An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions

Computers in Biology and Medicine 83 (2017) 69–83

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Computers in Biology and Medicine journal homepage: www.elsevier.com/locate/compbiomed

An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions

MARK

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Ting Shu, Bob Zhang , Yuan Yan Tang Department of Computer and Information Science, Avenida da Universidade, University of Macau, Taipa, Macau, China

A R T I C L E I N F O

A BS T RAC T

Keywords: Texture feature analysis Facial key block analysis Diabetes Mellitus detection Image gray-scale histogram Medical biometrics

Introduction: Researchers have recently discovered that Diabetes Mellitus can be detected through noninvasive computerized method. However, the focus has been on facial block color features. In this paper, we extensively study the effects of texture features extracted from facial specific regions at detecting Diabetes Mellitus using eight texture extractors. Materials and methods: The eight methods are from four texture feature families: (1) statistical texture feature family: Image Gray-scale Histogram, Gray-level Co-occurance Matrix, and Local Binary Pattern, (2) structural texture feature family: Voronoi Tessellation, (3) signal processing based texture feature family: Gaussian, Steerable, and Gabor filters, and (4) model based texture feature family: Markov Random Field. In order to determine the most appropriate extractor with optimal parameter(s), various parameter(s) of each extractor are experimented. For each extractor, the same dataset (284 Diabetes Mellitus and 231 Healthy samples), classifiers (k-Nearest Neighbors and Support Vector Machines), and validation method (10-fold cross validation) are used. Results: According to the experiments, the first and third families achieved a better outcome at detecting Diabetes Mellitus than the other two. Conclusions: The best texture feature extractor for Diabetes Mellitus detection is the Image Gray-scale Histogram with bin number=256, obtaining an accuracy of 99.02%, a sensitivity of 99.64%, and a specificity of 98.26% by using SVM.

1. Introduction Diabetes Mellitus (DM) or more commonly known as diabetes, is a disease that occurs when the body cannot effectively use the insulin it produces [1]. In addition, it can occur when the pancreas does not produce enough insulin [1]. The World Health Organization (WHO) estimated that in 2014 there were 422 million people worldwide suffering from DM [2] and this number will increase to 642 million by 2040 [3]. This means 1 in 10 adults will have DM, with the Western Pacific (including East/Southeast Asia and all of Oceania) accounting for 214.8 million DM patients [3]. There are four DM traditional diagnostic techniques [4]: A1C test, Fasting Plasma Glucose (FPG) test, 2-h Plasma Glucose (2-h PG), and Oral Glucose Tolerance Test (OGTT). All four diagnostic methods draw blood from the patient, which can cause pain and discomfort as well as being invasive. For example, in order to administer OGTT [5,6], the patient must drink a liquid containing glucose after at least 8 h of fasting. Then, a small blood sample is usually taken every hour for 2–3 h.

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Recently, researchers discovered that DM can be detected through facial block color analysis [7]. Compared with the traditional DM diagnostic method [5,6], this method is non-invasive and causes no discomfort to the patient. There are two kinds of features in facial block analysis: color [7] and texture. However, there exists little to none in the literature on facial block analysis using texture features. More specifically, applying these texture features to detect DM. There are many texture feature extractors other than Gabor filters in texture analysis, such as co-occurrence matrices [8], eigenfilter [9], fractal model [10], Local Binary Pattern [11], etc. In [12], the texture features are categorized into four families: statistical texture feature, structural texture feature, signal processing based texture feature, and model based texture feature. Statistical texture features are computed based on the statistical distribution of image intensities at specified relative pixel positions, which measures the spatial distribution of pixel values. In this family, there are many features with first to higher order statistics which are determined based on the pixels number of each observation [12]. In

Corresponding author. E-mail addresses: [email protected] (T. Shu), [email protected] (B. Zhang), [email protected] (Y. Yan Tang).

http://dx.doi.org/10.1016/j.compbiomed.2017.02.005 Received 16 January 2017; Received in revised form 16 February 2017; Accepted 16 February 2017 0010-4825/ © 2017 Elsevier Ltd. All rights reserved.

