Chapter 6 CLASSIFICATION ALGORITHMS FOR DETECTION OF ABNORMALITIES IN MAMMOGRAM IMAGES

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1 CLASSIFICATION ALGORITHMS FOR DETECTION OF ABNORMALITIES IN MAMMOGRAM IMAGES The two deciding factors of an efficient system for the detection of abnormalities are the nature and type of features extracted and the classifier employed. In this chapter we carried out a detailed study of the suitability of two chosen feature sets GLCM and Wavelet coefficients. For the experiments, we selected distance measure, Multilayer Perceptron (MLP), Extreme Learning Machine (ELM) and group of Lazy classifiers. Standard database is used for all experiments. Performances of the systems are measured with class accuracy, Sensitivity and Specificity. The result obtained by the different classifiers against each feature set is compared.

2 6.1Introduction The human beings are the best pattern recognizers. But we do not know how the brain understand and recognize patterns. Pattern recognition is the study of how machines can observe the environment, learn to distinguish pattern of interest from their background, and make sound and reasonable decisions about the categories of the patterns. Automatic (machine) recognition, description, classification and grouping of patterns are important problems in a variety of engineering and scientific disciplines. Pattern recognition can be viewed as the categorization of input data into identifiable classes via the extraction of significant features or attributes of the data. Duda and Hart [Duda and Hart 1973], [Duda et.al. 21] define pattern recognition as a field concerned with machine recognition of meaningful regularities in noisy or complex environment. It encompasses a wide range of information processing problems of great practical significance from pattern recognition of simple patterns to complex patterns like breast tumor detection in medical diagnosis. Today, pattern recognition is an integral part of the intelligent systems built for decision making. Normally the pattern recognition processes make use of one of the following two classification strategies. i. Supervised classification in which the input patterns are identified as a member of a predefined class. ii. Unsupervised classification in which the patterns are assigned to a hitherto unknown class. In this chapter we focus on the classification of mammogram images using different supervised classification techniques such as simple distance measure, ANN, ELM and Lazy classifiers. Unsupervised classification techniques like clustering algorithms are dealt in the next chapter. 13 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

3 Classification Algorithms for Detection of Abnormalities in Mammogram Images For building successful classifiers, we have to define appropriate features capable of characterizing the image features. In this work two effective approaches are employed for feature extraction. In the first approach wavelet transformation is employed to extract features. A set of high valued wavelet coefficients selected from the approximation band form the feature vector. Experiments are conducted with two prominent wavelet decomposition schemes viz. Stationary Wavelet Transformations (SWT) and Discrete Wavelet Transformations. In each case representatives from different wavelet filter families are employed. In the second approach, texture features extracted using GLCM form the basis of classification. The feature vector is formed with contrast, energy, homogeneity and correlation values extracted from GLCM. A wide range of classifiers are chosen for the study. Classification experiments with the different features are carried out with distance measure, Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM) and a set of lazy classifiers. A systematic analysis of performance of the different feature set-classifier pair is carried out by employing the chosen dataset explained in section 3.2. Further for ANN, ELM and Lazy classifiers a feature reduction method is implemented to reduce the complexity. In the next session, a comprehensive review of related works is presented. This is followed by the description of the experiments carried out and detailed analysis of the results obtained. 6.2 Related Work Ferreira and Borges [Ferreira and Borges, 23] proposed and implemented a supervised classification algorithm for classification of radial, circumscribed, microcalcifications, and normal samples of mammogram Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 131

4 images using wavelet transformations. The authors also used a special set of coefficients as features and Euclidean distance for separating mammogram images into benign, malignant and normal. Soltanian-Zadeh et.al [Soltanian-Zadeh et.al, 24] presented an evaluation of the performance of four different texture and shape feature extraction methods for the classification of benign and malignant micro calcifications in mammograms. They extracted microcalcification clusters, texture and shape features using different approaches like conventional shape quantifiers, co-occurrence based method of Haralick [Soltanian-Zadeh et.al, 24] and multi level wavelet transformations. Rashed and Awad [Rashed and Awad, 26] developed a supervised diagnosis system for digital mammograms. In this model, a diagnosis process is done by transforming the image data into feature vectors using wavelets multilevel decomposition. This vector is used as the features for separating different mammogram classes. This model classified mammogram images into tumor types and risk level. The result reported is very encouraging. Rashed et al [Rashed et al, 27] also suggested a multiresolution analysis system for interpreting digital mammograms. This system is based on using fractional amount of biggest wavelets coefficients in multilevel decomposition. They used 25% of the Mini-MIAS images for creating a class core vector and the entire ROI s of mammogram images from Mini-MIAS database is classified by taking the minimum Euclidean distance measure from each mammogram images to the class core vector. A comparative study made by the Nithya and Santhi [Nithya and Santhi, 211a] on the second order statistical feature extraction methods shows significant results compared to other methods. The study used a 132 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

