CHAPTER 4 TEXTURE FEATURE EXTRACTION

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1 83 CHAPTER 4 TEXTURE FEATURE EXTRACTION This chapter deals with various feature extraction technique based on spatial, transform, edge and boundary, color, shape and texture features. A brief introduction to these texture features is given first before describing the gray level co-occurrence matrix based feature extraction technique. 4. INTRODUCTION Image analysis involves investigation of the image data for a specific application. Normally, the raw data of a set of images is analyzed to gain insight into what is happening with the images and how they can be used to extract desired information. In image processing and pattern recognition, feature extraction is an important step, which is a special form of dimensionality reduction. When the input data is too large to be processed and suspected to be redundant then the data is transformed into a reduced set of feature representations. The process of transforming the input data into a set of features is called feature extraction. Features often contain information relative to colour, shape, texture or context. 4. TYPES OF FEATURE EXTRACTION Many techniques have been used to extract features from images. Some of the commonly used methods are as follows:

2 84 Spatial features Transform features Edge and boundary features Colour features Shape features Texture features 4.. Spatial Features Spatial features of an object are characterized by its gray level, amplitude and spatial distribution. Amplitude is one of the simplest and most important features of the object. In X-ray images, the amplitude represents the absorption characteristics of the body masses and enables discrimination of bones from tissues Histogram features The histogram of an image refers to intensity values of pixels. The histogram shows the number of pixels in an image at each intensity value. Figure 4. shows the histogram of an image and it shows the distribution of pixels among those grayscale values. The 8-bit gray scale image is having 56 possible intensity values. A narrow histogram indicates the low contrast region. Some of the common histogram features are mean, variance, energy, skewness, median and kurtosis are discussed by Myint (00).

3 85 Intensity Figure 4. Histogram of an image 4.. Transform Features Generally the transformation of an image provides the frequency domain information of the data. The transform features of an image are extracted using zonal filtering. This is also called as feature mask, feature mask being a slit or an aperture. The high frequency components are commonly used for boundary and edge detection. The angular slits can be used for orientation detection. Transform feature extraction is also important when the input data originates in the transform coordinate Edge and Boundary Features Asner and Heidebrecht (00) discussed edge detection is one of the most difficult tasks hence it is a fundamental problem in image processing. Edges in images are areas with strong intensity contrast and a jump in intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data

4 86 and filters out unimportant information, while preserving the important properties of an image. Edges are scale-dependent and an edge may contain other edges, but at a certain scale, an edge still has no width. If the edges in an image are identified accurately, all the objects are located and their basic properties such as area, perimeter and shape can be measured easily. Therefore edges are used for boundary estimation and segmentation in the scene Sobel technique Sobel edge detection technique consists of a pair of 3 3convolution kernels. One kernel is simply the other rotated by 90 as shown in Figure 4.. These kernels are designed to respond maximally to edges running vertically and horizontally relative to the pixel grid of the image, one kernel for each of the two perpendicular orientations. The kernels can be applied separately to the input image, to produce separate measurements of the gradient component in each orientation. These can then be combined together to find the absolute magnitude of the gradient at each point and the orientation of that gradient G x G y Figure 4. Masks used for Sobel operator

5 Robert technique The Robert cross operator performs a simple, quick to compute, -D spatial gradient measurement on an image. Pixel values at each point in the output represent the estimated absolute magnitude of the spatial gradient of the input image at that point. The operator consists of a pair of convolution kernels as shown in Figure 4.5. One kernel is simply the other rotated by 90. This is very similar to the Sobel operator G x G y Figure 4.3 Masks used for Robert operator Prewitt technique Prewitt operator is similar to the Sobel operator and is used for detecting vertical and horizontal edges in images G x G y Figure 4.4 Masks for the Prewitt gradient edge detector

