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Computerized Medical Imaging and Graphics 34 (2010) 160–166
Contents lists available at ScienceDirect
Computerized Medical Imaging and Graphics
journal homepage: www.elsevier.com/locate/compmedimag
A discrimination method for the detection of pneumonia using chest radiograph
Norliza Mohd. Noor a,∗ , Omar Mohd. Rijal b , Ashari Yunus c , S.A.R. Abu-Bakar d
a
Department of Electrical Engineering, College of Science and Technology, UTM International Campus, Universiti Teknologi Malaysia, Jalan Semarak,
54100 Kuala Lumpur, Malaysia
b
Institute of Mathematical Sciences, University Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia
c
Institute of Respiratory Medicine, Jalan Pahang, 50586 Kuala Lumpur, Malaysia
d
Faculty of Elect. Eng., Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia
a r t i c l e
i n f o
Article history:
Received 3 April 2009
Received in revised form 5 August 2009
Accepted 17 August 2009
Keywords:
Detection
Texture measures
Pneumonia
Principal component analysis (PCA)
Discriminant analysis
Digital chest X-ray
a b s t r a c t
This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest
X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then
multiplied by the orthogonal matrix Q2 . The first two elements of the transformed vectors were shown
to have a bivariate normal distribution. Misclassification probabilities were estimated using probability
ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia
by constructing probability ellipsoids or discriminant function using maximum energy and maximum
column sum energy texture measures where misclassification probabilities were less than 0.15.
© 2009 Elsevier Ltd. All rights reserved.
1. Introduction
The chest X-ray of a patient is frequently used as the initial indicator of pneumonia mainly due to cost consideration despite the
existence of better and more expensive medical imaging modalities [1]. Depending on the experience of the medical practitioner,
types of lung diseases and their stage of infection may be difficult
to identify from the chest X-ray.
Respiratory infections are the most common of all infections.
The most serious respiratory infection, pneumonia, shows the highest mortality rate of all infectious diseases and is the sixth leading
cause of death in people over 65 years of age [2]. In 2004, 58,564
people died of pneumonia in the United States of America [3].
Pneumonia is a serious infection or inflammation of the lungs.
Pneumonia can have over 30 different causes which include various chemicals, bacteria, viruses, mycoplasmas and other infectious
agents such as pneumocystis (fungi). Symptoms of pneumonia
include fever, coughing, shortness of breath, chest pain and loss
of appetite [4].
Texture is an important item of information that humans use
in analyzing a scene [5]. Textures may be considered as a global
∗ Corresponding author. Tel.: +60 3 2615 4589/19 327 4854; fax: +60 3 2615 4315.
E-mail addresses: [email protected] (N.Mohd. Noor), [email protected]
(O.Mohd. Rijal), [email protected] (A. Yunus), [email protected] (S.A.R. AbuBakar).
0895-6111/$ – see front matter © 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.compmedimag.2009.08.005
repetition of a basic pattern defined over a local region, however,
the basic pattern is difficult to define. This problem has led to the
creation of several measures of textures, and the choice of texture
measure is closely related to the issue of visual perception [6].
There are studies involving texture features extracted from
wavelet coefficients. Unser [7], Fauzi and Lewis [8,9] and Sengur et
al. [10] used mean of energy as the wavelet-based texture features.
Boukerroui et al. [11] used angular second moment and correlation
as the texture feature to extract feature and segmentation for breast
tumors in ultrasonic imaging. Agani et al. [12] used four texture
features namely, entropy, contrast, angular second moment and
inverse difference moment derived from the co-occurrence matrix
in diagnosis of myocardial infarction tissue from echocardiography images and retrieval in small dimension images. Muneeswaran
et al. [13] used five texture measures which are Norm-2 energy,
Norm-1 energy, standard deviation of the energy, average residual and entropy for rotational and scale invariant feature set for
textural image classification.