5 Classification Algorithms for Detection of Abnormalities in Mammogram Images sample of mammogram images from the DDSM database. The same authors [Nithya and Santhi, 211b] proposed another method incorporating GLCM features and ANN for the classification of normal and abnormal patterns in digital mammograms and reported sensitivity and specificity more than 9% for a sample set of digital mammogram images from the DDSM dataset. [Khuzi et.al, 29] proposed a method for the detection and classification of masses and non-masses in a mammogram images using GLCM features. This method extracted the features from the ROIs which were segmented using algorithms such as Otsu thresholding and K-means. The accuracy of the classification is measured with a sample set consisting of 2 abnormal and 2 normal images from the Mini-MIAS database. The work reported an accuracy of more that 8% for both Otsu thresholding techniques and 7% for K-Means. A hybrid feature reduction method namely Linear forward selection and genetic algorithm for reducing the GLCM feature sets was proposed by Vasantha and Bharathi [Vasantha and Bharathi, 21] [Vasantha and Bharathi, 211]. In this work 6 images from DDSM database and 118 images from Mini-MIAS database were used with decision tree classifier. They could achieve 86% accuracy with DDSM and 95% with Mini-MIAS Database. Using ANN and GLCM features, Abdulla et.al [Abdulla et.al, 211] proposed a method for detection of masses in digital mammogram and achieved 91% sensitivity and 84% specificity while classifying 9 mammogram images randomly selected from the Mini-MIAS database. Islam et al. [Islam et.al, 21] also proposed a classification method using ANN Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 133

6 and GLCM features to classify benign-malignant classes of mammogram images and achieved 9% sensitivity and 84% specificity. 6.3 Detection and Classification of Mammogram Images Using Different Distance Measures One of the simplest approach for classification is by employing a distance measure. The general principle of such a classification is that for each class in the domain of interest, a class core vector is formed by using the features extracted from a set of representative images from the class. If C k is the class core vector of k th class and F is the feature vector extracted from a test image I, then I k if dist(f, C k ) is minimum for some distance measures. In this section we discuss the classification experiments carried out with two prominent distance measures Euclidean and Bray Curtis. Both wavelet feature and GLCM features are considered for the experiments Classification of Mammogram Images Using Wavelet Transformation Features Image texture is a confusing measurement that depends mainly on the scale in which the data are observed. Different types of images have different types of textures. Textures of mammograms are irregular and it posses fuzzy like characteristics. Wavelet transformation is the best tool for analyzing images of these characteristics. We propose a modified version of the works proposed in [Ferreria and Borges, 23] and [Rashed et.al, 27] for classifying mammogram images using wavelet multiresolution analysis. SWT and DWT of an image result in a set of wavelet coefficients at different levels of decomposition. Of these, the approximation coefficients set is found to characterize the texture properties of the image. A subset from each level 134 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

7 Classification Algorithms for Detection of Abnormalities in Mammogram Images of transformation comprising of a predefined fraction of the biggest coefficients in that level is selected for forming feature vector. In the proposed approach, Region of Interest (ROIs) of size 124x124 is extracted from each mammogram images in the dataset. Each ROI in the dataset is subjected to wavelet decomposition using different types of wavelet filters. The decomposition is carried out up to 4 levels. For each class m, four class core vectors decomposition using the equation 6.1 C j m j C m are formed corresponding to the four levels of N 1 j = Am(i) (6.1) N i= 1 Where j = 1, 2, 3 & 4, the number of levels of the wavelet decomposition, N is the total number of ROIs in a classes m of the images in the dataset. j A is m the feature vector containing α % of the wavelet coefficients in level j of the transformed ROI belonging to class m and α is a predefined value. In order to classify a mammogram images, we extracted a set of 322 ROIs of size 124 x 124 pixels from the 322 mammogram image available in the Mini-MIAS database by identifying the center location of the abnormality of the mammogram images. The extracted ROIs contain benign, malignant and normal images. The class core vector for the classes normal, benign and malignant, are created by taking only 1% of the ROIs randomly selected from each class as the training set. The classification of new instance (ROI) is carried out by defining a distance measure Dist (A, m) as the distance of A from a class m, given by equation 6.2 j 1 l l Dist(A,m) = d(a - Cm) (6.2) j l= 1 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 135

