[IEEE 2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE) -...

Phase Congruency Image and Sparse Classifier for Newborn Classifying Pain State

Muhammad Naufal Mansor School of Mehatronic Engineering

Universiti Malaysia Perlis Perlis, Malaysia

[email protected]

Mohd Nazri Rejab Department of Mechanical

Politeknik Tuanku Syed Sirajuddin Perlis, Malaysia

[email protected]

Abstract—Most of infant pain cause changes in the face. Clinicians use image analysis to characterize the pathological faces. Nowadays, infant pain research is increasing dramatically due to high demand from all medical team. This paper presents a sparse and naïve Bayes classifier for the diagnosis of infant pain disorders. Phase congruency image and local binary pattern are proposed. The proposed algorithms provide very promising classification rate.

Index Terms—Infant Pain, Phase congruency image, Local Binary Pattern, Sparse Classifier, Naïve Bayes Classifier.

I. INTRODUCTION

Nowadays, people have a problem of failing in handling their pain states due the factors such as lack of support, inspiration and motivation. It affects their attitude and deteriorates their performance in daily life and working ability. Recent days, the affective states of a human can be detected through visual modalities, including facial expressions, muscle movements, action units, and body movements [17]. Thus, objective assessments’ techniques have been proposed to facilitate pain for assessing the infant pain events and improve inter-judge agreements about pain events [10-16]. However, these types of assessments are subjective, inconsistent, time-consuming and prone to error [10-13]. After that, in [1-8], an overview of automatic pain recognition and classification of pain events is discussed. There are variety approaches that have been considered in previous work. As we know, there is no previous work in existing literature using Phase congruency image as features extraction algorithm and sparse classifier as classification technique in this research area.

Therefore, a feature extraction method is proposed for the recognition of pain using Phase congruency image in this paper. Sparse classifier is used for testing the effectiveness of the Phase congruency image features in the recognition of pain in infant images. Fig. 1 shows the general block diagram of infant pain recognition.

II. PROPOSED METHOD

As illustrated in Fig. 1, the experimental procedures can be divided into the following stages: preprocessing, feature extraction, and classification. The original images are adopted from Classification of Pain Expressions (COPE) database. The

Images are reduced to 100pixels x 120pixels, in order to reduce the time processing. In the preprocessing stage, we present our new pre-processing method for infant pain recognition. In the feature extraction stage, facial features such as Phase congruency image and Local Binary Pattern are employed as the feature vectors further. Finally, sparse classifier and naïve Bayes classifier are used to classify the feature vectors into the following category pairs: pain/nonpain. All experiments were processed in the MATLAB environment under Windows XP operating system using an Intel® Core ™ 2Duo CPU, 2.80 GHz processor.

Fig. 1. Infant pain Experimental Design

NO

NO YES

YES

Results Validation

End

Start

Preprocessing Data

System Implementation &

Testing

Feature Extraction

Decision Making Using Classifier

2013 IEEE International Conference on Control System, Computing and Engineering, 29 Nov. - 1 Dec. 2013, Penang, Malaysia

978-1-4799-1508-8/13/$31.00 ©2013 IEEE 450

III. PRE-PROCESSING The our main step involved 3 subsection such as (a) Image

Subjects, (b) grayscale images (c) Feature Extraction (Phase congruency image and Local Binary Patten finally (d) Sparse classifier and naïve Bayes classifier.

A. Image Subjects Photographs were taken of the infants at baseline rest and

while experiencing several noxious stimuli: bodily disturbance, an air stimulus on the nose, friction on the external lateral surface of the heel, and the pain of a heel stick. The goal of the Infant COPE study design was to obtain a representative and challenging set of facial images for classification experiments [1-3].

Fig. 2. Infant COPE Database

B. Grayscale Image We convert the true color image of the subjects to the grayscale intensity image. The grayscale image eliminating the hue and saturation information while retaining the luminance [9] as shown in Fig.3.

Fig. 3. Grayscale Infant COPE Database Image

C. Noise Image Salt & Pepper noise was employed similar in [18]. In contrast from their studies, we expand the range of noise levels varied from 10% to 90% with increments of 20% to prone our

robustness. In Fig. 4, we present restoration results for the corrupted infant pain images.