Kazuhiko et al. [14] developed computer aided diagnosis for
interstitial pneumonia using chest X-ray with relatively good result.
Their study used second order statistics in distinguishing normal images and interstitial pneumonia images. The second order
statistics used in this method is derived from the co-occurrence
matrix and run-length matrix and the features obtained from these
matrices attempts to quantify microscopic variation of density.
Recently, Oliveira et al. [15] has developed a computer aided diagnosis method using Haar wavelet to extract features from chest
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N.Mohd. Noor et al. / Computerized Medical Imaging and Graphics 34 (2010) 160–166
161
radiographs and distance-dependent weighting for image classification in detection of childhood pneumonia with high sensitivity
and specificity. They have designed a prototype system, named
Pneumo-CAD that is able to detect pneumonia present against
pneumonia absent in chest X-ray images to aid pneumonia diagnosis in children.
In this paper a novel discrimination method to detect lobar
pneumonia using a digitized chest radiograph is proposed. The discrimination method uses texture measure. Each patient therefore
is represented by a vector of texture measures. These vectors of
texture measures were found to be normally distributed. Twodimensional probability ellipsoids and appropriate discriminant
functions estimate the following error probability;
and
˛ = P(Type 1 Error) = P(PNEU|NL),
ˇ = P(Type 2 Error) = P(NL|PNEU),
for a selected texture measure.
Fig. 1. (a) Chest X-ray of a pneumonia patient, and (b) a subset image of the infected
area.
The Institute of Respiratory Medicine, Kuala Lumpur.
2. Materials and methods
This study involved collaboration with the Institute of Respiratory Medicine (IPR), Malaysia, which is the Malaysian national
referral centre for respiratory diseases. Cases that arrived at the
IPR may be considered a random sample since an individual case
may come from any of the Malaysian hospitals or clinics. The chest
X-ray films from IPR were then digitized into DICOM format using
the X-ray Film Scanner Kodak LS 75 with the following specifications: pixel spot size of 100 ␮m, 12 bit per pixel, image size of
2016 × 2048 pixels. An example of a digitized X-ray film is shown
in Fig. 1.
Each of the ROI for a given image was subjected to the twodimensional Daubechies wavelet transform as shown in Fig. 2
[16,17]. The wavelet transform convert the image into four subsets, labeled LL, LH, HL and HH representing the trend, horizontal,
vertical and diagonal detail coefficients.
The twelve texture measures considered were;
(i) Mean Energy, E =
1
N
j
1
N2
j
(iii) Contrast =
j
where =
1
N2
j
(vi) Standard
1
N2
j
k
(iv) Homogeneity, H =
|Cjk |2
k
|Cjk |2 log |Cjk |2
j
(Cjk − )
2
k
Cjk
k
deviation
(|Cjk |2 − )
2
of
where =
STDE =
energy,
1
N2
k
j
|Cjk |2
k
(vii) Maximum wavelet coefficient value, max = max(Cjk )
(viii) Minimum wavelet coefficient value, min = min(Cjk )
⎛
(ix) Maximum value of energy, Emax = max ⎝
2.1. Texture measures
(ii) Entropy = −
1
N2
(v) Standard deviation of value, STDV =
j
⎞
|Cjk |2 ⎠
k
(x) Maximum row sum energy
(xi) Maximum column sum energy
(xii) Average number of zero-crossings
where Cjk is the element of sub-image (say, LL) found in row-j and
column-k [18,19]. Hence, twelve texture measures in each of LL, LH,
HL and HH yields 48 descriptors or features, u, that will be used to
detect pneumonia.
2.2. Modified principal component method
k
2
(j − k) Cjk
j
k
Cjk
1+|j−k|
A sample of thirty images were concurrently read and interpreted for the presence of lobar pneumonia with no secondary
ailment (PNEU) by two independent pulmonologists who are
trained according to the World Health Organization (WHO) guideline [20,21] and the affected region (ROI) was identified (Fig. 1).