8 where A is the feature vector of the test image, C m is the class core vector, j is the number of decomposition levels, m represents the number of classes in the image set and Dist(.) depends on the distance function used. With Euclidean distance measure d (A q i= 1 l - C l m ) is defined as: l l l l 2 d (A C ) = (A (i) C (i)) (6.3) m where A i (i) is the i th coefficient value in the feature vector of m j A, (i) C l m is the i th coefficient value in the C l, q is the number of wavelet transformation m coefficients used that depends on α and l denotes the level of the wavelet decomposition. To study the influence of the type of wavelet transformations, we conducted experiment with SWT and DWT. Also to compare the performance with different family of wavelet, we employed representatives from Daubechies, Haar and Biorthogonal wavelets. To study the impact of distance measure on classification performance, another distance measure Bray Curtis [Faith et.al, 1987] [Kadir et.al, 212] defined by equation 6.4 is also used. 1 l Dist(A,m) = d (A l Cm) (6.4) j l l and d (A - C ) can be defined as m q l l A (i) Cm (i) l l 1 d (A - Cm ) = (6.5) j i= 1 l l A (i) + C (i) j k= 1 m 136 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

9 Classification Algorithms for Detection of Abnormalities in Mammogram Images Results and Discussion We implemented the above algorithms in MATLAB and tested the performance of the algorithm using a dataset consisting of three different classes of images namely Normal (N), Benign (B) and Malignant (M). In order to extract wavelet coefficients features, we have used two different types of wavelet transformations viz. Stationary Wavelet Transformation and Discrete Wavelet Transformations. The class core vector is created by taking 1% of mammogram images randomly from each class of images in the dataset. For testing we have chosen 162 mammogram ROIs randomly selected from the dataset which comprises of 98 normal images, 38 benign and 26 malignant images. The wavelet coefficients are generated using three wavelets families. The filters used in each family are the Daubechies-4, Daubechies-8 and Daubechies-16 from Daubechies family, Haar wavelet from Haar family and Bior2.8 from the Biorthogonal family. For both SWT and DWT, four levels of decomposition are applied resulting in four sets of wavelet coefficients. Experiment is conducted with different values of α (the fraction of wavelet transformation coefficients chosen). The distance measures namely Euclidean and Bray Curtis are used for calculating the distance between the class core vector and the feature vector of the test images. A class label is attached to each test image based on the minimum distance criteria Performance Analysis of SWT Features The classification results of 162 mammogram ROIs using Stationary Wavelet Transformation with the two different distance measures are given in Table 6.1 to Table 6.4. These tables show the confusion matrices generated during the classification of images. Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 137

10 Table 6.1: Classification of mammogram images using Daubechies filters in SWT (Euclidean) Coef. In Daubechies % Db4 Db8 Db16 N B M T N B M T N B M T N B M T N B M T N B M T N B M N: Normal image B: Benign image M: Malignant image T: Total Table 6.2: Classification of mammogram images using Haar and Biorthogonal filters in SWT (Euclidean) Cof. In % Haar Biorthogonal N B M T N B M T N B M T N B M T N B M T N B M T N: Normal image B: Benign image M: Malignant image T: Total 138 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

11 Classification Algorithms for Detection of Abnormalities in Mammogram Images Table 6.3: Classification of mammogram images using Daubechies filters in SWT (Bray Curtis) Coef. Daubechies In % Db4 Db8 Db16 N B M T N B M T N B M T N B M T N B M T N B M T N B M T N: Normal B: Benign M: Malignant T: Total Table 6.4: Classification of mammogram images using Haar and Biorthogonal Filters in SWT (Bray Curtis) Coef. In % Haar Biorthogonal N B M T N B M T N B M T N B M T N B M T N B M T N: Normal B: Benign M: Malignant T:total Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 139