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order to explore the influences of this type of feature in DM detection, different orders of features are used in this paper. Image Gray-scale Histogram (IGH) [13–15] is a typical first order statistical texture feature, Gray-level Co-occurrence Matrix (GLCM) [16–18] belongs to the second order, and Local Binary Pattern (LBP) [11,19–22] as a higher order feature is also employed. Structural texture features are characterized by texture primitives which can be as simple as individual pixels, a region with uniform graylevels, or line segments [12]. In our paper, Voronoi Tessellation (VT) [23–25] as the texture primitives of the facial specific regions is used. Most signal processing based texture features are computed based on the energy of the responses produced by applying filter banks on the image. Three filters (Gaussian [26–28], Steerable [29–31], and Gabor [32–34]) are explored in this paper. Model based texture features are generally presented through stochastic and generative models with the estimated model parameters as the texture features for texture analysis [12]. Models used in this type of feature include many types, such as random field models. Here, Markov Random Field [35–37] (MRF) is applied as the model in the model based texture feature. Based on categorization mentioned above, we will extensively analyze each texture extractor family in order to determine the optimal extractor for DM detection. The facial images are first captured through a specially designed device where four facial key blocks are extracted automatically from the facial specific regions. Next, we use all of the eight texture feature extractors to extract the texture features from each facial key block. Two traditional classifiers (k-Nearest Neighbors (kNN) [38–40] and Support Vector Machines (SVM) [41–43]) are applied to detect DM based on the extracted texture feature values with 10-fold cross validation [44–46]. The optimal texture feature extractor is selected according to this classification result. In addition, different parameter values in the extractors are experimented to evaluate the best method and its most fitting parameter(s). The rest of this paper is organized as follows. Section 2 introduces the facial specific regions and the eight texture feature extraction methods. The experimental results of the eight methods for DM detection are described in Sections 3 and 4 concludes this paper.

Fig. 1. Facial different regions according to TCM.

Fig. 2. An example of a facial image with four located key blocks.

blocks are located through Eq. (1)–(4).

2. Materials and methods 2.1. Facial specific regions As mentioned above, each facial image is captured through a specially designed image capture device. The device is a black box consisting of a 3-CCD camera in the center and two lamps on each side. When capturing the facial image, the individual places his/her head on the chin rest in front of the camera. This ensures all facial images captured under different environments are not affected by lighting. In Traditional Chinese Medicine (TCM) it is believed that the status of the internal organs can be determined from different regions of the face [47–49]. Fig. 1 shows a human face partitioned into various regions according to TCM [50]. Applying this idea to our proposed method, four facial key blocks are automatically extracted from each facial image representing the main regions. No facial block is used to represent region C in Fig. 1 due to the existence of facial hair. Fig. 2 is an example of a facial image with its four key blocks labeled. Forehead Block (FHB) is on the forehead, Left Cheek Block (LCB) and Right Cheek Block (RCB) are symmetric and positioned below the left and right eyes respectively, and the last block (Nose Bridge Block (NBB)) is on the mid-point of LCB and RCB, located on the nose. We extract the four facial key blocks automatically. In the automatic key blocks extraction procedure the pupils are first detected and marked. The positions of the two pupils are denoted as LP: (w1, h1) (left) and RP: (w2, h2 ) (right). Based on LP and RP, the four facial key

⎛ w + w2 h1 + h2 1 ⎞ LFHB = ⎜ 1 , + H⎟ ⎝ 2 2 3 ⎠

(1)

⎛ 1 ⎞ LLCB = ⎜w1, h1 − H ⎟ ⎝ 4 ⎠

(2)

⎛ w + w2 h1 + h2 2 ⎞ LNBB = ⎜ 1 , − H⎟ ⎝ 2 2 9 ⎠

(3)

⎛ 1 ⎞ LRCB = ⎜w2, h2 − H ⎟ ⎝ 4 ⎠

(4) th

where L ith key block name means the position of i key block, such as LFHB is the position of FHB; and W and H are the width and height of the facial image, respectively. Fig. 2 depicts the locations of the four facial key blocks based on the left and right pupil positions. The size of each facial key block is first set to be 64×64. In order to select the optimal block size, various facial key block sizes are experimented in Section 3.6. More details about the facial image capture device and facial key blocks can be found in [7]. 2.2. Texture feature extraction The eight texture extractors are described in this section. First, the three methods (IGH, GLCM, and LBP) from the statistical texture feature family are given along with a description of VT from the structural texture feature family. Afterwards, the three filters (Gaussian, Steerable, and Gabor) from the third family (signal processing based) are described. Finally, MRF coming from the last family (model based) is introduced. Table 1 lists the eight methods from the four families. 70

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often pairs of pixels with specific values in a specified spatial relationship occur in the image [16–18]. For GLCM, a matrix is first generated by calculating the frequency occurrence of a pair of pixels in an image. Fig. 4 is an example of GLCM generation [54]. After the matrix is generated, various properties can be extracted to represent the texture of the image. In this paper, four properties are extracted: (1) contrast: evaluating the local variations in the matrix, (2) correlation: measuring the joint probability occurrences of the pairs, (3) energy: the sum of squared elements in the matrix, and (4) homogeneity: estimating the proximity of the distribution of elements in the matrix. An example of the four properties of FHB from a DM sample and a Healthy sample are shown in Table 2.

