CHAPTER 5 HARALICK FEATURES EXTRACTION

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1 118 CHAPTER 5 HARALICK FEATURES EXTRACTION 5.1 OVERVIEW OF FEATURE EXTRACTION The feature is defined as a function of one or more measurements, each of which specifies some quantifiable property of an object, and is so computed that it quantifies some significant characteristics of the object. All features can be coarsely classified into low-level features and high-level features. Low-level features can be extracted direct from the original images, whereas high-level feature extraction must be based on low-level features. Texture is a surface property. It is characterized by the spatial distribution of gray levels in a neighbourhood. Since texture shows its characteristics both by pixel co-ordinates and pixel values, there are many approaches used for texture classification. The image texture depends on the scale or resolution at which it is displayed. A texture with specific characteristics in a sufficiently small scale could become a uniform texture if it is displayed at a larger scale. The Gray-Level Co-occurrence Matrix (GLCM) seems to be a well-known statistical technique for feature extraction. The GLCM is a tabulation of how often different combinations of pixel gray levels could occur in an image. The goal is to assign an unknown sample image to one of a set of known texture classes. Textural features can be scalar numbers, discrete histograms or empirical distributions. They characterize the textural properties of the images, such as spatial structure, contrast, roughness, orientation, etc and have certain correlation with the desired output.

2 119 In pattern recognition and image processing, feature extraction is a special form of dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant, then the input data will be transformed into a reduced representation set of features. Transforming the input data into a set of features is called feature extraction. If the features extracted are carefully chosen it is expected that the feature set will extract the relevant information from the input data in order to perform the desired task using the reduced representation instead of the full size input. Features often contain information relative to gray shade, texture, shape or context. To classify an object in an image, we must first extract some features out of the image. The process by which the features are extracted from an ultrasound placenta is shown in the Figure 5.1 below Ultrasound Histogram Processed Placenta Images Equalization Image Feature Extraction Results: Normal Placenta Feature Classification Feature Selection Figure 5.1 Histogram Equalization and Feature extraction of Ultrasound Placenta 5.2 IMAGE TEXTURE FEATURE EXTRACTION Texture features reflect the regular changes of gray values in images. Such changes are correlated statistically and spatially. Figure 5.2 represent the Haar Wavelet Decomposition of Left Placenta with its

3 120 equivalent histogram. Figure 5.3 shows the Haar Wavelet Decomposition of Right Placenta with its equivalent histogram. Figure 5.4 displays the synthesized Image from image fusion of left and right placenta with its histogram. Figure 5.2 Haar Wavelet Decomposition of Left Placenta with its equivalent histogram Figure 5.3 Haar Wavelet Decomposition of Right Placenta with its equivalent histogram

4 121 Figure 5.4 Synthesized Image from image fusion of left and right placenta with its histogram Texture Features Based on the GLH The Gray Level Histogram (GLH) is a function of the gray scale. It describes the probability of its occurrence in an image and reflects the periodicity and density of image texture element structures. Thus the GLH provides a basic tool for subsequent image processing. For example h(r k ) is a discrete function for an image with L gray scales. It can then be denoted as h(r k ) = n k. (5.1) r k nk = k th grayscale = number of pixels with r k grayscale be calculated as The probability of occurrence for the r k gray scale is P(r k ) and can

5 122 ( ) = (5.2) = total number of the pixels in an image Therefore, ( ) = 1 (5.3) The first order statistics measure the probability of having a specific value on the point which is being evaluated. The histogram of the image will become an important tool to calculate this statistical value. Then by looking at the image histogram, the following first order properties can be computed. = ( ) (5.4) = ( ) ( ) (5.5) = log ( ) ( ) (5.6) where, MGS is the mean of the gray scale for pixels in an image. STD is the measurement of the dispersivity of the gray scale from the MGS. STD becomes small if the gray scales of pixels are approximated. It could also reflect the uniformity of image intensities. ENT measures the uniformity of the grayscale distribution. It reflects the non-uniformity on the complexity of texture in an image. It has its maximum value when the probability of the grayscale occurrence is the same.