6 88 The Prewitt operator measures two components. The vertical edge component is calculated with kernel calculated with kernel y Gx and the horizontal edge component is G as shown in Figure 4.4. G G gives an indication of the intensity of the gradient in the current pixel. x y Canny technique The Canny edge detection algorithm is known popularly as the optimal edge detector. The Canny algorithm uses an optimal edge detector based on a set of criteria which include finding the most edges by minimizing the error rate, marking edges as closely as possible to the actual edges to maximize localization, and marking edges only once when a single edge exists for minimal response. According to Canny, the optimal filter that meets all three criteria that can be efficiently approximated using the first derivative of a Gaussian function. The first stage involves smoothing the image by convolving with a Gaussian filter. This is followed by finding the gradient of the image by feeding the smoothed image through a convolution operation with the derivative of the Gaussian in both the vertical and horizontal directions. This process alleviates problems associated with edge discontinuities by identifying strong edges, and preserving the relevant weak edges, in addition to maintaining some level of noise suppression. Figure 4.5 Input landsat image

7 89 Figure 4.6 Output of the edge detection techniques Finally, hysteresis is used as a means of eliminating streaking. Streaking is the breaking up of an edge contour caused by the operator output fluctuating above and below the threshold. Figure 4.6 shows the output of the different edge detection technique of given input image as shown in Figure Colour Features Colour is a visual attribute of object things that results from the light emitted or transmitted or reflected. From a mathematical viewpoint, the colour signal is an extension from scalar-signals to vector-signals. Colour features can be derived from a histogram of the image. The weakness of colour histogram is that the colour histogram of two different things with the same colour can be equal. Platt and Goetz (004) discussed colour features are still useful for many biomedical image processing applications such as

8 90 cell classification, cancer cell detection and content-based image retrieval (CBIR) systems. In CBIR, every image added to the collection is analyzed to compute a colour histogram. At search time, the user can either specify the desired proportion of each colour or submit an example image from which a colour histogram is calculated. Either way, the matching process then retrieves those images whose colour histograms match those of the query most closely Shape Features The shape of an object refers to its physical structure and profile. Shape features are mostly used for finding and matching shapes, recognizing objects or making measurement of shapes. Moment, perimeter, area and orientation are some of the characteristics used for shape feature extraction technique. The shape of an object is determined by its external boundary abstracting from other properties such as colour, content and material composition, as well as from the object's other spatial properties Texture Features Guiying Li (0) defined texture is a repeated pattern of information or arrangement of the structure with regular intervals. In a general sense, texture refers to surface characteristics and appearance of an object given by the size, shape, density, arrangement, proportion of its elementary parts. A basic stage to collect such features through texture analysis process is called as texture feature extraction. Due to the signification of texture information, texture feature extraction is a key function in various image processing applications like remote sensing, medical imaging and contentbased image retrieval.

9 9 There are four major application domains related to texture analysis namely texture classification, segmentation, synthesis and shape from texture. Texture classification produces a classified output of the input image where each texture region is identified with the texture class it belongs. Texture segmentation makes a partition of an image into a set of disjoint regions based on texture properties, so that each region is homogeneous with respect to certain texture characteristics. Texture synthesis is a common technique to create large textures from usually small texture samples, for the use of texture mapping in surface or scene rendering applications. The shape from texture reconstructs three dimensional surface geometry from texture information. For all these techniques, texture extraction is an inevitable stage. A typical process of texture analysis is shown in Figure 4.7. Input Image Pre-processing Feature extraction Segmentation, Classification, Synthesis, Shape from texture Post-processing Figure 4.7 Various image analysis steps

10 9 4.3 TEXTURE FEATURE EXTRACTION Neville et al (003) discussed texture features can be extracted using several methods such as statistical, structural, model-based and transform information Structural based Feature Extraction Structural approaches represent texture by well defined primitives and a hierarchy of spatial arrangements of those primitives. The description of the texture needs the primitive definition. The advantage of the structural method based feature extraction is that it provides a good symbolic description of the image; however, this feature is more useful for image synthesis than analysis tasks. This method is not appropriate for natural textures because of the variability of micro-texture and macro-texture Statistical based Feature Extraction Statistical methods characterize the texture indirectly according to the non-deterministic properties that manage the relationships between the gray levels of an image. Statistical methods are used to analyze the spatial distribution of gray values by computing local features at each point in the image and deriving a set of statistics from the distributions of the local features. The statistical methods can be classified into first order (one pixel), second order (pair of pixels) and higher order (three or more pixels) statistics. The first order statistics estimate properties (e.g. average and variance) of individual pixel values by waiving the spatial interaction between image pixels. The second order and higher order statistics estimate properties of two or more pixel values occurring at specific locations relative to each other. The most popular second order statistical features for texture analysis are derived