Table 1 List of the texture feature extractors. Family

Method name

1. Statistical

Image Gray-scale Histogram (IGH) Gray-level Co-occurrence Matrix (GLCM) Local Binary Pattern (LBP) Voronoi Tessellation (VT) Gaussian Steerable Gabor Markov Random Field (MRF)

2. Structural 3. Signal processing based

4. Model based

2.3. Statistical texture feature family

2.3.3. Local Binary Pattern (LBP) LBP is a particular case of the Texture Spectrum model and was first described in [56]. LBP has been proven to be a powerful feature and is commonly used for texture classification [11]. Fig. 5 is an example on how to extract LBP from an image. The image is first divided into N non-overlapping cells (3 ≤ N ≤ ImageSize ). For each cell a histogram with size of B is extracted to represent the cell. Finally, the histograms of all the cells are concatenated into the LBP feature of the image. The size of this LBP feature is 1 × L , where L = B × N . An example of calculating LBP is shown in Fig. 6. Comparing each pixel of a cell with its 8 neighbors, the number at this position is ‘0′, where the pixel is greater than its neighbor; otherwise, it is ‘1′. Following the pixels from left to right and top to bottom order, an 8-bit binary number is generated and converted to decimal for convenience. Over the cell, a histogram is computed based on the frequency of each number occurring. Figs. 7a and b show an example of LBP applied to FHB of a DM and a Healthy sample, respectively, where the cell size is 32.

The statistical texture features measure the spatial distribution of pixel values by computing the local features based on the statistical distribution of the image intensities at specified relative pixel positions [12,51,52]. Based on the number of pixels which defines the local feature, statistical texture feature extraction algorithms can be categorized into first order (one pixel), second order (two pixels), and higher order (three and more pixels) statistics [52]. The first order statistics estimate properties of one pixel value, whereas second and higher order statistics evaluate properties of the spatial interaction between two and more image pixels [51,53]. In order to explore the various orders of statistics for DM detection, we select one typical method from each order. Hence, IGH, GLCM, and LBP are the first, second, and higher order statistical texture feature extractors used, respectively. They are introduced one by one in this subsection. 2.3.1. Image Gray-scale Histogram (IGH) IGH calculates the image gray-scale value intensities and uses the calculated histogram to represent the image [13–15]. IGH is easy and fast to compute, which analyzes the spatial distribution of gray-scale values of each facial key block. Let H = [h (0), h (1), …, h (C )] denote one IGH of each block, where h(i) corresponds to the frequency of the grayscale value i of this block and 1 ≤ C ≤ 255 means the max gray level. It is defined as:

h (i ) =

∑ fi (x, y),

2.4. Structural texture feature family Structural texture feature is characterized by texture primitives and the spatial placement rules of these primitives [12]. For this feature type, texture primitives are first extracted from the images and the spatial placement rules are then generalized. The texture primitives can be pixels or line segments, while the placement rules can be geometric relationships or statistical properties among these primitives [53]. In [57], Tuceryan et al. proposed an algorithm based on VT to segment texture and proved VT is effective in texture analysis. Except for texture segmentation, VT has been employed in many other applications (such as cluster finding, distribution study, and so on) and proven to be effective and easy in texture processing [58,59]. Therefore, in this paper, VT and its distances are used as the texture primitive and the spatial placement rule for the structural texture feature, respectively.

i = 0, 1, …, C

x, y

(5)

where fi (x, y) is given as:

⎧1, if im (x, y) = i , fi (x, y) = ⎨ ⎩ 0, otherwise

(6)

and im (x, y) is the gray-scale value of pixel (x,y) in this block. Therefore, each facial image has an IGH vector with a size of C + 1 feature values for each block ×4 facial key blocks = 4C + 4 dimensionality. An example of IGH applied to FHB for both DM and Healthy is shown in Fig. 3a and b, respectively. Here the number of bins is 256.

2.4.1. Voronoi Tessellation (VT) For VT, some tokens [60], which are some meaningful points in the image are first specified beforehand. A region corresponding with each token is next created and is called a Voronoi cell. The region consists of all points closer to that token than to any other. Hence, an image is

2.3.2. Gray-level Co-occurrence Matrix (GLCM) GLCM represents the texture of an image through calculating how

Fig. 3. An example of IGH applied to FHB for DM (left) and Healthy (eight).

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Fig. 4. An example of GLCM generation [55].

Edge Detector) in image processing [62]. In two dimensions (2-D), the Gaussian function is defined as:

Table 2 An example of FHB's four properties for both a DM and a Healthy sample using GLCM. Class

Contrast

Correlation

Energy

2

Homogeneity

Gaussian (x, y) = DM

0.2406

0.4991

0.3384

0.8797

Healthy

2.3059

0.8352

0.6696

0.9588

x +y 1 − ·e 2σ 2 2πσ 2

2

(7)

where x indicates the distance between the point and the origin in the horizontal axis while the distance from the origin in the vertical axis is denoted by y, and σ means the standard deviation of the Gaussian distribution. In this paper, different values of the two parameters: filter size and σ are set to produce the optimal result. Initially, we apply the 2-D Gaussian function to generate a Gaussian filter bank. Next, each Gaussian filter convolves with each facial key block to produce a response R (x, y):

partitioned into some regions. An example of a VT diagram is shown in Fig. 8. Using VT to extract the texture feature from images was first proposed in [61]. A VT feature can be represented based on the properties of the partitioned regions, such as its area and so on. Table 3 shows an example of the VT feature vector for both a DM and Healthy FHB, respectively, where the token number is 4.