Fig. 4. Noise Infant COPE Database Image

IV. FEATURE EXTRACTION A. Phase Congruency Image

Phase congruency image (PCI) provides a measure that is independent of the overall magnitude of the signal making it invariant to variations in image illumination and/or contrast as shown in Fig. 5. It can be shown that this measure of phase congruency is a function of the cosine of the deviation of each phase component from the mean

( ) ( )( )( )( )∑

∑ −=

n n

n n

xAxxA

xPCφφcos

)(1 (1)

This measure of phase congruency does not provide good

localization and it is also sensitive to noise. Kovesi [19-20] developed a modified measure consisting of the cosine minus the magnitude of the sine of the phase deviation; this produces a more localized response. For details of this phase congruency measure and its implementation see Kovesi [20-22].

Fig. 5. Phase congruency images of Infant COPE Database


451

B. Local Binary Pattern The feature extraction algorithms used is Local Binary

Pattern (LBP) by [23-24]. LBP was introduced by Ojala et al. [25]. LBP value is computed by comparing it with those of its neighborhoods:

)(21

0, ci

p

i

iRP ggfLBP −×=∑

−

=

(2)

⎩⎨⎧

<≥

=0001

)(xx

xf (3)

where cg is the gray value of the center pixel, gi is the gray value of its neighbors, P is the number of neighbors and R is the radius of the neighborhood. LBP extracted the relevant information in a face image and encoded as efficiently as possible. The image face defined by LBP as shown in Fig 6.

Fig. 6. LBP of Infant COPE Database

V. CLASSIFIER A. Sparse Classifier

The Sparse Classifier (SC) is proposed in [26]. It is based on the assumption that the training samples of a particular class approximately form a linear basis for a new test sample belonging to the same class. If testkv , is the test sample

belonging to the thk class then,

k

n

iikik

knkknkkkkkktestk

k

v

vvvv

∈+=

∈++++=

∑=1

,,

,,2,2,1,1,, ...

α

ααα (4)

where sv ik ′, are the training samples of the thk class and

k∈ is the approximation error. Equation (5) expresses the assumption in terms of the training samples of a single class. Reconstruct a sample was done for each class by a linear combination of the training samples belonging to that class using.

ji

n

jjirecon viv

i

,1

,)( ∑=

= α (5)

The error between the reconstructed sample and the given test sample by

( ) ( ) 2,, irecontestktest vviverror −= (6)

Finally the class having the minimum error is the class of the given test sample. B. Naïve Bayes Classifier

The naïve Bayes classifier (NB) combines model with a decision rule. The common rule is to choose a better MAP decision rule. The algorithm of naïve Bayes classifier is shown as

( ) ( )cCfFpcCpclassify ii

n

ic==Π==

=1maxarg (7)

VI. EXPERIMENTAL RESULTS Conventional validation scheme is used for testing the

effectiveness of the results of the classifier. 80% of data are used for training and 20% of data are used for testing. Three analyses such as sensitivity, specificity and accuracy are conducted after extracting phase congruency features from each of the images under different noise levels with sparse and naïve Bayes classifier.

Fig. 7. Sensitivity Vs Different Noise Level

The result of the sensitivity analysis at various trials of sparse classifier with phase congruency is shown in Fig. 7. From the figure, it is observed that the sensitivity of the phase


452

congruency + local binary pattern + sparse classifier is high compares to other approach.

Fig. 8. Specificity Vs Différent Noise Level

The result of the specificity analysis at various trials of sparse classifier with phase congruency is shown in Fig. 8. From the figure, it is observed that the specificity of the phase congruency + local binary pattern + sparse classifier is high compares to other approach.

Fig. 9. Accuracy Vs Différent Noise Level

The accuracy classification results of different features using phase congruency are tabulated in Fig.9. From the above figure, it is observed that the overall accuracy for the two case studies also varies or affects. From the above discussion, it is observed that the suggested features, of combination between phase congruency and local binary pattern can be used as a good discriminating parameter of normal and pathological (pain) infant. Moreover, it can be concluded that, the proposed classifiers can be used for maximum classification of infant pain since the classification accuracies are very promising and encouraging

VII. CONCLUSION

This paper presents a phase congruency extraction method based on different noise levels classification of pain. In order to test the effectiveness and reliability of the system, suggested sparse based classifier is used. Three experiments are conducted using the extracted features. The experimental results show that the suggested features give very promising classification accuracy of 88% for all the trial. The suggested method can be used to detect pain states of a newborn. In the future work, feature reduction will be applied to reduce feature dimension and other classification algorithms will be developed to improve the current results with less computation

ACKNOWLEDGMENT This research was conducted under Fundamental Research Grant Scheme (FRGS) which is contributed by Ministry of Higher Education Malaysia.

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