6 Texture Features Based on the GLCM The co-occurrence matrix and texture features were initially used for the automated classification of rocks. The fourteen Haralick measures were used to extract useful texture information from the co-occurrence matrix. From then on the GLCM has been one of the commonly used tools for texture analysis because it can estimate image properties related to second-order statistics. An image with the size of pixels and gray levels could illustrate the frequency of pixel (i.e) at the position ) occurrence with gray level and in accordance with a distance d from a certain pixel at the position ( ) with gray level. Frequency is denoted by ( ) and its mathematical expression is ( ) = ( ), ( ) (5.7) Where ( ) denotes the coordinates of the image, pixel and are the gray scale. and denote the position offsets d is the step, is the direction and f is the gray level, theta is often taken in four directions (0, 45, 90, 135 ). Once the GLCM in a certain direction is achieved, ( ) needs to be normalized to ensure that its feature are not influenced by the regions limitation. The normalized ( ) is ( )= ( ) (5.8) If is 0, is taken as ( 1) If is 90, is taken as ( 1) If is 45 is taken as ( 1) ( 1)

7 HARALICK FEATURES EXTRACTION FROM FUSED PLACENTA Gray level co-occurrence matrices (GLCM) proposed by Haralick has become one of the most well-known and widely used texture measures. Let a two-dimensional image ( ), ( = 1, = 1,, ) have gray levels. A co-occurrence matrix depicts the joint gray-level histogram of the image (or a region of the image) in the form of a matrix with the dimensions of. The entries are the joint probability density of pairs of gray levels that occur at pairs of points separated by the displacement vector. Suppose ( ) denotes the cardinality of the set of pairs of points that have gray level values of i and j, for a displacement vector = ( ) = ( ), ( ) ( ) ( ) (5.9) Haralick Features describe the correlation in intensity of pixels that are next to each other in space. Haralick proposed fourteen measures of textural features which are derived from the co-occurrence matrix a well known statistical technique for texture feature extraction. It contains information about how image intensities in pixels with a certain position in relation to each other occur together. Texture is one of the most important defining characteristics of an image. The grey level co-occurrence matrix is the two dimensional matrix of joint probabilities ) between pairs of pixels separated by a distance in a given direction. The second order image histogram referred to as the Grey Level Co-occurrence Matrix (GLCM) of an image offers greater information about the inter-pixel relationship, periodicity and spatial grey level dependencies. This matrix is a source of fourteen texture descriptors.

8 (a) G L C M (b) G L C M (c) Figure 5.5 Asymmetric gray level co-occurrence matrices. (a) is original image, (b) is GLCM in direction (c) is GLCM in direction (Copyright The MathWorks. Incorporated.)

9 Figure 5.6 Symmetric gray level co-occurrence matrices. Left is the original image, right is GLCM in direction The first order statistics of an image can be obtained from mean and standard deviation. These are concerned with properties of individual pixels. The second order statistics of an image can be obtained from GLCM which accounts for the spatial inter-dependency or co-occurrence of two pixels at specific relative positions. Co-occurrence matrices are calculated for the directions of0, 45, 90, and 135. For each matrix, the fourteen features like Angular Second Moment, Contrast, Correlation, Sum of Squares or Variance, Inverse Difference Moment, Sum Average, Sum Variance, Sum Entropy, Entropy, Difference Variance, Difference Entropy, Information Measure of Correlation and Cluster Tendency are obtained for the synthesized placenta image. The homogeneity, contrast, entropy and energy are sensitive to the choice of the direction. The homogeneity and entropy supplies the indication on the dominancy values of the main diagonal on the basis of the frequencies of the problem. The energy supplies the information on the randomness of the spatial distribution.