R (x, y) = G (x, y)*im (x, y)

(8)

where im (x, y) is a facial key block. Here, G (x, y) denotes each filter in the Gaussian filter bank, and * represents convolution. The texture value of each response is the mean of all its pixels. Therefore, the size of each sample feature vector extracted by the Gaussian filter is 4 (1 feature value for each block ×4 blocks). An example of FHB's Gaussian feature value for both DM and Healthy samples is represented in Table 4, where the filter size is 13 and σ = 3.

2.5. Signal processing based texture feature family Signal processing based texture feature is commonly extracted by applying filter banks to extract edges, lines, isolated dots, etc. from the image. The Gaussian filter is a common filter in the third family [26– 28], and a Steerable filter is a derivative of the Gaussian filter and is very useful for texture analysis [29–31]. At the same time, the Gabor filter is similar with the optical system of human beings [33,32]. Each filter will be described below.

2.5.2. Steerable filter The Steerable filter as a derivative of the Gaussian filter and was first proposed by Freeman and Adelson in 1991 [30]. Steerable filters are a bank of filters with arbitrary orientations. Each filter in this bank is generated through a linear combination of a set of bias functions. The Gaussian derivative filters are applied to generate the bias functions in this paper. A first derivative filter for any direction θ can be defined as:

2.5.1. Gaussian filter In signal processing, a Gaussian filter is a filter which convolves with a signal (or image) producing a Gaussian function (or its approximation) response. The Gaussian filter has always been used in preprocessing to remove noise for edge detection (such as the Canny

Fig. 5. An example of LBP extraction from an image.

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Fig. 6. An example of calculating a local binary pattern.

Dθ = Gx cos θ + Gy sin θ

(9)

where Gx and Gy are the first x derivative and the first y derivative of a Gaussian function, while cos θ and sin θ are known as the interpolation functions of the basis functions Gx and Gy. Varying σ and θ values are set to locate the optimal parameters of the Steerable filter in DM detection. Similar with the Gaussian filter, the Steerable filter convolves with each block through Eq. (8) where G (x, y) represents the Steerable filter. Table 5 shows an example of the Steerable feature value for a DM FHB and a Healthy FHB, respectively, where σ = 2 and θ = 210°.

2.5.3. Gabor filter In the spatial domain, a 2D Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave [32]. We applied a Gabor filter bank consisting of 40 filters (five σ values ×eight θ values) which convolved with each facial key block. The feature value of each block is the mean of the 40 filter responses. The size of each Gabor filter is 39×39 and no explanation was given for this size. For this paper, we set various filter sizes, σ, and θ values to extract the texture features from each block and to locate the best parameters of the Gabor filter in DM detection. To compute the texture value of each block a 2-D Gabor filter is applied and defined as: x ′2 + γ 2·y ′2

G (x , y ) = e

−2σ 2

⎛ x′ ⎞ cos ⎜2π ⎟ ⎝ λ⎠

Fig. 8. An example of a VT diagram. Table 3 An example of VT's feature vector for a DM FHB and a Healthy FHB. Class

Feature vector

DM

[872.58, 139.40, 519.47, 2.23, 110.51, 112.58, 155.09, 954.61, 112.96, 165.62, 200.96, 1.47, 235.52, 294.22, 220.68, 115.88] [502.43, 445.60, 435.40, 465.90, 294.54, 115.36, 127.49, 189.17, 172.75, 90.00, 146.86, 217.78, 1.33, 1.04, 1.05, 242.92]

Healthy

(10)

Table 4 An example of FHB's Gaussian feature value for both DM and Healthy samples.

where x′ = x·cos θ + y·sin θ , y′ = −x·sin θ + y·cos θ , σ is the variances, λ is the wavelength, γ is the aspect ratio of the sinusoidal function, and θ is the orientation. The texture feature value is calculated in the same way as the Gaussian filter using Eq. (8). Here G (x, y) in Eq. (8) is a Gabor filter. An example of FHB's Gabor feature vector for DM and Healthy is shown in Table 6, where the filter size is 16, σ = 1, and θ = 315°.

Class

Feature value

DM Healthy

189.7529 216.1719

Fig. 7. An example of LBP applied to the FHB of a DM (left) and Healthy (right) sample.

