R) can be computed by summing up the
P, R =

sign ((np-nc)) if U (LBPP,R) ≤ 2

|sign ((np-nc)) - sign ((np-1-nc))|


Fig 3: Histogram formation from LBP pattern



2.3 Different classifiers

2.3.1 Naive Bayes Classifier

The Naive Bayes Classifier technique[11]is based on the so-called
Bayesian theorem and is particularly suited when the dimensionality
of the inputs is high. Despite its simplicity, Naive Bayes can often
outperform more sophisticated classification methods. Bayesian
classification is an algorithm which allows us to categorize inputs
probabilistically. Here in this experiment normal naive Bayesian
classifier is used.


Abstractly, the probability model for this classifier is a
conditional model

P (C|F1….,, Fn)
over a dependent class variable C with a small number of outcomes
or classes. All the class elements have „n‟ number of features
namely F1, F2,..Fn. If the value „n‟ is not so large then this classifier
provides good accuracy but on the contrary if it is so then the
performance degrades. Even if the features take a large range of
values then also its efficiency may reduce.
Using Bayes' theorem, we write
P (C|F1, F2, …..Fn) =



In normal way the above equation can be written as:
Posterior = (prior X likelihood)/ (evidence)
In practice we are only interested in the numerator of that fraction,
since the denominator does not depend on C and the values of the
features Fi are given, so that the denominator is effectively constant.
The numerator is equivalent to the joint probability model
P(C,F1 , …… , Fn)
which can be rewritten as follows, using repeated applications of the
definition of conditional probability[11].






p(C, F1, ……, Fn)
=p(C) P (F1 , ……, Fn|C)
=p(C) p (F1|C) p (F2…Fn|C,F1)
=p(C) p (F1|C) p (F2|C, F1) p (F3, F4…Fn|C,F1,F2)
=p(C) p (F1|C) p (F2|C, F1)…p(Fn|C,F1,F2.Fn-1)
Now Naïve conditional independence assumptions come into play.
Assume that each feature Fi is conditionally independent of every
other feature Fj for i≠j. This means that p (Fi|C,Fj)=p(Fi|C); therefore
the equation is
=p(C) p (F1|C) p (F2|C)…p (Fn|C)
So ultimately we get




p(C, F1, ……, Fn)=



p(C) p (Fi|C)
= p(C)

p (Fi|C)

belongs to a particular class, the following probability density
function is used.

For calculating the probability value of each numeric feature

Where „µ‟,‟σ‟ are the mean and standard deviation of the training
samples of a particular feature particular class respectively. ‟x‟ is the
feature value of the testing input -pattern.


2.3.2 Multi-Layer Perceptron (MLP)

An MLP is a feed-forward layered network of artificial neurons.
Each artificial neuron in the MLP computes a special function on the

Page 4

weighted sum of all its inputs. The function may be a sigmoid or a
polynomial or a hyperbolic tangent or a simple linear function. In
our case we have used sigmoid function for each node in each layer.
An MLP[12] consists of one input layer, one output layer and a
number of hidden or intermediate layers. The output from every
neuron in a layer of the MLP is connected to all inputs of each
neuron in the immediate next layer of the same. The numbers of
neurons in the input and the output layers of an MLP are chosen
depending on the problem to be solved. For example in our
experiment number of nodes in the input side is 160 that is equal to
the number of features and in the output side it is 2 that is the
number of classes. The number of neurons in hidden layer is
determined by a trial and error method at the time of its training. An
ANN requires training to learn an unknown input-output relationship
to solve a problem.
The MLP classifier designed for the present work is
trained with the Back Propagation (BP) algorithm. It minimizes the
sum of the squared errors for the training samples by conducting a
gradient descent search in the weight space. The number neurons in
a hidden layer in the same are also adjusted during its training.

2.3.3 Support Vector Machine

Recently Support Vector Machine has been used successfully for
pattern recognition and regression tasks formulized under the
concept of structural risk minimization rule [13] It was mainly
designed for binary classification, in order to construct an optimal
hyper-plane, to maximize the margin of separation between the
negative and positive data set. Although, SVM is used for two class
pattern classification problem but multi-class problem can also be
solved by extending the binary classification to multi class
classification.
For the Support Vector Machine classifier, an open source
software LibSVM tool is used. In general, a classification task
usually involves with training and testing data which consist of some
data instances. Each instance in the training set contains one “target
value” (class labels) and several “attributes” (features). The goal of
SVM is to produce a model which predicts target value of data
instances in the testing set which are given only the attributes.
Before considering the data directly from the linearly scaling each
attribute to the range [-1, +1] or [0, 1].
Given a training set of instance-label pairs (xi, yi);
i =1,…., N; where xi Є Rn and y Є{1, -1} , the support vector
machines (SVM) require the solution of the following optimization
problem:


min (w,w0,€,)
½ wTw +C

Subject to yi(wTxi+b)≥1- €i ,
€i≥0


It's not difficult to generalize this linear program to the nonlinear
case replacing xi with a nonlinear function M(xi):

min (w,w0,€,)
½ wTw +C

Subject to
yi(wT M(xi)+b)≥1- €i ,

€i≥0

Here expression for maximum margin is given as [13]















The above illustration is the maximum linear classifier with the
maximum range. Here training vectors xi are mapped into a higher
dimensional space by the function M. Then SVM finds a linear
separating hyper plane with the maximal margin in this higher
dimensional space.
Furthermore, k(xi,xj) ≡M(xi)TM(xj) is called the kernel function.
In the current work, we have used the RBF kernel and polynomial
kernel and the corresponding expressions are given below:




First one is for RBF and the next one is for polynomial.

