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Data mining technique for breastcancer detection in thermograms usinghybrid feature extraction strategyMuthu Rama Krishnan Mookiah a , U. Rajendra Acharya a b & E.Y.K.Ng ca Department of Electronics and Computer Engineering, Ngee AnnPolytechnic, Singapore 599489b Department of Biomedical Engineering, School of Engineering,University of Malaya, Kuala Lumpur, Malaysia 50603c School of Mechanical and Aerospace Engineering, NanyangTechnological University, Singapore 639798Version of record first published: 20 Nov 2012.

To cite this article: Muthu Rama Krishnan Mookiah, U. Rajendra Acharya & E.Y.K. Ng (2012): Datamining technique for breast cancer detection in thermograms using hybrid feature extractionstrategy, Quantitative InfraRed Thermography Journal, DOI:10.1080/17686733.2012.738788

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Data mining technique for breast cancer detection in thermograms usinghybrid feature extraction strategy

Muthu Rama Krishnan Mookiaha*, U. Rajendra Acharyaa,b and E.Y.K. Ngc

aDepartment of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489;bDepartment of Biomedical Engineering, School of Engineering, University of Malaya, Kuala Lumpur,Malaysia 50603; cSchool of Mechanical and Aerospace Engineering, Nanyang Technological University,

Singapore 639798

(Received 25 April 2012; final version received 8 October 2012)

Breast thermography is capable of detecting cancer at an early stage. In this work, we haveused for analysis 50 thermograms (25 each of normal and abnormal). The main objectiveof this work is to evaluate the use of discrete wavelet transform (DWT) and texturefeatures extracted from thermograms in classifying normal and abnormal groups. Oneclinically significant texture and two DWT features were fed to Decision Tree (DT), FuzzySugeno, Naïve Bayes Classifier, K Nearest Neighbour, Gaussian Mixture Model andProbabilistic Neural Network classifiers to evaluate the best classifier. Our results showthat, DT and fuzzy classifiers yielded a highest average accuracy of 93.30%, sensitivity of86.70% and specificity of 100%. The proposed computer-aided diagnostic system can beused for an automatic classification of normal and abnormal breast thermograms which canaid the radiologists in their diagnosis.

Keywords: breast cancer; breast thermography; texture; discrete wavelet transform;decision tree; fuzzy; GMM

1. Introduction

Cancer is a class of disease in which a group of cells display uncontrolled growth resulting ina mass called tumour. Tumour can be benign or malignant. Malignant tumour tends to growrapidly and invades its surrounding tissues and sometimes metastasises. Breast cancer refersto the erratic, uncontrolled growth and proliferation of cells that originate in the breast tissue.Usually, the tumour appears as a lump. Other indications of breast cancer include changes inbreast size or shape, skin dimpling, nipple inversion, or spontaneous single-nipple discharge,differences in the colour of breast skin, breast aches, etc. [1]. Breast cancer may be detectedvia a cautious study of clinical history, physical examination and imaging. The mostcommonly used imaging modalities are mammography and ultrasound.

Breast cancer is the most common cancer in women worldwide, comprising 16% of allfemale cancers [2]. Although breast cancer is thought to be a disease of the developed world,a majority (69%) of all breast cancer deaths occurs in developing countries [3]. Survival ratesdue to breast cancer vary greatly worldwide, ranging from 80% or over in North America,Sweden and Japan to around 60% in middle-income countries and below 40% in low-income

*Corresponding author. Email: mkm2@np.edu.sg

Quantitative InfraRed Thermography2012, 1–15, iFirst article

ISSN 1768-6733 print/ISSN 2116-7176 online� 2012 Taylor & Francishttp://dx.doi.org/10.1080/17686733.2012.738788http://www.tandfonline.com

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countries [4]. The American Cancer Society has predicted that about 230,480 new cases ofinvasive breast cancer and about 57,650 new cases of non-invasive breast cancer will be diag-nosed in the USA in 2011, and around 39,520 women will die from breast cancer [5]. Inrecent years, the incidence rate of breast cancer has considerably increased. Simultaneously,breast cancer survival rate has also improved over the past few years with the developmentof more effective diagnostic techniques and improvements in treatment methodologies.The low survival rates in less developed countries may be due to lack of early detectionprogrammes, thereby resulting in a higher number of women being detected with the later-stage disease [5].