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while the linear kernel function is used in SVM. Other values of k and different kernel functions were experimented for k-NN and SVM respectively, but did not improve upon the accuracy. The focus of this paper is to extensively analysis different texture features for our application. Therefore, the traditional classifiers of k-NN and SVM were chosen due to its high performance and robustness. The 284 DM samples were collected from The Hong Kong Foundation for Research and Development in Diabetes, Prince of Wales Hospital, Hong Kong SAR in mid-2012. The 231 Healthy samples were captured in The Guangdong Provincial TCM Hospital, Guangdong, where 142 Healthy samples were captured in late 2011 and the other 89 taken in 2015. In 10-fold cross validation, the facial image dataset is split randomly into 10 parts: the size of the first 9 parts are equal (28 for DM and 23 for Healthy) and the size of the 10th part is 56 (32 for DM and 24 for Healthy). In each step, one of the 10 parts is the testing set and the rest of the data is the training set. This is repeated until each part has been the testing set. The final accuracy is the mean of the ten accuracies from the ten steps, where accuracy is the proportion of testing data that is classified correctly. In this paper, three performance measurements including accuracy, sensitivity, and specificity are used to measure the experimental results and are defined as:

Table 5 An example of the Steerable feature value from a DM FHB and a Healthy FHB. Class

Feature value

DM Healthy

0.0095 0.0034

Table 6 An example of FHB's Gabor feature vector for a DM and a Healthy FHB. Class

Feature vector

DM Healthy

[2.52, 0.60, 0.60, 0.51, 2.54, 0.53, 0.47, 0.56] [2.89, 0.53, 0.45, 0.44, 2.84, 0.46, 0.61, 0.70]

2.6. Model based texture feature family Model based texture feature considers a texture to be a stochastic, possibly periodic, two-dimensional image field. Therefore, it generally uses stochastic and generative models to represent images. Its texture features are characterized based on the estimated model parameters [12,37]. For this family, the models can be fractal models, random field models, and so on. Here, MRF as a typical method of random field models is used to extract texture features from each facial key block.

Accuracy =

2.6.1. Markov Random Field (MRF) MRF assumes that the sufficient point of achieving a good global image representation is local information. Therefore, one major challenge is to efficiently estimate the model parameters. There are four main steps in MRF extraction for computer vision [63]: (i) a set of nodes that may correspond to pixels or agglomerations of pixels is generated from the image, (ii) hidden variables corresponding with the nodes are recommended into a model, (iii) over the pixel values and the hidden variables, a joint probabilistic model is created, (iv) the groups are expressed based on the hidden variables and often depicted as edges in the graph of MRF. As for each facial key block, the MRF feature vector has a dimensionality of 50, hence it is difficult to show an example of a DM and a Healthy sample. The MRF feature value of each facial key block is represented as the mean MRF energy from each iteration. Therefore, the 50 dimensionality feature vector is from the 50 iterations of MRF energy mean values.

True Pos. +True Neg. All Data Number

(11)

Sensitivity =

True Pos. True Pos. +False Neg.

(12)

Specificity =

True Neg. True Neg. +False Pos.

(13)

The experimental results are displayed in the order of methods in Table 1. In Figs. 9–16, the black line represents accuracy, the light purple line is for sensitivity, and specificity is shown as the blue line. 3.1. Statistical texture feature family results In accordance with Subsection 2.2, 3 methods (IGH, GLCM, and LBP) from the first family were used to extract texture features from each facial key block. The DM detection results using the 3 methods are discussed in this subsection. Figs. 9–11 show the results using the 3 methods with k-NN and SVM. 3.1.1. IGH results The results based on IGH with its histogram bin number varying using k-NN and SVM are illustrated in Figs. 9a and b, respectively. According to Subsection 2.3.1, the histogram bin number equals to C + 1 and 1 ≤ C ≤ 255, therefore the range of the histogram bin number is [2256]. In order to focus on the optimal parameter value (the histogram bin number) of IGH, different bins

3. Results and discussions For the experiments, 284 DM and a new Healthy dataset consisting of 231 samples are classified via k-NN and SVM with 10-fold cross validation based on the facial key block texture features extracted by the eight extractors. In all the experiments, k equals to 1 for k-NN,

Fig. 9. IGH k-NN and SVM results with bin numbers changing.

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Fig. 10. GLCM k-NN and SVM results with different property combinations.

([21, 22, …, 28] = [2, 4, …, 256]) were experimented. As shown in Fig. 9a, the accuracy and sensitivity were increasing with an increase in the number of bins. The best accuracy of 93.62% with a sensitivity of 99.64%, and a specificity of 86.18% was achieved where the histogram bin number was 256. Fig. 9b represents the results of IGH using SVM. The highest accuracy (99.02%) was also reached with 256 bins, while its sensitivity and specificity were 99.64% and 98.26%, correspondingly. The specificity value was better than sensitivity when the histogram bin number was smaller than 64, while after that the opposite was true.

Correlation, Energy, and Homogeneity) in the matrix of GLCM. Hence, all of the 15 combinations for the four properties were used to represent each facial key block. The matrix property combinations are shown in Table 7. The results based on GLCM with various property combinations using k-NN and SVM are depicted in Figs. 10a and b, respectively. As shown in Fig. 10a, using k-NN the best accuracy of 70.99% with a sensitivity of 81.38% and a specificity of 58.12% was obtained, where Contrast + Correlation was used. The highest accuracy using SVM (71.97%) was 0.98% higher than that of k-NN, with a sensitivity of 89.51% and a specificity of 50.33%, using only Correlation.