Fig. 2. (a) Region of interest, (b) the transformed image where four image subset was formed.
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This process was repeated for a sample of another thirty individuals labeled as pneumonia-absent group or normal (NL) who came
for a routine medical check-up. These sixty cases (30 cases each for
PNEU and NL) will be used as the control group. Another forty cases
(20 cases each for PNEU and NL), were similarly obtained and will
be used as the test group to estimate misclassification probabilities.
The standard principal component method is generally used for
purposes of reducing dimension in multivariable problems. Geometrically the principal component transformation merely rotates
the data (vectors) such that the Euclidean distance between the
vectors is unchanged. This rotation is achieved by means of an
orthogonal transformation. Unfortunately, if there exist two over-
Fig. 3. 1st two component of pseudo-PC scatter plot for PNEU ( ) and NL( ).
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N.Mohd. Noor et al. / Computerized Medical Imaging and Graphics 34 (2010) 160–166
Fig. 3. (Continued ).
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N.Mohd. Noor et al. / Computerized Medical Imaging and Graphics 34 (2010) 160–166
lapping groups achieving reduction in dimension does nothing to
increase the separation of the two groups. A modified principal
component (ModPC) method is now proposed in order to investigate the possibility of both reduction in dimension and increased
separation between groups. This proposed method is applied
to the problem of differentiating pneumonia cases from normal
lung.
The data used were divided into two sets (u1 , u2 , · · ·, u60 ) is the
control data set and (u61 , u2 , · · ·, u100 ) is the test data set.
Let u1 , u2 , · · ·, u30 where u ∈ G1 represent the texture measures
for pneumonia samples and u31 , u32 , · · ·, u60 where u ∈ G2 represent the texture measures for normal lung samples. The main
problem of the ModPC method is the choice of an orthogonal transformation. Let M be an orthogonal matrix such that
ur is transformed to u∗r = Mur (r = 1, · · ·, 100).
1
Let n−1
Sj be the estimate of the covariance matrix for group Gj (j = 1,
2). For example,
30
S1 =
(ur − u)(ur − u)
T
where u =
r=1
(u1 + u2 + · · · + u30 )
30
and
Recall
G(v) = ln
g1 (v)
g2 (v)
where g1 (v) denote the probability distribution for PNEU, say
N2 ( , ˙1 ), and g2 (v) denote the probability distribution for
1
NL, say N2 ( , ˙2 ). The equality of covariance matrices was
2
tested using the Box’s Test [22], and if ˙1 = ˙2 , G(v) is the linear discriminant function (LDF), otherwise, G(v) is the quadratic
discriminant function (QDF). The number of times G(v) < 0 for
vj (j = 61, . . ., 80) gives an estimate of ˇ. Henceforth ˛ is similarly
derived.
For both of LDF and QDF, the criterion G(v) < 0 is true assuming
equal a priori probabilities and costs of misclassification d(i|j) which
is the cost of misclassifying observation-j (i = 1, 2 and j = 1, 2).
2.3. Testing for normality
Given v1 , v2 , · · ·, vn ∈ 2 . The statistics v =
n
(vj − v)(vj − v)
T
(v1 +···+vn )
n
and Sv =
T
and yj = (vj − v) ((n − 1)Sv −1 )(vj − v) for j = 1,
j=1
S2 is similarly defined for u31 , u32 , · · ·, u60 .
The spectral decomposition of the estimated covariance matrix are,
1
S1 = Q1 1 Q1T for G1
n−1
and
. . ., n were calculated. If vj is normally distributed then yj (j = 1, . . .,
n) must come from a chi-squared distribution with two degrees of
freedom. Effectively, testing normality of v1 , v2 , · · ·, vn is equivalent
to testing whether yj (j = 1, . . ., n) comes from a chi-squared distribution using the Kolmogorov–Smirnov test [23]. In all cases studied
the Kolmogorov–Smirnov test confirms that yj has a chi-squared
distribution, henceforth, indicating that vj is bivariate normal.