Mammography is considered the gold standard screening tool for the detection of breastcancer. It uses low dose X-ray, high-contrast and high-resolution detectors and an X-ray sys-tem designed specifically for imaging the breasts. It applies doses of ionising radiation toform images of the breast area, which are then used to detect abnormal breast mass [6,7].Mammography has found its application in both screening and diagnosis of breast cancer.However, a false-positive or false-negative results may arise from mammography. False-posi-tive results can lead to anxiety and other forms of psychological distress in affected women.The additional tests can also be costly and time-consuming and caused physical discomfort.The main reason of false-negative results is high breast density and it occurs more oftenamong younger women than older woman because younger women are more likely to havedense breasts. False-negative results can lead to delays in treatment of cancer. Another riskinvolved with mammography is the exposure of radiation which is harmful to the patient’shealth [8].

Mammogram is the most commonly recommended diagnostic modality for breast cancerdetection. It is able to identify about 61–87% of breast cancer cases [9]. However, it has alower sensitivity in women of aged less than 50 years because of its inability to effectivelyimage dense breast tissue for younger women [9]. Moreover, the false-negative rates of mam-mogram are between 5 and 15%. The mammography is also difficult to differentiate tumourfrom post-operative breast scar. Thus, despite being the primary imaging method, mammogra-phy has its own limitations. In addition, ultrasound and mammogram can only detect analready developed cancer that is sizeable enough to be detected [9].

To overcome these limitations of the current popular imaging modalities, several adjunc-tive imaging modalities were evaluated for breast cancer screening and diagnosis [9]. One ofthe most popular imaging modality is the infrared thermogram [10]. Low trusts in the USmedicine of thermography in breast cancer diagnostics are important. Moreover, thermogra-phy has very high reliability of multimodality data [11,12]. Schaefer et al. [13] have used thefeatures derived from cross co-occurrence matrix, together with fuzzy classification and devel-oped a system to analyse breast thermogram for cancer diagnosis. Their proposed algorithmwas able to identify the malignancies with an accuracy of 80%. Tan et al. [14] have proposeda complementary learning fuzzy neural network (CLFNN), as a computer-assisted interven-tion tool for breast thermogram analysis. Their experimental results show that the confluenceof breast thermography and CLFNN not only provided a low-cost alternative but also aidedthe physician in breast cancer detection using thermograms with the recall rate of 100% [14].

EtehadTavakol et al. [15] have used k- and fuzzy c-means clustering for colour segmenta-tion of infrared breast images. They suggested that fuzzy c-means was preferred because thefuzzy nature of pixels in the IR breast images helped it to provide more accurate results withno empty cluster. Recently, Wiecek et al. [16] have used discrete wavelet transform (DWT)with biorthogonal and Haar mother wavelets features and neural networks to classify the nor-mal and benign thermograms. They were able to classify accurately with an efficiency of86.6%.

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In another study by Tan et al. [17], discrete temperature readings were recorded byplacing 16 temperature sensors on the surface of the breast to detect normal, benign, cancerand suspected cancer stages [17]. They used five classifiers viz. back-propagation algorithm,probabilistic neural network (PNN), fuzzy, Gaussian mixture model (GMM) and supportvector machine (SVM) for classification. They were able to achieve more than 80% accuracyin classifying the four different groups.

The study by Acharya et al. [18] used gray-level co-occurrence matrix (GLCM) featuresand SVM to classify normal and cancerous thermograms. They were able to classify thesamples with an accuracy of 88%.

Breast thermography is the recording of temperature signatures to form an image (thermo-gram) of the temperature distribution of the breast surface. It has been used as a technique ormedical imaging since 1960s. Thermography makes use of a thermal detector to capture infraradiation emit from the object of interest and relayed to a processing system which measuresand displays the heat pattern of the object surface or human skin [19]. Thermography ispassive in nature as it does not emit any harmful radiation or subject the patient to any riskcompare to anatomical tests such as X-rays, computed tomography imaging or mammogra-phy. It is a hygienic procedure as it is a non-contact scanning process. Other benefits of ther-mography include high portability and real-time imaging, which allow data to be recordedand process in the computer [20]. Figure 1(a) and (b) shows a typical thermogram of apatient with normal breast and cancerous breast, respectively.