3.1.2. GLCM results From Subsection 2.3.2, there are four properties (Contrast,

Fig. 11. LBP k-NN and SVM results with varying cell sizes.

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Fig. 12. VT k-NN and SVM results with different token numbers.

than that of even sizes (refer to Fig. 13a). From filter size =12 to 64, the highest accuracy of even filters was 87.20%, and that of odd sized filters was 87.98%. As shown in Fig. 13c (filter size=13 and σ changing), the best accuracy with the same sensitivity and specificity remained unchanged. The results using the Gaussian filter with its filter size and σ increasing classified by SVM are represented in Figs. 13b and d, respectively. In both figures, it is obvious that the results remained the same with the size and σ changing in the Gaussian filter. The best accuracy was 82.58% with a better sensitivity of 98.93% and a worse specificity of 62.17% compared to k-NN.

3.1.3. LBP results LBP as a higher order statistical texture feature extraction method was used as the third extractor in facial key block extraction. As the size of each facial key block is 64×64, the range of the cell size N is from 3 to 64 (refer to Subsection 2.3.3). The results based on LBP with N fluctuating using k-NN and SVM are represented in Fig. 11. As shown in Fig. 11a, the accuracy and sensitivity using k-NN increased significantly from N=3 to 30, while afterwards this value they had little change. The best accuracy of 91.89% with a sensitivity of 91.56% and a specificity of 92.25% was reached when N=32. The sensitivity based on LBP using SVM was always higher than the specificity for all N values, while the accuracy was between them (refer to Fig. 11b). With a sensitivity of 93.94% and a specificity of 90.53%, the highest accuracy using SVM was 92.44%, where N was 21.

Three filters including Gaussian, Steerable, and Gabor were applied on facial key blocks to extract texture features. Figs. 13–14 show the results based on the three filters with different parameters using k-NN and SVM.

3.3.2. Steerable filter result The Steerable filter as a derivative of the Gaussian filter and was the second one used in this family. According to Subsection 2.5.2, the Steerable filter has two parameters: θ and σ. The range of these parameters are 0°:15°:360° and 1: 1: 8, respectively. The k-NN and SVM classification results using the Steerable filter with the varying θ and σ are depicted in Fig. 14. The highest accuracy of 73.53% with a sensitivity of 75.94% and a specificity of 70.54% using k-NN was achieved, where θ = 210° and σ = 2 . As shown in Fig. 14a (θ changing and σ = 2 ), compared with the accuracy, the sensitivity and specificity fluctuated a lot. The fluctuation range of the accuracy was from 50% to 74%. The results based on the Steerable filter with θ = 210° and σ changing using k-NN are illustrated in Fig. 14b. From σ = 2 , the specificity fell, however the sensitivity raised with the increasing of σ. The accuracy had two peaks, where σ = 2 and 4. The highest accuracy of 78.02% with the sensitivity of 82.37% and the specificity of 72.70% based on the Steerable filter with θ = 315° and σ = 4 using SVM was obtained. According to Fig. 14c (θ changing and σ = 4 ), the same thing happened in Fig. 14a, the sensitivity and specificity oscillated, while the accuracy had a smaller change ([55%78.1%]). The results with the constant θ and the changing σ are represented in Fig. 14d. The specificity rose from 50.65% to 82.68% with the increasing of σ, while from σ = 2 the sensitivity fell from 87.68% to 53.30%. The accuracy had a peak of 78.02%, where σ = 4 .

3.3.1. Gaussian filter results The Gaussian filter, as the most commonly used filter in pattern recognition was first applied. From Subsection 2.5.1 there are two parameters: filter size and σ for the Gaussian filter. The classification results by k-NN and SVM based on the Gaussian filter with its size and σ increasing are illustrated in Fig. 13. The range of the filter size and σ are 1:1:64 and 1:1:8, respectively. The highest accuracy using k-NN based on the Gaussian filter with size = 13 and σ = 3 was 87.98% with a sensitivity of 90.58%, and a specificity of 84.84%. The accuracy of odd filter sizes was always better

3.3.3. Gabor filter result Similar with the human optical system, the Gabor filter is an efficient texture feature extractor. In this paper, the Gabor filter has three parameters: filter size, σ, and θ (refer to Subsection 2.5.3). The range of these parameters are 1: 1: 64 , 1: 1: 8, and 0°: 45°: 315°, respectively. Fig. 15 shows the results based on the Gabor filter with size, σ, and θ increasing classified by k-NN and SVM. The highest accuracy obtained through k-NN was 96.18% with a sensitivity of 98.66%, and a specificity of 93.12%, where filter size = 16 , σ = 5, and θ = 315°. As shown in Fig. 15a, from size = 1 to 18, the