11 93 from the co-occurrence matrix. Statistical based texture features will be discussed in section Model based Feature Extraction Model based texture analysis such as fractal model and Markov model are based on the structure of an image that can be used for describing texture and synthesizing it. These methods describe an image as a probability model or as a linear combination of a set of basic functions. The Fractal model is useful for modeling certain natural textures that have a statistical quality of roughness at different scales and self similarity, and also for texture analysis and discrimination. There are different types of models based feature extraction technique depending on the neighbourhood system and noise sources. The different types are one-dimensional time-series models, Auto Regressive (AR), Moving Average (MA) and Auto Regressive Moving Average (ARMA). Random field models analyze spatial variations in two dimensions. Global random field models treat the entire image as a realization of a random field, and local random field models assume relationships of intensities in small neighbourhoods. Widely used class of local random field models are Markov models, where the conditional probability of the intensity of a given pixel depends only on the intensities of the pixels in its neighbourhood Transform based Feature Extraction Transform methods, such as Fourier, Gabor and wavelet transforms represent an image in space whose co-ordinate system has an interpretation that is closely related to the characteristics of a texture. Methods based on Fourier transforms have a weakness in a spatial localization so these do not perform well. Gabor filters provide means for better spatial localization but

12 94 their usefulness is limited in practice because there is usually no single filter resolution where one can localize a spatial structure in natural textures. These methods involve transforming original images by using filters and calculating the energy of the transformed images. These are based on the process of the whole image that is not good for some applications which are based on one part of the input image. 4.4 STATISTICAL BASED FEATURES The three different types of statistical based features are first order statistics, second order statistics and higher order statistics as shown in Figure 4.8. Statistical based features First order Statistics Second order Statistics Higher order Statistics Figure 4.8 Statistical based features 4.4. First Order Histogram based Features First Order histogram provides different statistical properties such as four statistical moments of the intensity histogram of an image. These depend only on individual pixel values and not on the interaction or co-occurrence of neighbouring pixel values. The four first order histogram statistics are mean, variance, skewness and kurtosis.

13 95 A histogram h for a gray scale image I with intensity values in the range I ( x, y) 0, K would contain exactly K entries, where for a typical 8-bit grayscale image, K Each individual histogram entry is defined as, h (i) = the number of pixels in I with the intensity value I for all 0 i K. The Equation (4.) defines the histogram as, h ( i) cardinality ( x, y) I( x, y) i (4.) where, cardinality denotes the number of elements in a set. The standard deviation, and skewness of the intensity histogram are defined in Equation (4.) and (4.3). ( I ( x, y) N m) (4.) 3 (I(x, y) m) skewness (4.3) 3 N 4.4. Second Order Gray Level Co-occurrence Matrix Features Some previous research works compared texture analysis methods; Dulyakarn et al. (000) compared each texture image from GLCM and Fourier spectra, in the classification. Maillard (003) performed comparison works bewteen GLCM, semi-variogram, and Fourier spectra at the same purpose. Bharati et al. (004) studied comparison work of GLCM, wavelet texture analysis, and multivariate statistical analysis based on PCA (Principle Component Analysis). In those works, GLCM is suggested as the effective texture analysis schemes. Monika Sharma et al (0) discussed GLCM is applicable for different texture feature analysis.