Thermography is based on the principle that metabolic and vascular circulation in cancer-ous tissue and the area surrounding a developing breast cancer is almost always higher thanin normal breast tissue. A process called angiogenesis (new blood vessel formation) andregional vasodilatation of the blood vessels occur when cancerous cells are present. Due tothe increased blood flow and metabolic activities, the temperature of the regional skin surfacewill be relatively higher than the normal breast temperature. For non-cancer patients, the tem-perature distribution of the thermogram is generally symmetrical across the midline of thebody. Therefore, in breast cancer, thermography detects disease by identifying areas of asym-metric temperature distribution on the breasts’ surface. The analysis of the thermogramsshould be carried out by a trained physician in comparing the temperature distribution of leftand right breasts [20]. This comparison takes scrupulous training which is dependent on theexperience and watchfulness of the physician. Thus, computer-aided diagnostic (CAD) inthermograms can show great promise in being used as an adjunct tool for early breast cancerdetection.

Figure 1. Thermogram of (a) normal and (b) cancerous breast.

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In the work, we proposed a CAD technique for fast, cost-effective, accurate and objectiveclassification of normal and malignant breast tissues using thermal images. We extracted three(run length percentage, average and energy of wavelet coefficients) statistically significant fea-tures based on GLCM, and DWT from the thermograms. The extracted feature vectors werefed to one of the six classifiers: Decision Tree (DT), Fuzzy Sugeno (FS), GMM, K NearestNeighbour (KNN), Naive Bayes Classifier (NBC) and PNN. To evaluate the performance ofthese classifiers, we used accuracy, sensitivity, specificity and positive predictive value asmeasures. The block diagram of the proposed CAD system for breast cancer detection isshown in Figure 2. The organisation of the paper is as follows: Section 2 explains the detailsof the thermograms, pre-processing steps followed, feature extraction techniques and classifi-ers used. Section 3 presents the results obtained in this work. Detailed discussion on theobtained results is given in Section 4. Finally, the paper concludes in Section 5.

2. Materials and methods

In this section, a brief description of data used, pre-processing, feature extraction and classifi-ers is provided.

2.1. Data acquisition

Field data were collected from the Department of Diagnostic Radiology, Singapore GeneralHospital using non-contact thermography [17,18]. Infrared thermograms were acquired usingNEC-Avio Thermo TVS2000 MkIIST System 3.0–5.4 μm short wavelength (30 frames/s),Stirling cooler, InSb detector with (256� 200) elements (Japan) (URL: www.nec-avio.co.jp/en/contact/index.html) which has a measuring accuracy of ± 0.4% (full scale) and temperatureresolution of 0.1 °C at 30 °C black body, with the instrument placed 1m away from the chestwith lens (FOV 15°� 10°, IFOV 2.2mrad) attached. Ninety patients were chosen at randomto undergo the thermography examination. Examination was done in a temperature controlled

Pre-processing

Feature Extraction

GLCM, DWT

Significant Feature Selection

Run percentage,

Norm and energy of wavelet coefficient

Breast Thermogram

Classification

MalignantNormal

Figure 2. Block diagram of the proposed automated breast cancer diagnosis system.

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room with the temperature range of 20–22 °C (within ± 0.1 °C). Humidity of the examinationroom was maintained at 60 ± 5% [11,20,21]. The patients were required to rest for at least15min to stabilise and acquire the basal metabolic rate, which will result in minimal surfacetemperature changes, and therefore, satisfactory thermograms [22]. Also, the patients wereasked to wear a loose gown that does not restrict airflow. Furthermore, it was ensured thatthe patients were within the recommended period of the 5th to 12th and 21st day after theonset of menstrual cycle since during these periods the vascularisation is at basal level withleast engorgement of blood vessels [23]. In this work, we have used 50 thermograms (25 nor-mal and 25 malignant).

2.2. Pre-processing of thermograms

The breast thermograms collected were different in image size, thus there is need to crop allthe thermograms to the same image size before running through the software. The breast ther-mograms were cropped to 120� 150 pixels which show the section of the left and rightbreast.