2.4. Confidence region of the first two components of v
1
S2 = Q2 2 Q2 T for G2
n−1
where n = 30, Qj (j = 1, 2) is the appropriate matrix of eigenvectors,
and j (j = 1, 2) is the corresponding diagonal matrix of eigenvalues. Henceforth, the matrix M may be chosen in two ways, namely
M = Q1 or M = Q2 .
For a selected M matrix, take the first two components of
ur ∗ (r = 1,. . .,100) which explain at least 90% of the variability,
relabel it as vr (r = 1,. . .,100) and perform the following: for the
(v +···+v )
vectors v1 , v2 , · · ·, v30 , ∈ 2 calculate the statistics v1 = 1 30 30
and Sv1 =
(b) Estimation of ˛ and ˇ from discriminant function:
30
T
(vj − v1 )(vj − v1 ) . The vectors v1 , v2 , · · ·, v30 were
j=1
found to be bivariate normal. Henceforth the pneumonia ellipsoid
T
(v − v1 ) ((n − 1)Sv1 −1 )(v − v1 ) = c was drawn where c was selected
from a standard chi-square table. Further, the estimate of g1 (v),
which is the probability distribution for G1 is also obtained.
The above was repeated for v31 , v32 , · · ·, v60 yielding the NL
ellipsoid and the corresponding estimate of g2 (v) which is the probability distribution for G2 . Finally the estimate of the discriminant
function G(v) = ln
g1 (v)
g2 (v)
may be derived.
The two error probabilities ˛ and ˇ may be estimated by using
the test set v61 , v62 , · · ·, v80 from G1 and v81 , v82 , · · ·, v100 from G2 in
two ways;
(a) Estimation of ˛ and ˇ from the probability ellipsoid:
(i) The number of times vj (j = 61, . . ., 80) falls into the NL ellipsoid gives an estimate of ˇ.
(ii) The number of times vj (j = 81, . . ., 100) falls into the PNEU
ellipsoid gives an estimate of ˛.
A 2D plot of the first two components of v together with its corresponding approximate confidence region (an ellipse) was used
to investigate clustering of the two groups of individuals. If two
clusters of points are discovered on the 2D plot and each cluster
is contained in a separate confidence ellipse then the texture measures used (u) is regarded as a useful feature for discrimination. The
95% probability ellipsoid was drawn for each group, PNEU and NL
(see Fig. 3).
Since v is bivariate normal, therefore a value for c may be
selected from standard chi-squared tables. The quadratic form,
T
for example, (v − v1 ) ((n − 1)S1 −1 )(v − v1 ) is approximately a chisquared random variable where the approximation is considered
good if n ≥ 25 [22].
3. Results
When all the vectors of texture measures were multiplied by Q2
(obtained from NL data), a modified principal component method
for the detection of pneumonia gave at most misclassification probabilities of 0.15. Given that Q2 was selected, the probability ellipsoid
may be used as a graphical method of detection and the discriminant functions used as a formal detection method. The selected
texture measures to be used as features for purposes of discrimination are maximum energy and maximum column sum energy.
The selection of the type of texture measure and the matrix
Qj (j = 1, 2) for the probability ellipsoid is given in Table 1 whilst
Table 2 gave similar results using the discriminant function.
Table 1 clearly shows that using Q1 gave large ˇ errors, whilst
using Q2 , maximum energy and maximum column sum energy
showed acceptable levels of ˛ and ˇ (≤0.15). It is of interest
to note that using all twelve features, resulted in ˛ = 0.20 and
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N.Mohd. Noor et al. / Computerized Medical Imaging and Graphics 34 (2010) 160–166
Table 1
Estimated misclassification probabilities using ellipsoids.