14 96 The GLCM is a well-established statistical device for extracting second order texture information from images. A GLCM is a matrix where the number of rows and columns is equal to the number of distinct gray levels or pixel values in the image of that surface. GLCM is a matrix that describes the frequency of one gray level appearing in a specified spatial linear relationship with another gray level within the area of investigation. Given an image, each with an intensity, the GLCM is a tabulation of how often different combinations of gray levels co-occur in an image or image section. Texture feature calculations use the contents of the GLCM to give a measure of the variation in intensity at the pixel of interest. Typically, the cooccurrence matrix is computed based on two parameters, which are the relative distance between the pixel pair d measured in pixel number and their relative orientation. Normally, is quantized in four directions (e.g., 0º, 45 º, 90 º and 35 º), even though various other combinations could be possible. GLCM has fourteen features but between them most useful features are: angular second moment (ASM), contrast, correlation, inverse difference moment, sum entropy and information measures of correlation. These features are thoroughly promising Gray Level Run Length Matrix Features Petrou et al (006) defined gray level run length matrix (GLRLM) is the number of runs with pixels of gray level i and run length j for a given direction. GLRLM generate for each sample of image fragment. A set of consecutive pixels with the same gray level is called a gray level run. The number of pixels in a run is the run length. In order to extract texture features gray level run length matrix are computed. For each element, ( i, j) the run length, r of the GLRLM represents the number of runs of gray level i having

15 97 length j. GLRLM can be computed for any direction. Mostly five features are derived from the GLRLM. These features are: Short Runs Emphasis (SRE), Long Runs Emphasis (LRE), Gray Level Non-Uniformity (GLNU), Run Length Non-Uniformity (RLNU), and Run Percentage (RPERC). These are quite improved in representing binary textures Local Binary Pattern Features Local binary pattern (LBP) operator is introduced as a complementary measure for local image contrast. Lahdenoja (005) discussed the LBP operator associate statistical and structural texture analysis. The LBP describes texture with smallest primitives called textons (or, histograms of texture elements). For each pixel in an image, a binary code is produced by thresholding, its neighbourhood with the value of the center pixel. A histogram is then assembled to collect the occurrences of different binary codes representing different types of curved edges, spots, flat areas, etc. This histogram is an arrangement as the feature vector result of applying the LBP operator. The LBP operator considers only the eight nearest neighbours of each pixel and it is rotation variant, but invariant to monotonic changes in gray-scale can be applied. The dimensionality of the LBP feature distribution can be calculated according to the number of neighbours used. LBP is one of the most used approaches in practical applications, as it has the advantage of simple implementation and fast performance. Some related features are Scale-Invariant Feature Transform (SIFT) descriptor (SIFT is a distinctive invariant feature set that is suitable for describing local textures), LPQ (Local Phase Quantization) operator, Center- Symmetric LBP (CS-LBP) and Volume-LBP.

16 Auto Correlation Features An important characteristic of texture is the repetitive nature of the position of texture elements in the image. An autocorrelation function can be evaluated that measures this coarseness. Based on the observation of autocorrelation feature is computed that some textures are repetitive in nature, such as textiles. The autocorrelation feature of an image is used to evaluate the fineness or roughness of the texture present in the image. This function is related to the size of the texture primitive for example the fitness of the texture. If the texture is rough or unsmooth, then the autocorrelation function will go down slowly, if not it will go down very quickly. For normal textures, the autocorrelation function will show peaks and valleys. It has relationship with power spectrum of the fourier transform. It is also responsive to noise interference. The autocorrelation function of an image I ( x, y) is defined in Equation (4.4) as follows P N N I ( u, v) I ( u x, v y) u 0 v 0 ( x, y) (4.4) N N u 0 v 0 I ( u, v) Co-occurrence Matrix SGLD Statistical methods use second order statistics to model the relationships between pixels within the region by constructing Spatial Gray Level Dependency (SGLD) matrices. A SGLD matrix is the joint probability occurrence of gray levels i and j for two pixels with a defined spatial relationship in an image. The spatial relationship is defined in terms of distance, d and angle,. If the texture is coarse and distance d is small compared to the size of the texture elements, the pairs of points at distance d should have similar gray levels. Conversely, for a fine texture, if distance d is