Figure 3 presents the original breast thermogram of a breast cancer patient of image size,345� 272 pixels, cropped to show the left and right breasts of identical image size,120� 150 pixels. As per the principles of thermography in detection of breast cancer, it canbe seen from Figure 3 that there is an asymmetric heat distribution between the right and leftbreasts. The left breast has a higher skin surface temperature than the right breast, implying ahigh possibility of left breast cancer. The gray-scale image (Figure 4) of the right and leftbreasts is used to extract the texture parameters.

Figure 3. Pre-processing of breast cancer thermogram.

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2.3. Texture analysis

The thermogram has various granular structures due to temperature variation depending oneither normal or malignant condition, which are self-similar patterns at different scales termedas ‘texture’. Texture measures smoothness, coarseness and regularity of pixels in an image.These features describe the mutual relationship among intensity values of neighbouring pixelsrepeated over an area larger than the size of the relationship [24]. The texture recognition sys-tem that can be grouped into four main classes: geometrical, statistical, model based methodsand signal processing methods. Structural texture analysis is more complex compared to theother approaches [24–26]. Statistical approaches yield characterisation of textures as smooth,coarse, grainy, etc. These methods are based on the relationship between intensity values ofpixels; measures include entropy, contrast and correlation based on the GLCM. Moreover,psychophysical research has given evidence that the human brain does a frequency analysisof the image [24]. Texture is especially suited for multi-resolution (wavelet) type of analysisbecause of its properties. In statistical methods, features are described using a spatial gray-level dependency matrix. In multi-resolution methods, features are described using time andfrequency. Some of the statistical and wavelet features extracted from the thermograms aredescribed in the next section.

2.3.1. Co-occurrence matrix

The presence of various granular structural changes in the echo images makes the use ofimage texture analysis techniques suitable for capturing good discriminating features. Mostmedical images have a variation in gray-level intensity values which are repetitive, and thesevariations are characterised as the texture of the image [27].

Figure 4. Gray-scale image of breast cancer thermogram.

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A statistical method of examining texture that considers the spatial relationship of pixelsis the GLCM, also known as the gray-level spatial dependence matrix [27]. The GLCM func-tions characterise the texture of an image by calculating how often pairs of pixel with specificvalues and in a specified spatial relationship occur in an image, creating a GLCM, and thenextracting statistical measures from this matrix. Given an M�N image, the GLCM is defined[27,28] by

Cd ¼j ðp; qÞ; ðpþ Dx; qþ DyÞ: Iðp; qÞ ¼ i; Iðpþ Dx; qþ DyÞ ¼ jf g j ð1Þ

where (p, q), ðpþ Dx; qþ DyÞ belong to m� n; d ¼ ðDx;DyÞ and j . . . j denotes the setcardinality. The probability of a pixel with a gray-level intensity i having a pixel with a gray-level intensity j at a distance ðDx;DyÞ away in the image is given by

Pdði; jÞ ¼ Cdði; jÞP\i>

P\j> Cdði; jÞ ð2Þ

where the summation is over all possible i and j. We calculated the following features:

Energy:

E ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXi

Xj

½Pdði; jÞ�2s

ð3Þ

Contrast:

C ¼Xi

Xj

ði� jÞ2Pdði; jÞ ð4Þ

Homogeneity:

H ¼Xi

Xj

Pdði; jÞ1þ ði� jÞ2 ð5Þ

Entropy:

Ent ¼ �Xi

Xj

Pdði; jÞ � ln Pdði; jÞ ð6Þ

Angular second moment:

A2M ¼Xn�1

k¼0

PdðkÞ2 ð7Þ

where PdðkÞ ¼P

i

Pj Cdði; jÞ in which j i� j j¼ k; k ¼ 0; 1; . . . ; n� 1 and n is the number

of gray-scale levels. The similarity between two pixels that are ðDx;DyÞ apart is measured bythe homogeneity feature. Contrast quantifies the local variation between those two pixels.Entropy is the largest when all elements of the co-occurrence matrix are the same. Energyquantifies the denseness in the image. When the values of PdðkÞ are very similar or close,A2M is small.