10 127 First order statistics: Mean and standard deviation are concerned with properties of individual pixels. Second order statistics: It accounts for the spatial interdependency or co-occurrence of two pixels at specific relative positions Contrast Contrast measures the quantity of local changes in an image. It reflects the sensitivity of the textures in relation to changes in the intensity. It returns the measure of intensity contrast between a pixel and its neighborhood. Contrast is 0 for a constant image. It is the amount of local variation present in an image. If the amount of local variation is large, the contrast feature also has consistently higher values comparatively. If the gray scale difference occurs continually, the texture becomes coarse and the contrast becomes large. The texture becomes acute if the contrast has a small value. = ) (5.10) Correlation This feature measures how correlated a pixel is to its neighborhood. It is the measure of gray tone linear dependencies in the image. Feature values range from -1 to 1, these extremes indicating perfect negative and positive correlation respectively. and are the means and and are the standard deviations of ) and ), respectively. If the image has horizontal textures the correlation in the direction of 0 degree is often larger than those in other directions. It can be calculated as

11 128 = ( ) ) (5.11) Homogeneity Homogeneity measures the similarity of pixels. A diagonal gray level co-occurrence matrix gives homogeneity of 1. It becomes large if local textures only have minimal changes. = ) (5.12) Energy Energy also means uniformity, or angular second moment (ASM). The more homogeneous the image is, the larger the value. When energy equals to 1, the image is believed to be a constant image. = ) (5.13) Entropy Entropy is a measure of randomness of intensity image. = ( )log ( ( )) (5.14) Angular Second Moment Feature (ASM) Angular second moment feature is a measure of the uniformity of local gray scale distribution. If ( ) is centralized near the main

12 129 diagonal area the local gray scale distribution becomes uniform. It is a measure of homogeneity of the image. In a homogeneous image very few dominant gray tone transitions will be present. The matrix for the image will have fewer entries of large magnitude. It can be calculated using the formula, = ( ) (5.15) Energy, entropy, contrast, homogeneity and correlation features are often used among the 14 Haralick texture features to reveal certain properties about the spatial distribution of the texture image. Since real textures usually have so many different dimensions, these texture properties are not independent of each other. For instance, the energy measure generated from gray level co-occurrence matrix is also known as homogeneity and variance is a measure of contrast in images. Therefore, when choosing a subset of meaningful features from gray level co-occurrence matrix for a particular application, features do not have to be independent because a subset of fully independent features is usually hard to find. Haralick illustrated the applications of some textural features computed based on co-occurrence matrices. He employed distance co-occurrence matrices to compute angular second moment, contrast, correlation and entropy for categorization tasks for several kinds of images. The features computed based on the co-occurrence matrices have a general applicability for different kinds of images. Algorithm 2: Image Feature Extraction Input: Synthesized Image obtained from phase 1(Wavelet Decomposition and Image Fusion)

13 130 Output: Image Features for each Image where D is the collection of synthesized image. 1. For each Image do 2. Compute the grey level Co-occurrence Matrices in an neighborhood of the current pixel 3. ( ) 4. For each extract the fourteen features defined by Haralick 5. End for 6. Store the features fi in a file 5.4 RESULTS The Haralick features which are obtained from the ultrasound images are recorded in the following Table 5.1. This table shows the discriminating features that aid in the classification of normal placenta and placenta complicated by gestational diabetes mellitus. The features Mean, Contrast, Correlation, Entropy recorded in the Table 5.1 is obtained from equation 5.10, 5.11 and Table 5.1 Haralick Features for Ultrasound Placenta Images for sample images Images Mean Contrast Correlation Entropy Sum of squares Class Img e e e4 7.2 AN Img e e e5 1.1 AN Img e e e4 5.6 AN(GDM) Img e e e4 1.0 AN

14 131 Table 5.1 contd. Haralick Features for Ultrasound Placenta Images for sample images Images Mean Contrast Correlation Entropy Sum of squares Class Img e e e4 1.2 N Img e e e5 2.5 AN Img e e e4 1.7 AN Img e e e AN Img e e e4 1.2 N Img e e e N 5.5 CONCLUSION The ultrasound images of placenta which was decomposed using Haar Wavelet is fused using the Max approximation and mean detailed. It is already discussed earlier in table 3.1, 3.2, 3.3, 3.4 and 3.5. The fusion results can be obtained from 4.1, 4.2, 4.3 and 4.4. The Haralick features that were extracted from the wavelet fused ultrasound placenta, highlights on the characteristic features of the input image. These features form the basis for effective classification of placenta whether it is normal or complicated by gestational diabetes mellitus which will be discussed in the consequent chapters.