12 Based on the above tables, the performance of the classification algorithms using different wavelet families, different distance measures and different α(% of wavelet transformation coefficients) in SWT are evaluated and they are shown in the Table 6.5 to Table 6.8. In addition to this, we also evaluated the performance of the classifiers using two important parameters Sensitivity and Specificity defined in chapter 3 based on the risk level of the classification. The above classification algorithm classifies mammogram ROIs into Normal, Benign and Malignant type. Out of these three classes malignant images pose more risk (cancerous) and need further investigation. Benign types are non cancerous and can be treated as normal mammogram images. Based on this, we evaluated Specificity and Sensitivity of the SWT based classification algorithm employing Euclidean and Bray Curtis measure as shown in Table 6.9 and 6.1. Table 6.5: Successful classification rate (in %) of mammogram images using discrete stationary Daubechies filters (Euclidean) Coef In % Db4 Db8 Db16 N B M Perfor Perfor N B M mance mance N B M N : Normal B: Benign M:Malignant Table 6.6: Successful classification rate (in %) of mammogram images using discrete stationary Haar and Biorthogonal filters (Euclidean) Coef. In Haar Biorthogonal % N B M Performance N B M Performance N : Normal B: Benign M:Malignant Perfor mance Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

13 Classification Algorithms for Detection of Abnormalities in Mammogram Images Table 6.7: Successful classification rate (in %) of mammogram images using discrete stationary Daubechies filters (Bray Curtis) Db4 Db8 Db16 Coef. Perfor Perfor In % N B M N B M N B M mance mance N: Normal B: Benign M: Malignant Perfor mance Table 6.8: Successful classification rate (in %) of mammogram images using discrete stationary Haar and Biorthogonal filters (Bray Curtis) Coef. Haar Biorthogonal In % N B M Performance N B M Performance N: Normal B: Benign M: Malignant Table 6.9: Performance of the classifiers evaluated based on Sensitivity and Specificity parameters using different Wavelet filters in SWT (Euclidean) Wavelet Filter: Wavelet Filter: Wavelet Filter: Wavelet Filter: Wavelet Filter: Coef. db4 db8 db16 Haar Biorthogonal In % SN SP SN SP SN SP SN SP SN SP SN = Sensitivity SP = Specificity Table 6.1: Performance of the classifiers evaluated based on Sensitivity and Specificity parameters using different Wavelet filters in SWT (Bray Curtis) Wavelet Filter: Wavelet Filter: Wavelet Filter: Wavelet Wavelet Filter: Coef Db4 Db8 Db16 Filter: Haar Biorthogonal In % SN SP SN SP SN SP SN SP SN SP SN = Sensitivity SP=Specificity Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 141

14 From the above tables, a summary of the performance of the classifiers is given below. i) Euclidean distance measure with α = 25% The overall performance of the classification is 75.93% in Biorthogonal filter followed by 75.31% in db16 wavelet filter. Out of 98 normal mammogram images, 78 (79.59%) images are correctly classified using Biorthogonal and db16 wavelets. Out of 38 benign images 3 (78.95%) images are correctly classified using the Biorthogonal wavelet filter. Out of 26 malignant images 16 (%) images are correctly identified and labeled by the Haar wavelet filter whereas it is % for all other wavelet filters. The optimum sensitivity is obtained in Haar wavelet filter with % followed by all other wavelet filters with sensitivity 57.69%. The highest specificity (93.38%) is obtained in Biorthogonal wavelet filter, which is followed by all Daubechies filters with 92.65%. ii) Euclidean distance measure with α = % The overall performance of the classification is 75.31% obtained with db16 filter. Out of 98 normal mammogram images, 78 (79.59%) images are correctly classified using db8, db16 and Haar wavelet filter Out 38 benign images 28 (73.68%) images are correctly classified using db16 and Biorthogonal wavelet filter. Out of 26 malignant images 16 (%) images are classified correctly using four wavelet filters. 142 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