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2.3.2. Run length matrix

In run length matrix, the gray-level runs are characterised by the gray tone, length and thedirection of the run. Ph(i,j) is the run length matrix. In each entry, it consists of the numberof elements where gray level “i” has the run length “j” continuous in the direction h [29].Various textural features were calculated from the run length matrices of h = 0°, 45°, 90° and135° [30]. The features computed for classification are given below.

Gray-level non-uniformity:

Pi

Pj Phði; jÞ

n o2

Pi

Pj Phði; jÞ ð8Þ

Run percentage:

Xi

Xj

Phði; jÞ=A; ð9Þ

where A is the area of interest. Normalisation is performed to scale down the values of thecomputed features.

2.3.3. Discrete wavelet transform (DWT)

The DWT of a signal x is evaluated by sending it through a sequence of down-sampling high-and low-pass filters [31]. The low-pass filter is defined by the transfer function g[n] and thehigh-pass filter is defined by the transfer function h[n]. The output of the high-pass filter D[n]is known as the detail coefficients. Equation (10) shows how these coefficients are obtained.

D½n� ¼X1k¼�1

x½k�h½2n� k� ð10Þ

The output of the low-pass filter is termed as the approximation coefficients. Thesecoefficients are found with Equation (11).

A½n� ¼X1k¼�1

x½k�g½2n� k� ð11Þ

The frequency resolution is further increased by cascading the two basic filter operations.To be specific, the output of the first-level low pass filter is fed into the same low- and high-pass filter combination. The detailed coefficients are output at each level and they form thelevel coefficients. In general, each level halves the sample number and doubles the frequencyresolution. Consequently, in the final level, both detail and approximation coefficients areobtained as level coefficients.

For 2D signals, the 2D DWT is used. Our discussion focuses on Wavelet packets (WP)for images. These images are represented as an m� n gray-scale matrix I[i, j], where eachelement of the matrix represents the intensity of one pixel. All non-border pixels in I[i, j],where i R f0; ng and j R f0; ng, have eight immediate neighbouring pixels. These eight neigh-bours can be used to traverse through the matrix. However, changing the direction with whichthe matrix is traversed just inverts the sequence of pixels and the 2D DWT coefficients are

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the same. For example, the WP result is the same when the matrix is traversed from left toright as from right to left. Therefore, we are left with four possible directions, which areknown as decomposition corresponding to 0° (horizontal, H), 90° (vertical, V) and 45° or135° (diagonal, D) orientations [25,26,32]. The implementation of this algorithm follows theblock diagram shown in Figure 5 illustrating the N �M dimensional input image I[i, j] andthe results for level 1. The results from level 1 were sufficient to obtain significant features.

In this work, we have selected Biorthogonal 3.1 wavelet function. These waveletfunctions have both unique low-pass filter transfer function g[n] and unique high-pass filtertransfer function h[n]. The 1st level 2D DWT yields four result matrices, namely Dh1, Dv1,Dd1 and A1, whose elements are intensity values. Unfortunately, these matrixes cannot beused for classification directly, because the number of elements is too high. Therefore, wedefined two averaging methods viz. average and energy of the wavelet coefficient whichrepresent a result matrix with just one number.

The first method is used to extract average measures from 2D DWT result vectors.

Average Dv1 ¼ 1

N �M

Xx¼hNi

Xy¼hMi

jDv1ðx; yÞj ð12Þ

The second method averages the energy of the intensity values.

Energy ¼ 1

N 2 �M 2

Xx¼hNi

Xy¼hMi

ðDv1ðx; yÞÞ2 ð13Þ

These two elements (Average Dv1 and Energy) form the feature vector.

Figure 5. DWT decomposition.

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2.4. Statistical analysis

2.4.1. Test of Gaussian nature

Before classification, the data distribution needs to be known a priori. Since the features aredue to superposition of many random processes, it must follow normal distribution due tocentral limit theorem [33]. In order to verify the fact that the features follow Gaussian distri-bution, there is a need for testing the Gaussian nature of the features. Few such importanttests are performed here as follows.

Quantile–Quantile plot is the plot of sample quantiles of feature (X) vs. theoretical quan-tiles from a normal distribution. If the distribution of feature (X) is normal, the plot will beclose to linear [34–36]. We have tested our extracted nine texture features using Q–Q plot tocheck whether the features are following normal distribution. All the features are followingnormal distribution (Figure 6). Here, we have provided Q–Q plot for the features contrast,energy and entropy. Further, t-test is used to find statistical significance of the features.