15 167 CHAPTER 8 CONCLUSION This research suggests wavelet based image fusion approach for the classification of ultrasound placenta images. The placenta is the organ that supports the growth of the fetus throughout the gestation period. The ultrasound is taken during the pregnancy to ensure the growth of the fetus. In order to sustain the growth of the fetus, there arises the need to monitor the morphology of the placenta, thereby the growth of the placenta. The growth or the age of the placenta contributes directly or indirectly to the fetal growth. In the Indian scenario, routine pelvic examination of pregnant women, placenta appears to be treated with less attention than the fetus or the gravid uterus. This research uses placenta images (10 weeks, 12 weeks, 15 weeks, 17 weeks and more than 20 weeks of both normal and GDM complicated Placenta) from the Mediscan (Chennai), Excel Diagnostics (Chennai) and Precision Diagnostics (Chennai). The research also uses placenta images from Imaging Consult (Online). The ultrasound images taken during the routine scan reveals the placenta. It holds the essential features regarding the complications which may increase the mortality rate of the fetus. The quality of the ultrasound image is usually low in resolution. Such images may not be directly suitable for medical diagnostics. The wavelet decomposition when applied on the ultrasound placenta images were able to improve the region which retains the essential characteristics. The image is subjected to various decomposition techniques in wavelet. It is found that the images obtained from Haar wavelet decomposition of the ultrasound placenta contained more information than the Daubechies, and

16 168 Symlet. As the decomposition level is increased, finer detail is retained. The decomposition performed at level four using Haar wavelet is found to have improved quality. During the pregnancy screening the ultrasound images are captured as sequence of images. This is due to the dynamic nature of the fetus. Each of these ultrasound images has specific information characteristics. When these images are fused together, a combination of relevant information is obtained which is required to assist the experts in decision making process. The routine pregnancy screening shows multiview of the ultrasound placenta and the fetus. The idea is to combine the best features from each image and to represent as a single image. The Image Fusion results in a single image containing more precise details than any of the source images. It employed min, mean, max approximation and min, mean and max detail image fusion methods. The image quality measures obtained from Haar wavelet decomposed and fused image is comparatively better when the images are fused using max approximation and mean detail method. It indicates that the ultrasound image fused using max approximation and mean detail has richness of information. It clearly shows that the wavelet decomposed images when subjected to image fusion increases the quality of information in an image. Thus the essential features that characterize the placenta are then extracted. This is supported by the results of various quality assessment methods performed on the image obtained from the different wavelet decomposition techniques. The Entropy, Mean, Standard Deviation, Root Mean Square Error, Peak Signal to Noise Ratio, Fusion Mutual Information, Normalized Cross Correlation, Normalized Absolute Error, Average Difference, Max Difference, Mean Square Error are the quality assessment methods used in this work. It is found that Fusion Mutual Information,