Texture measures
Mean of energy
Entropy
Std. dev. of intensity value
Std. dev. of energy
Maximum value
Minimum value
Maximum Energy
Max. row sum energy
Max. column sum energy
Zero-crossing
Contrast
Homogeneity
All 12 features
Q1 (PNEU ellipsoids)
Q2 (NL ellipsoids)
˛
ˇ
˛
ˇ
0
0
0
0
0.05
0.05
0
0
0
0
0
0
0
0.45
0.65
0.6
0.45
0.7
0.65
0.6
0.65
0.6
0.3
0.75
0.7
0.7
0.55
0.45
0.6
0.55
0
0.65
0.15
0.1
0.15
0.15
0.05
0.05
0.2
0.2
0.45
0.65
0.2
0.25
0.25
0.15
0.2
0.1
0.85
0.2
0.4
0.25
˛ = P(Type I Error) = P (PNEU|NL). ˇ = P(Type II Error) = P (NL|PNEU)
Table 2
Estimated misclassification probabilities from LDF/QDF.
Texture measures
Mean of energy
Entropy
Std. dev. of intensity value
Std. dev. of energy
Maximum value
Minimum value
Maximum energy
Max. row sum energy
Max. column sum energy
Zero-crossing
Contrast
Homogeneity
All 12 features
Q1 (PNEU)
Q2 (NL)
˛
ˇ
˛
ˇ
0.8000
0.3000
0.0500
0.9000
0.1000
0.6500
0.1000
0.1000
0.1000
0.0500
0.1500
0.9000
0.2500
0.1000
0.4000
0.3500
0.0500
0.0000
0.2500
0.1000
0.1500
0.1000
0.7500
0.1500
0.0000
0.0000
0.5000
0.3500
0.0500
1.0000
0.1000
0.7500
0.1500
0.1000
0.1500
0.4500
0.8500
0.9000
0.2000
0.2500
0.3000
0.3000
0.0500
0.1500
0.2000
0.1000
0.1500
0.1000
0.6000
0.1000
0.0000
0.1000
˛ = P(Type I Error) = P (PNEU|NL). ˇ = P(Type II Error) = P (NL|PNEU).
ˇ = 0.25.
Table 2 shows that when using maximum value, maximum
energy, maximum row sum energy, maximum column sum energy,
without the need to choose between Q1 and Q2, both ˛ and ˇ are
less than 0.15.
A general result that could be summarized from both
Tables 1 and 2 is that both probability ellipsoids and discriminant
function can be simultaneously used if Q2 and maximum energy
and maximum column sum energy texture measure were selected.
165
The ˛ and ˇ errors are used as indicators of group separation.
Estimates of ˛ and ˇ were obtained by counting the number of test
data being misclassified. The use of discriminant functions is to be
preferred over the use of probability ellipsoids, especially in the
case where the data is normally distributed.
Fixing a ceiling of 0.15 for both ˛ and ˇ shows that the matrix Q2
allows the combined use of the probability ellipsoids and discriminant function, when maximum energy and maximum column sum
energy texture measures are considered. In other words, the probability ellipsoids could provide a first estimate of misclassification
probabilities graphically whilst the discriminant function provides
the final optimal detection procedure when using Q2 .
Texture measures are widely applied [8–15], but only [15]
appear to use texture measure for the detection of pneumonia.
However [15], studied detection of childhood pneumonia using
mean of energy as the texture measure with sensitivity (100%) and
specificity (80%) being considered as measures of performance. In
contrast this study focuses on the detection of pneumonia for adults
using statistical methods where the Type I Error and Type II Error
are considered as measures of performance.
The proposed detection method may only be used to differentiate PNEU from NL. In general extra information may be derived
from the images, for example, the possible existence of pulmonary
tuberculosis or lung cancer. There is a need to reselect the orthogonal transformation when the detection of the other diseases is of
interest.