2.4.2. Independent sample t-test

It is mandatory to verify whether a feature or a set of features has the discriminating capabil-ity among the labelled classes or not. In doing so, classical statistical inference provides oneof the well-established statistical tests viz. independent samples t-test, which is used forcomparing the population means of two classes. For large samples, the procedure often per-forms well [34,35]. The procedure will also produce confidence interval estimation for thedifference of two means. A large difference between the two samples means to reject the nullhypothesis H0: μ1 = μ2. In addition to this, it is useful to study the distribution pattern foreach of the features over the classes.

Figure 6. Q–Q plots of sample data vs. standard normal for the features entropy, contrast and energy.

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2.5. Classifiers

DT: a set of rules for the different classes are derived from the tree that is constructed usingthe input DWT and texture features from the training data [37]. These rules are used topredict the unknown class from the test data.

FS: it is also called Fuzzy Inference System which contains a set of fuzzy rules that map theinput feature to the output to be determined [38]. These rules are used for futureclassification. In this work, we have used Sugeno-type fuzzy inference system [39].

GMM: the conditional probability density function of the feature vector with respect to thedifferent classes is modelled as a linear mixture of multivariate Gaussian Probability DensityFunctions. Expectation–maximisation [40] is one of the maximum likelihood algorithms usedfor fitting the GMM to train the data. The test data are then evaluated using the trainedGMM classifier.

KNN: it is a simple classifier in which a feature vector is assigned the class that is the mostcommon among its KNNs [37].

NBC: it is based on Bayes theorem, and works with the assumption that the features are inde-pendent random variables. Hence, it is easier to compute the probabilities required by theBayes formula from even a small training data with a high number of features [37].

PNN: the distances from the test feature vector to the training feature vectors are determinedby the first layer which is the radial basis layer. The next layer is a competitive layer thatadds up the distance vectors for each input classes and produces a vector of probabilities asits output. The “compete” transfer functions in this layer then use the maximum of theseprobabilities to determine the class of the test data [41].

3. Results and discussion

3.1. Test of statistical significance

The extracted nine features were subjected to the independent sample ‘t’ test. It comparesmeans of the different groups. When the means between classes were relatively high, the testwas considered to be statistically significant (p< 0.01). In this work, three features (Run Per-centage, Average Dv1 and Energy) as listed in Table 1 have p-values of less than 0.0001.These clinically significant features were considered for classification. The values (mean

Table 1. Summary statistics of the features extracted from thermograms.

Features Normal Abnormal p-value

Homogeneity 0.41 ± 0.12 0.36 ± 0.14 0.0795Energy 200 ± 306 149 ± 304 0.4101Entropy 2.90 ± 0.58 3.03 ± 0.62 0.2704Contrast 7.53 ± 1.37 8.46 ± 2.51 0.0242Angular second moment 452 ± 332 612 ± 612 0.1071Run percentage 1.90 ± 0.07 2.17 ± 0.11 <0.0001Gray-level non-uniformity 9560 ± 3840 12080 ± 6840 0.0252Average Dv1 0.11 ± 0.007 0.13 ± 0.009 <0.0001Energy 1.59� 10�7 ± 0.11�10�7 1.97� 10�7 ± 0.18� 10�7 <0.0001

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± standard deviation) of the three features extracted from the two classes of thermograms arehigher for malignant compared to the normal classes.

3.2. Classification results

Fivefold stratified cross-validation method was used to test the performance of six classifiers.The whole data-set was split into five equal parts. Four parts of the data (training set) wereused for classifier development and the built classifier was evaluated using the remaining onepart as test set (i.e. in each class, 40 images were used for training and 10 images for testingeach time). This procedure was repeated five times using a different part as the test set ineach case. Finally, the average of the accuracy, sensitivity, specificity and positive predictivevalue obtained over the five evaluations were taken as the overall performance measures.

Table 2 shows the sensitivity, specificity, positive predictive accuracy and accuracy of DT,Fuzzy, GMM, KNN, NBC and radial basis probabilistic neural networks (RBPNN) classifiers,respectively. It can be seen that the DT and Fuzzy classifiers are able to identify the unknownclass with an accuracy of 93.30%. The DT and Fuzzy classifier resulted in higher sensitivity(86.70%), specificity (100%) and positive predictive value (100%).