3.
EXPERIMENTAL SETUP

3.1 Preparation of database

For the experiment we don‟t have any direct database that‟s why
different images of ringworm skin and normal skin have been
collected from internet. These collected images are not directly used
in the experiment. In order to prepare database of diseased skin and
normal skin we have undertaken a sequence of steps. The processing
steps of the images are described later in the same section. The
database can be downloaded from the link www.cmaterju.org on
request.
A total of 140 images are used for the experiment. 70 images
are of ringworm affected skin and remaining 70 images are of
normal skin. We have trained different classifiers using 50% of
images from the ring worm positive and negative image sets. Thus
our training set is formed with 70 images with equal number of
images from each class.
The remaining images from the two sets are used as test data set for
our experiment.

3.1.1. Preprocessing steps

In the processing steps we have done the following things.

i.
Resizing all the images. All the images are of (144 x
144) pixels. For this we have used fotoxx software in
Linux (Ubuntu) OS.
ii.
Cropping the specified zones of the diseased skin.
iii.
Converting those images into gray images. Here
„.pgm‟ format is used for the actual experiments.



3.1.2. Decomposition of skin images


After the pre-processing, LBP histogram is calculated. For this,
different region based approach is also considered. Each image is
decomposed into 16 zones or regions R0, R1 ...R15. and each region
is assigned equal weight because unlike face recognition, here all the
regions are of equal importance. Here P is chosen as 8 and R as 1.
2
i
1
margin argmin ( )
D

x
argmin

x
d
i
D
b
d
w




x w
x

Page 5

For each region 10 histogram features are generated. Thus total of
160 features (16x10) are calculated for each texture pattern.
So normalized feature vector is itself the region feature vector.
By concatenating the entire region feature vector together, global
information of the entire image can be obtained.










Fig 4: sample of Ringworm images







Fig 5: sample of normal skins



4.
RESULT


For the present work, Multi Layer Perceptron(MLP) with one hidden
layer is chosen. This is mainly to keep the computational
requirement of the same, low without affecting its function
approximation capability . To design the MLP for classification of
ring worm disease, Back Propagation (BP) learning algorithm with
learning rate () = 0.8 and momentum term () = 0.7 is used here
for training the classifier with varying number of neurons in its
hidden layer. On the other hand, to implement SVM classifier we
used an open source software LibSVM tool [14] .Among different
existing kernels in LIBSVM we were used Radial basis Function
(RBF) kernel with 0.5 gamma value.
In case of Bayesian classifier no parameters has to be set
for the experiment externally. Just the mean and standard deviation
of each individual feature for the training data has been calculated
using the standard formula. After that for each testing pattern the
probability for belonging to both classes was calculated and based
on this value all the patterns are classified.
We have used 10 fold cross validation techniques to
validate the database. The results are shown in Table 1. From the
table we have found that MLP classifier gives maximum average
success rate of 95.71% with 13.55 standard variations. Whereas,
Bayesian classifier gives lowest average success rate of 70% with
17.10% standard deviation. SVM provides 74.28% success rate for
these
We have applied the above methodologies on the designed
dataset described in section 3. After training the classifiers we have
used the test set to get actual recognition. Table no 2 shows the
result of different classifiers on test data. From the Table no 2 we
have found 72.85%, 94.28%, 90% success rate using Bayesian, MLP
and SVM classifier respectively. And using majority voting scheme,

we have obtained 91.42% success rate. The Fig 6 shows the number
of classified data by different classifiers and their intersections and
unions. Fig 7 shows some samples of misclassified data.


















M1
2

B: set of skin images that are classified correctly by Bayesian
classifier
M: set of skin images that are classified correctly by MLP
classifier
S: set of skin images that are classified correctly by SVM
classifier
B1: B - ( (B∩M∩S) U(B∩M)U( B∩S))
S1: S - ((B∩M∩S) U(M∩S)U( B∩S))
M1:M - ((B∩M∩S) U(B∩M)U( M∩S))


Fig 6 Distribution of correctly and incorrectly classified
samples by Bayesian, MLP and SVM







(a)
(b) (c) (d) (e)

Fig 7: (a),(b)Ringworm infected skins classified as Ringworm
negative;(c),(d),(e) Normal skin images classified as Ringworm
positive







B∩M∩S
49
B∩S
0
M∩S
14
B∩M
1
B1
1
S1
0
B: Bayesian
S: SVM
M:MLP