3.2. Structural texture feature family results In this family, only one method (VT) was used to extract texture features from each facial key block. For this paper, the minimum of each region in the block was applied as the token of VT and the mean distance between the minimum and its corresponding cell points were used to represent each facial key block. The token number ranges from [2, 4, 8, 16], as the max token number is ImageSize and the size of each 4 block is 64. The results based on VT with its various token numbers using k-NN and SVM are depicted in Fig. 12. In both Figs. 12a and b, the best accuracy was obtained when the token number was 4. However, the best accuracy of 54.15% using SVM was better than that of 51.60% using k-NN. Its corresponding sensitivity and specificity with k-NN were 54.46% and 48.04% and those of SVM were in turn 76.43% and 26.52%. 3.3. Signal processing based texture feature family results

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Fig. 13. Gaussian filter k-NN and SVM results.

used in this paper. MRF has two parameters: potential and iteration, ranging from 0.1: 0.1: 0.5 and 10: 10: 50 , respectively (refer to Subsection 2.6.1). Fig. 16 represents the detection results based on MRF with the two parameters changing using k-NN and SVM. The highest accuracy of 69.78% with a sensitivity of 76.29%, and a specificity of 61.83% using k-NN was obtained, where potential = 0.1 and iteration was 50. The results classified by k-NN based on MRF with potential and iteration varying are illustrated in Figs. 16a and b, respectively. The accuracy fell with an increase in potential, while it rose by increasing iteration. According to Figs. 16c and d, the highest accuracy using SVM was 71.61% with a sensitivity of 84.87%, and a specificity of 55.14%. The accuracy rose with an increase in potential from Fig. 16c, however it had little change in Fig. 16d.

results of all three performance metrics increased significantly, while afterwards accuracy, sensitivity, and specificity fluctuated only a little. The results based on the Gabor filter with the changing σ, constant filter size, and θ using k-NN are represented in Fig. 15c. In this figure, the accuracy decreased as σ went up. On the other hand, the accuracy rose with an increasing θ (refer to Fig. 15e). The best accuracy of 83.72% with a sensitivity of 87.90%, and a specificity of 78.44% using SVM was reached, with a filter size = 54 , σ = 5, and θ = 315°. From Fig. 15b (size changing, σ = 5, and θ = 315°) there was a minor change in accuracy with an increasing filter size, while in both Figs. 15d and f, the accuracy increased along with σ and θ, respectively. 3.4. Model based texture feature family results MRF from the fourth texture feature family was the last extractor 77

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Fig. 14. Steerable filter k-NN and SVM results.

after that shows its classification time, and finally the last column is the total time. The parameters of each texture extractor are the same with Table 8, which shows the best result of each feature extractor and classifier. From Table 9, it is obvious that IGH took the least amount of time (6.9184 ms) to extract its texture feature using 256 bins. For all texture extractors k-NN was faster than SVM. In terms of the total running time, IGH + k-NN performed the fastest at 7.2893 ms, while the longest time of 1587.2427 ms (1.587 s) was obtained by Gabor + SVM. This is reasonable as the Gabor filter needs to set up a 2-D filter bank first, which requires time and further time is required to produce a response value for each pixel. The first family of texture feature extractors on the other hand simply calculates the pixel value distribution, which is fast and easy. This is why on average its extraction time is quicker than the other three families.

3.5. Comparison Fig. 17 and Table 8 summarizes the best results from the eight facial key block texture feature extractors. In Fig. 17, the accuracies of k-NN are represented in the blue bar and the red bar depicts the results using SVM. As shown in Fig. 17, the 6 algorithms from the first and third texture feature families are more effective than the 2 methods of the second and fourth families, as all of its 12 accuracies (6 methods ×2 classifiers) were higher than those of the other 4 accuracies (2 methods ×2 classifiers). According to Section 2.2, the first and third texture feature families focus on information such as the local area of the image, while the other two families are interested in the whole image information. Hence, the first and third family of methods are more suitable for extracting texture features from the four facial key blocks than the other two, which is consistent with the experimental results. In Table 8, there are 5 results higher than 90%. They are IGH (k-NN and SVM), LBP (k-NN and SVM), and Gabor (k-NN), where the highest accuracy was 99.02% obtained through IGH with bin number = 256 using SVM. The four facial key blocks contain only skin, therefore, the texture feature extractors that focus on the structure of the block are not suitable for this application. However, IGH only calculates the distribution of the block pixel values and is more appropriate. The result proves that IGH can represent the facial key blocks well for DM detection, while the other seven extractors may ignore some useful information in the block. Another way to compare and contrast the different extractors is to calculate its running time (in miliseconds shown in Table 9). In this table the first column is the name of the facial key block texture feature extraction method, the following column is its corresponding parameter(s), the third column shows the texture extraction time (for all four facial key blocks), the next column gives the classifier used, the one

3.6. Facial key block size analysis In order to explore the influences that various facial key block sizes have in DM detection, experiments using IGH with bin number = 256 to extract texture features from blocks with sizes ranging from [1: 1: 64] classified through SVM with the linear kernel function was implemented. This result is shown in Fig. 18 and analyzed in the following. In this figure, the blue stars indicate the classification accuracy for a corresponding block size. Looking at Fig. 18, it is obvious that the accuracy increased with the increasing of the facial key block size, where the best result with a block size of 59 is indicated with a red star. For block sizes ranging from 19 to 64, the accuracy fluctuated only a little, with a mean accuracy of 98.65% and a standard deviation of 0.00197. We can extrapolate from this result that a block size greater than 64 would not deviate too much this mean. Therefore, a larger block size ( > 64) would not only add to the computation cost, but also 78

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Fig. 15. Gabor filter k-NN and SVM results.