4. Discussion

In this work, we have demonstrated the utility of breast surface temperature as an indicatorfor malignancy. This method is suitable to young women for whom mammography hasproved to be not very efficient. Due to the higher metabolic rate, the temperature increases[22] when the cancerous cells replenish. A thermogram presents a visual representation ofthose ‘hot spots’ on the breast surface, and but, the interpretation may be subjective. We havepresented a CAD technique for breast tumour classification in this paper. A novel combina-tion of one texture and two DWT-based features that adequately quantify the non-linearchanges in malignant and normal breast IR images was used to develop classifiers. This novelcombination of features can extract the subtle changes in the gray levels with high accuracy.Our study shows that the DT and FS classifiers are capable of classifying normal and malig-nant conditions with a high accuracy of 93.30%, sensitivity of 86.70% and specificity of100%. The developed classifier is robust as it was evaluated with 25 normal and 25 malig-nant samples using threefold stratified cross-validation. The preliminary results obtained usingthe proposed system show that the features are discriminative enough to yield good classifica-tion accuracy of 93.30%. Moreover, the CAD tool would be a more objective alternative tomanual analysis of IR images which might result in inter-observer variations. The system canbe installed as a stand-alone software application in the physician’s office at no extra cost.However, the system has been tested only on 50 cases, and further clinical validation will berequired to assess the diagnostic accuracy of proposed method.

Table 2. Classification results of various classifiers.

Classifiers TN FN TP FP Accuracy PPV Sensitivity Specificity

DT 5 1 4 0 93.30 100 86.70 100FS 5 1 4 0 93.30 100 86.70 100GMM 4 1 4 1 86.70 90.50 86.70 86.70KNN 5 1 4 0 90 94.40 86.70 93.30NBC 4 1 4 1 86.70 90.50 86.70 86.70PNN 5 1 4 0 90 94.40 86.70 93.30

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The summary of these studies is tabulated in Table 3.Out of the 25 cancerous cases studied, there were 10 carcinoma patients with stage II

cancer and 15 with stage III cancer. To enhance the usefulness of the proposed approach,more thermograms have to be obtained from women with early stage I malignancies. TheDWT and texture features need to be extracted from them and fed to the classifiers to eval-uate the efficiency. Besides, detection of malignant breasts with infrared thermography canbe further improved by developing an algorithm that analyses the relevant segmented partof the breast area instead of using the cropped image. Moreover, the accuracy and reliabil-ity of the system can be refined by increasing the number of training images used for clas-sification.

5. Conclusion

In this paper, we have proposed a CAD technique for the assessment of breast cancer basedon thermograms using texture and DWT features. We have selected clinically significant fivetexture features and two DWT features. These features reflect the subtle pixel variation andcontours in the images. These signatures (features) of the images coupled with DT and Fuzzyclassifiers presented a high average accuracy of 93.30%, sensitivity and specificity of 86.70%and 100%, respectively. The accuracy of a diagnostic tool that uses classifiers depends onseveral factors such as the size and quality of the training data and features chosen as classi-fier inputs. The accuracy can be further improved by extracting better features with a largersample size. Results of this work, and other studies discussed in this paper, indicate that auto-matic detection of breast cancer using thermograms shows a great promise in scanning thebreast in near future. We feel that our proposed CAD system can aid the radiologists to takethe second opinion in addition to their blind study.

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Table 3. Summary of automated breast cancer classification system using thermograms.

Authors Features Classifier Performance measure

Schaefer et al. [13] Cross co-occurrencematrix

Fuzzy Accuracy-80%

Tan et al. [14] Mean and modaltemperature of breast

CLFNN Recall rate-100%

Wiecek et al. [16] DWT features Neural network Accuracy-86.60%Tan et al. [17] Temperature readings of

breastBPNN, PNN, Fuzzy,GMM, SVM

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Acharya et al. [18] GLCM features SVM Accuracy-88%This study GLCM and DWT DT, Fuzzy Accuracy-93.30%

Sensitivity-86.70%Specificity-100%

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