17 99 comparable to the texture size, then the gray levels of points separated by distance d should often be quite different, so that the values in the SGLD matrix should be spread out relatively uniformly. Hence, one of the ways to analyze texture coarseness would be, for various values of distance d, some measure of scatter of the SGLD matrix around the main diagonal. Similarly, if the texture has some direction, i.e., is coarser in one direction than another, then the degree of spread of the values about the main diagonal in the SGLD matrix should vary with the direction. Thus texture directionality can be analyzed by comparing spread measures of SGLD matrices constructed at various distances of d. From SGLD matrices, a variety of features may be extracted. From each matrix, 4 statistical measures are extracted including: angular second moment, contrast, correlation, variance, inverse difference moment, sum average, sum variance, sum entropy, difference variance, difference entropy, information measure of correlation, information measure of correlation II and maximal correlation coefficient. The measurements average the feature values in all four directions Edge Frequency based Texture Features A number of edge detectors can be used to yield an edge image from an original image. An edge dependent texture description function E can be computed using Equation (4.5) as follows E f ( i, j) f ( i d, j) f ( i, j) f ( i, j d) f ( i, j) f ( i d, j) f ( i, j) f ( i, j d) (4.5) This function is inversely related to the autocorrelation function. Texture features can be evaluated by choosing specified distances d. It varies the distance, d, parameter from to 70 giving a total of 70 features.

18 Primitive Length Texture Features Coarse textures are represented by a large number of neighbouring pixels with the same gray level, whereas a small number represents fine texture. A primitive is a continuous set of maximum number of pixels in the same direction that have the same gray level. Each primitive is defined by its gray level, length and direction. Let B( a, r) represents the number of primitives of all directions having length r and gray level a. Assume image dimensions, L is the number of gray levels, N r M, N be is the maximum primitive length in the images and K is the total number of runs. It is given by the Equation (4.6) as L N r a r B( a, r) (4.6) texture. Then, the Equations (4.6) (4.0) define the five features of image Short primitive emphasis = L N r B( a, r) K a r r (4.7) Long primitive emphasis = L N r K a r B( a, r) (4.8) Gray level uniformity = L Nr K a r B( a, r) r (4.9) Primitive length uniformity = L K a N r r B( a, r) (4.0) Primitive percentage = L N r a r K rb( a, r) K MN (4.)

19 Law s Texture Features Law s of texture observed that certain gradient operators such as Laplacian and Sobel operators accentuated the underlying microstructure of texture within an image. This was the basis for a feature extraction scheme based a series of pixel impulse response arrays obtained from combinations of -D vectors shown in Figure 4.9. Each -D array is associated with an underlying microstructure and labeled using an acronym accordingly. The arrays are convolved with other arrays in a combinatorial manner to generate a total of 5 masks, typically labeled as L5, E5, S5, W5 and R5 for the mask resulting from the convolution of the two arrays. Level L5 [ ] Edge E5 [ 0 ] Spot S5 [ 0 0 ] Wave W 5 [ 0 ] Ripple R5 [ ] Figure 4.9 Five D arrays identified by laws These masks are subsequently convolved with a texture field to accentuate its microstructure giving an image from which the energy of the microstructure arrays is measured together with other statistics. The commonly used features are mean, standard deviation, skewness, kurtosis and energy measurements. Since there are 5 different convolutions, altogether it obtains a total of 5 features. For all feature extraction methods, the most appropriate features are selected for classification using a linear stepwise discriminant analysis. Among the above mentioned techniques, researchers suggested the GLCM is one of the very best feature extraction techniques. From GLCM,

20 0 many useful textural properties can be calculated to expose details about the image. However, the calculation of GLCM is very computationally intensive and time consuming. 4.5 GRAY LEVEL CO-OCCURRENCE MATRIX In 973, Haralick introduced the co-occurrence matrix and texture features which are the most popular second order statistical features today. Haralick proposed two steps for texture feature extraction. First step is computing the co-occurrence matrix and the second step is calculating texture feature based on the co-occurrence matrix. This technique is useful in wide range of image analysis applications from biomedical to remote sensing techniques Working of GLCM Basic of GLCM texture considers the relation between two neighbouring pixels in one offset, as the second order texture. The gray value relationships in a target are transformed into the co-occurrence matrix space by a given kernel mask such as 3 3, 5 5, 7 7 and so forth. In the transformation from the image space into the co-occurrence matrix space, the neighbouring pixels in one or some of the eight defined directions can be used; normally, four direction such as 0, 45, 90, and 35 is initially regarded, and its reverse direction (negative direction) can be also counted into account. It contains information about the positions of the pixels having similar gray level values. Each element ( i, j) in GLCM specifies the number of times that the pixel with value i occurred horizontally adjacent to a pixel with value j. In Figure 4.8, computation has been made in the manner where, element (, ) in the GLCM contains the value because there is only one instance in the