17 169 Entropy, Structural Content, RMSE and PSNR prove to be a benchmark in quality assessment methods in the case of ultrasound placenta images. The assessment of the ultrasound placenta at a regular scan using this technique brings out features that can be used to reduce the loss of the fetus. The ultrasound of a placenta surfaces the complications that are concealed beneath. The fusion of decomposed ultrasound placenta improves the uniqueness of the essential features which improve the classification accuracy. The onset of gestational diabetes mellitus changes the morphology of the placenta. But the variations are so minimal in the initial stages of pregnancy which escapes from human vision. The medical diagnoses on the placenta are possibly done during the later stages of second trimester. This raises the question of the survival of the fetus in the women complicated by GDM. There comes the need to classify the ultrasound placenta as normal or abnormal. The Haralick features obtained for the ultrasound image of placenta plays a significant role in the classification process. Fourteen features were obtained for the ultrasound placenta image which is subjected to Haar Wavelet decomposition and max approximation and max detail fusion. Of these features, Mean, Contrast, Correlation, Entropy and Sum of Squares showed significant discriminating features that classified the placenta. This is justified by the result which showed an increase in contrast in the case of ultrasound placenta which is complicated by GDM. The size of the placenta also plays a significant role in the classification of placenta complicated by GDM. The ultrasound placenta image subjected to wavelet decomposition and subsequent image fusion is then segmented using the watershed algorithm to extract the region of interest from the decomposed and fused multi-view ultrasound placenta. The statistical measurements like area and perimeter are obtained. It shows an

18 170 increase in the area and perimeter of the segmented placenta image which has GDM complications. The high values of major axis length and minor axis length strongly indicate the diabetic insult on the placenta. The statistical measurements obtained from the segmented ultrasound placenta image at early stages of pregnancy can minimize the loss due to GDM and can prevent placental abruption. Evaluation of these measurements are later stages of gestation may lead only to an increased morbidity or mortality rate of the fetus. In the Indian scenario, the estimation of placental volume is not a regular practice in the case of 2D ultrasound. Here, an attempt is made to estimate the placental volume using the Convex-Concave Hull model. The proposed method attempts to identify the changes in the ultrasound placenta at fifteen to twenty weeks of gestation under GDM conditions, which otherwise improves the risk of fetal destruction. The statistical measurements from the segmented placenta serve as a base for the volume estimation of the placenta. Placental volume is calculated using the linear measurements of placental thickness, height and width using the concave-convex shell formula. The clinical references of the Negroid race, the Dravidian sub race were compared against the statistical measurements obtained using Matlab 7.0. Based on these comparisons, the statistical measurements of the ultrasound placenta were classified as normal or abnormal placenta. The abnormal placenta is further classified into placenta complicated by Gestational Diabetes Mellitus and the placenta complicated by other reasons based on the volume features. The clinical references also show a very marginal difference between placenta with Beckwith Wiedemann Syndrome and placenta complicated by GDM. An increase in the volume measurement raises

19 171 suspicion of GDM. High volume of the placenta strongly indicates the diabetic insult on the placenta. Neural Network has been widely used to discover various biological discovery, drug discovery and health discovery. Even then, these techniques need to be used with great care in the medical applications. This is due to the sensitiveness of the medical data. Each learning algorithms may be superior for different medical data. The practitioners can choose appropriate algorithm as a solution for a particular problem. The decision to employ a network classifier varies from one application to another. It employed Radial basis neural network classifier on the statistical data obtained from Haralick features extraction. The performance of the Radial Basis Neural Network is compared against the other neural classifiers like Back Propagation, Perceptron and Feed Forward Neural Network. Radial Basis Neural Network showed 98.9% accuracy in the classification of the Haralick features of the ultrasound placenta. Feed Forward classifier gave 96% accuracy was closer to Radial Basis. The other classifier showed comparatively lower classification accuracy of 93%. The results suggest that Radial Basis can be employed in classifying the multi-view ultrasound placenta. Even then, these techniques need to be used with great care in the medical application as medical data are often very sensitive. The medical databases are of different types which consequently differ a lot. Integration and analysis of this valuable information is a tremendous task. The background knowledge or the domain knowledge for a particular problem is a crucial because the data in the medical field is heterogeneous, more ethical and social issues constraints apply to private medical domain.

20 172 The findings of this research are that Wavelet Image Fusion can be employed on the analysis on the growth of the placenta as seen in ultrasound at fifteen to twenty weeks of gestation complicated by Gestational Diabetes Mellitus otherwise paves way to the threat of fetal death. In future, the research can apply these methodologies on the extraction of stem cells from the placenta culture. Also, the possibility of creating a classifier using neural networks can be explored. The ultrasound images of placenta were obtained from Imaging Consult.