5. Conclusion
This study proposes a novel statistical method for the detection of pneumonia when texture measures extracted from digital
images of chest X-rays are used as feature vectors in a discrimination procedure. The texture measures maximum energy and
maximum column sum energy when used as feature vectors in the
proposed discrimination method yields low misclassification probabilities not exceeding 0.15. If the matrix Q2 is applied together
with the maximum energy and the maximum column sum energy
texture measures, the probability ellipsoids may be used graphically for initial data exploration and the LDF or QDF used as formal
methods of detecting pneumonia.
Acknowledgements
We would like to acknowledge the contribution from The Institute of Respiratory Medicine, Kuala Lumpur. This research was
funded under an E-Science Fund from the Ministry of Science, Technology and Innovation and Universiti Teknologi Malaysia.
4. Discussion
Thirty cases each for PNEU and NL constituted the control group
whilst twenty cases each for PNEU and NL formed the test group. For
each of these 100 cases (images), a region of interest was identified
and consequently the Daubechies transform was applied yielding
appropriate vectors of texture measures.
Each of the 100 vectors of texture measures were subjected to an
orthogonal transformation in particular the matrix Q2 was applied.
Henceforth, probability ellipsoids and discriminant function were
constructed from the control group data. The ˛ and ˇ errors were
in turned estimated with the test data.
Two probability ellipsoids of the modified principal components
that are both compact with minimal overlapping are regarded as
showing significant separation. A compact probability ellipsoid is
one in which all points are within the ellipsoid. Fig. 3 shows that
every pair of ellipsoids (for a given texture measure) appears to
have the same degree of overlap.
References
[1] Schilham AMR, Ginneken B, Loog M. A computer aided diagnosis system for
detection of lung nodules in chest radiographs with an evaluation on a public
database. Med Image Anal 2006;10:247–58, doi:10.1016/j.media.2005.09.003.
[2] Centers for Disease Control and Prevention. National Center for Health Statistics: Preliminary Data for 2005. USA; September 2007.
[3] Improving Life, One Breath at a Time. Lung Disease Data: 2008. New York:
American Lung Association; 2008: pp. 68.
[4] Karetzky M, Cunha BA, Brandstetter RD. The Pneumonias. New York: SpringerVerlag; 1993.
[5] Alparone L, Benelli G, Vagniluca A. Texture-based analysis techniques for the
classification of radar images. IEEE Proc 1990;137(4):276–82 (Pt.F.).
[6] Levine MD. Vision in Man and Machine. New York: McGraw-Hill; 1985. p. 448.
[7] Unser M. Texture classification and segmentation using wavelet frames. IEEE
Trans Image Proc 1995;4(11):1549–60.
[8] Fauzi MFA, Lewis PH. Automatic texture segmentation for content-based image
retrieval application. Pattern Analysis Application. Springer 2006;9:307–323.
[9] Fauzi MFA, Lewis PH. Texture-based image retrieval using multiscale subimage matching. In: Proc. of IS & T/SPIE 15 Ann. Symp. Electronic Imaging:
Image and Video Comm. and Processing, SPIE vol. 5022. 2006. p. 407–16.
Author's personal copy
166
N.Mohd. Noor et al. / Computerized Medical Imaging and Graphics 34 (2010) 160–166
[10] Sengur A, Turkoglu I, Ince MC. Wavelet packet neural networks for texture
classification. Expert Syst Appl 2007;32:527–33.
[11] Boukerroui D, Basset O, Guerin N, Baskurt A. Multiresolution texture based
adaptive clustering algorithmn for breast lesion segmentation. Eur J Ultrasound
1998;8:135–44.
[12] Agani N, Abu-Bakar SAR, Salleh SHS. Texture analysis and classification of
echocardiography images for diagnosis of myocardial infarction tissue and
retrieval in small dimension images. In: Proc. of 6th Asian Control Conference,
Bali, Indonesia, July 18–21. 2006. p. 401–6.
[13] Muneeswaran K, Ganesan L, Arumugam S, Saoundar KR. Texture classification
with combined rotation and scale invariant wavelet features. Pattern Recognit
2005;38:1495–506.