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Fig. 16. MRF k-NN and SVM results.

have a minor affect on the accuracy.

Table 7 Matrix property combination list.

4. Conclusion

Matrix property 1. Contrast 3. Energy 5. Contrast + Correlation 7. Contrast + Homogeneity 9. Correlation + Homogeneity 11. Contrast + Correlation + Energy 13. Contrast + Energy + Homogeneity

In recent years, more and more researchers have been interested in developing non-invasive methods to detect DM, particularly through facial analysis. There are two types of features that can be extracted from facial key images: color and texture. Given the lack of research in analyzing facial texture features, especially for DM detection, in this paper we explore the influences that different texture feature extractors have at detecting this disease. In texture classification, there exists many texture feature extraction methods. They can be categorized into four families: (1) statistical, (2) structural, (3) signal processing based, and (4) model based. For each facial key block representing a facial specific region, eight methods (with different parameters) from the four

2. Correlation 4. Homogeneity 6. Contrast + Energy 8. Correlation + Energy 10. Energy + Homogeneity 12. Contrast + Correlation + Homogeneity 14. Correlation + Energy + Homogeneity

15. All

Fig. 17. Best accuracies of the eight texture feature extractors using k-NN and SVM.

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Table 8 Summary of the best results.

enlarge the facial image dataset to include more diseases.

families were applied to extract its texture features. In the eight methods, 3 (IGH, GLCM, and LBP) are from the first family, 1 (VT) from the second family, another 3 methods (Gaussian, Steerable, and Gabor) belong to the third family, and 1 method (MRF) is from the last family. Using the texture feature vectors generated by applying one of the eight extractors to a facial key block classified with two classifiers (kNN and SVM), and using 10-fold cross validation, the experimental results were conducted. Based on these results, it can be concluded that IGH from the first family outperforms the other methods at DM detection with an accuracy of 99.02%, a sensitivity of 99.64%, and a specificity of 98.26% using 256 bins with a block size of 59 via SVM. Furthermore, IGH took the least amount of time (6.9184 ms) to extract the texture features. These results make sense since IGH would perform calculations on the distribution of the block pixel values rather than focusing on the block structure. As part of our future work, we will apply more classifiers and

Conflict of interest The authors declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgements This work was supported by the Research Grants of University of Macau [MYRG2015-00049-FST, MYRG2015-00050-FST]; the Science and Technology DevelopmentFund (FDCT) of Macau [128/2013/A, 124/2014/A3]; and Macau-China join Project [008-2014-AMJ]. This research project was also supported by the National Natural Science Foundation of China [61273244] and [61602540].

Table 9 Running time (in miliseconds) of each sample based on the eight extractors. Method

Parameter (s)

Extraction time (ms)

Classifier

Classification Time (ms)

Total Time (ms)

IGH

Bin number=256 Bin number=256 Contrast + Correlation Correlation Cell Size=32 Cell Size=21 Token Number=4 Token Number=4 Size=13, σ = 3 Size=1, σ = 1 σ = 2 , θ=210° σ = 4 , θ=315° Size=16, σ = 1, θ=315° Size=54, σ = 5, θ=315° Potential=0.1, Iteration=50 Potential=0.5, Iteration=20

6.9184 6.9184 9.0252 9.0233 11.4951 9.1126 9.9903 9.9903 8.7534 7.4796 11.8854 14.1437 34.9534 1586.2718 374.6563 174.5961

k-NN SVM k-NN SVM k-NN SVM k-NN SVM k-NN SVM k-NN SVM k-NN SVM k-NN SVM

0.3709 60.8505 0.1495 0.2155 0.5476 1.0796 0.1592 131.3184 0.1553 66.6913 0.1476 0.1845 0.1689 0.9709 0.1961 7.3146

7.2893 67.7689 9.1748 9.2388 12.0427 10.1922 10.1495 141.3087 8.9087 74.1709 12.0330 14.3282 35.1223 1587.2427 374.8524 181.9107

GLCM LBP VT Gaussian Steerable Gabor MRF

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Ting Shu is a PhD student in the Department of Computer and Information Science at

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Yuan Yan Tang is a Chair Professor in the Department of Computer and Information Science at the University of Macau. His research interests include pattern analysis and machine intelligence, knowledge and data engineering, wavelet and multiresolution analysis, information security, as well as Chinese computing.

Bob Zhang is an Assistant Professor in the Department of Computer and Information Science at the University of Macau. His research interests include biometrics, pattern recognition, image processing, and medical image analysis.

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