21 03 image where two, horizontally adjacent pixels have the values and. Element (, ) in the GLCM contains the value because there are two instances in the image where two, horizontally adjacent pixels have the values and. Figure 4.0 Creation of GLCM from image matrix Element (, ) in the GLCM contains the value because there are two instances in the image where two, horizontally adjacent pixels have the values and. The GLCM matrix has been extracted for input dataset imagery. Once after the GLCM is computed, texture features of the image are being extracted successively. 4.6 HARALICK TEXTURE FEATURES Haralick extracted thirteen texture features from GLCM for an image. The important texture features for classifying the image into water body and non-water body are Energy (E), Entropy (Ent), Contrast (Con), Inverse Difference Moment (IDM) and Directional Moment (DM).

22 04 Andrea Baraldi and Flavio Parmiggiani (995) discussed the five statistical parameter energy, entropy, contrast, IDM and DM, which are considered the most relevant among the 4 originally texture features proposed by Haralick et al. (973). The complexity of the algorithm also reduced by using these texture features. Let i and j are the coefficients of co-occurrence matrix, M i, j is the element in the co-occurrence matrix at the coordinates i and j and N is the dimension of the co-occurrence matrix Energy Energy (E) can be defined as the measure of the extent of pixel pair repetitions. It measures the uniformity of an image. When pixels are very similar, the energy value will be large. It is defined in Equation (4.) as E N i 0 N j o M i, j (4.) 4.6. Entropy This concept comes from thermodynamics. Entropy (Ent) is the measure of randomness that is used to characterize the texture of the input image. Its value will be maximum when all the elements of the co-occurrence matrix are the same. It is also defined as in Equation (4.3) as Ent N i 0 N j o M i, j ( ln( M ( i, j))) (4.3) Contrast The contrast (Con) is defined in Equation (4.4), is a measure of intensity of a pixel and its neighbour over the image. In the visual perception

23 05 of the real world, contrast is determined by the difference in the colour and brightness of the object and other objects within the same field of view. Con N i 0 N j o i j M i, j (4.4) Inverse Difference Moment Inverse Difference Moment (IDM) is a measure of image texture as defined in Equation (4.5). IDM is usually called homogeneity that measures the local homogeneity of an image. IDM feature obtains the measures of the closeness of the distribution of the GLCM elements to the GLCM diagonal. IDM has a range of values so as to determine whether the image is textured or non-textured. IDM N i 0 N j o i j M i, j (4.5) Directional Moment Directional moment (DM), as the name signifies, this is a textural property of the image computed by considering the alignment of the image as a measure in terms of the angle and it is defined as in Equation (4.6) DM N i 0 N j o M i, j i j (4.6) The Table 4. shows some of the texture features extracted using GLCM, to classify an image into water body and non-water body region.

24 06 Table 4. Texture features extracted using GLCM Energy Entropy Contrast IDM DM E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E

25 APPLICATION OF TEXTURE Texture analysis methods have been utilized in a variety of application domains such as automated inspection, medical image processing, document processing, remote sensing and content-based image retrieval Remote Sensing Texture analysis has been extensively used to classify remotely sensed images. Land use classification where homogeneous regions with different types of terrains (such as wheat, bodies of water, urban regions, etc.) need to be identified is an important application Medical Image Analysis Image analysis techniques have played an important role in several medical applications. In general, the applications involve the automatic extraction of features from the image which is then used for a variety of classification tasks, such as distinguishing normal tissue from abnormal tissue. Depending upon the particular classification task, the extracted features capture morphological properties, colour properties, or certain textural properties of the image. 4.8 SUMMARY This chapter detailed the gray level co-occurrence matrix based feature extraction to obtain energy, entropy, contrast, inverse difference moment and directional moment. These texture features are served as the input to classify the image accurately. Effective use of multiple features of the image and the selection of a suitable classification method are especially significant for improving classification accuracy. The chapter 5 discusses classification techniques for improving accuracy along with their applications.

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