[14] Kazuhiko T, Tetsuo S, Naoki K, Hiroyuki T, Kazuhiko I, Tomoharu K, Tomoyuki
K, Toshiteru S, Ichiro F. A basic study of computer-aided diagnosis system for
interstitial pneumonia by chest X-ray image. Bull Nagaoka Univ Technol Japan
2003;25:99–103.
[15] Oliveira LLG, e Silva SA, Ribeiro LHV, de Oliveira RM, Coelho CJ, Andrade ALSS.
Computer-aided diagnosis in chest radiography for detection of childhood
pneumonia. Int J Med 2007, doi:10.1016/j.ijmedinf.2007.10.010.
[16] Daubechies I. Ten Lectures on Wavelets. Pennsylvania: SIAM; 1992.
[17] Walker JS. A Primer on Wavelets and Their Scientific Applications. Boca Raton:
Chapman & Hall/CRC; 1999.
[18] Gonzalez RC, Woods RE. Digital Image Processing. Reading: Addison-Wesley;
1992. p. 510.
[19] Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision.
Pacific Grove: International Thomson Publishing; 1998. p. 652.
[20] WHO. Standardization of Interpretation of Chest Radiographs for the Diagnosis
of Pneumonia in Children. Geneva: Department of Vaccines and Biologicals;
2001.
[21] Cherian T, Mulholland EK, Carlin JB, Ostensen H, Amin R, de Campo M, et
al. Standardized interpretation of pediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies. Bull World Health Organ
2005;83(5):353–9.
[22] Johnson RA, Wichern DW. Applied Multivariate Statistical Analysis. sixth ed.
New Jersey: Prentice Hall; 2007. pp. 149–209.
[23] Hogg RV, Tanis EA. Probability and Statistical Inference. seventh ed. New Jersey:
Prentice Hall; 2006. p. 576.
Norliza Mohd Noor is currently pursuing her Ph.D. at the Universiti Teknologi
Malaysia (UTM) under the supervision of Assoc. Prof. Dr Syed Abdul Rahman Syed
Abu Bakar. She received her B.Sc. Electrical Engineering from Texas Tech University
of Lubbock, Texas and Master of Electrical Engineering from UTM in 1985 and 1996
respectively. She currently attached to the Dept. of Electrical Engineering, College of
Science and Technology, UTM International Campus, Kuala Lumpur. She is a senior
member of IEEE
Omar Mohd Rijal received his B.Sc. Maths (Operational Research) from New University of Ulster, Ireland (1979) and Ph.D. (Applied Statistics) from University of
Glasgow, UK in 1984. He has been attached to the Institute of Mathematical Sciences, University of Malaya since 1984 and was appointed as the associate professor
in 1998. His research interests are applied statistics, image and data analysis for
medical, industrial and remote sensing applications
Ashari Yunus is a Consultant Respiratory Physician at the Institute of Respiratory
Medicine, Kuala Lumpur. He received his M.D. from National University of Malaysia,
and MMED from Science University of Malaysia. He has completed the Advanced
Respiratory Training: RPAH & SVH (Sydney). He is also a member of the expert panel
Malaysian heart and lung transplant unit
S. A. R. Abu-Bakar (Syed Abdul Rahman Syed Abu Bakar) received the B.Sc. degree
from Clarkson University in Potsdam, New York (USA), in 1990, and an MSEE degree
from Georgia Tech in 1991 (USA), and the Ph.D. degree from the University of Bradford, England in 1997. In 1992, he joined the Faculty of Electrical Engineering as a
lecturer and currently he is an associate professor in the same faculty as well as the
head for the Computer Vision, Video and Image Processing research lab. His current
research interests include image processing with application in medical imaging,
biometrics, agricultural and industrial applications, and computer vision especially
in the area of security and surveillance. He has published more than 90 scientific
papers both at national and international levels. He is also a senior member of IEEE