15 Classification Algorithms for Detection of Abnormalities in Mammogram Images The sensitivity is % for all wavelet filters used in the experiment. The optimum specificity (92.65%) is obtained for all Daubechies filters followed by specificity (91.91%) for Haar and Biorthogonal filters. The performance of the different wavelet filters with different α are shown in Figure 6.1 to 6.4. Classification performance in% db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.1: Classification performance of mammogram images using 25% wavelet transofrmation coefficients ( Euclidean distance) C l a s s i f i c a t i o n p e r f o r m a n c e i n % db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.2: Classification performance of mammogram images using % wavelet transformation coefficients (Euclidean Distance) Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 143

16 C l a s s i f i c a t io n p e r f o r m a n c e i n % db4 db8 db16 Haar Bio rthog onal Nor ma l beni gn Malign ant Over all Figure 6.3: Classification performance of mammogram images using 75% wavelet transformation coefficients (Euclidean Distance) C la s s i f ic a t i o n p e r f o r m a n c e i n % db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.4: Classification performance of mammogram images using % wavelet transformation coefficients (Euclidean Distance) iii) Bray Curtis distance measure with α = 25% The overall performance of the classification is 75.31% obtained with db16 wavelet filter. Out of 98 normal mammogram images, db16 wavelet filter classified 77 (78.57 %) images correctly. 29 (76.32%) benign images are identified by db16 wavelet filter from the 38 benign images in the dataset. 144 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

17 Classification Algorithms for Detection of Abnormalities in Mammogram Images Using Haar wavelet filter, out of 26 malignant images, 16 (65.38%) images are correctly classified. The highest Sensitivity value obtained is 65.38% for Haar wavelet filter. The highest Specificity value is 9.44% for wavelet filters such as db8, db16 and Biorthogonal. iv) Bray Curtis distance measure with α = % The overall performance of the classification is 75.31% obtained with db16 wavelet Filter. Out of 98 normal mammogram images, db16 wavelet filter classified 77 (78.57%) correctly. 29 (76.32%) benign images are identified by db16 wavelet filter from the 38 benign images. Using Haar wavelet filter, out of 26 malignant images, 16 (65.38%) images are correctly classified. Optimum sensitivity value obtained is 65.38% for Haar wavelet filter and specificity 9.44% for db16 and Biorthogonal filters. The result obtained establishes the fact that increasing α beyond a limit will not improve the performance. The limit is to be found empirically. Smaller α results compact feature vector and hence the classification process becomes faster. The overall performance of the classification using different wavelet family with different α and Bray Curtis distance measures are shown in Figure 6.5 to 6.8. Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 145

18 Classification performance in% db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant over al l Figure 6.5: Classification performance of mammogram images using 25% wavelet transofrmation coefficients ( Bray Curtis distance) Classification performance in% db4 db8 db16 Haar Bio rtho gonal Nor ma l Beni gn Malign ant over al l Figure 6.6: Classification performance of mammogram images using % wavelet transformation coefficients (Bray Curtis Distance) Classification performance in% db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant over al l Figure 6.7: Classification performance of mammogram images using 75% wavelet transformation coefficients (Bray Curtis Distance) 146 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

19 Classification Algorithms for Detection of Abnormalities in Mammogram Images C l a s s i f ic a t i o n p e r f o r m a n c e i n % Nor ma l Beni gn Malign ant over al l db4 db8 db16 Haar Bio rthog onal Figure 6.8: Classification performance of mammogram images using % wavelet transformation coefficients (Bray Curtis Distance) Out of the different filters used in SWT, Biorthogonal and db16 found to outperform others in terms of overall classification accuracy. When it comes to the accurate classification of malignant types, Haar filter has an edge over others. In general Euclidean distance measure performed slightly better than Bray Curtis Performance Analysis of DWT Features The classification results of 162 mammogram ROIs using Discrete Wavelet Transformation (DWT) and the two different distance measures are given in Table 6.11 to Table Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 147

20 Table 6.11: Classification of mammogram images using Daubechies filter in DWT (Euclidean) Coef. Wavelet Filter : Daubechies in % db4 db8 db16 N B M T N B M T N B M T N B M T N B M T N B M T N B M T N : Normal Images B:Benign Images M: Malignant Images T:Total Table 6.12: Classification of mammogram images using Haar & Biorthogonal filters in DWT(Euclidean). Coef. In % Wavelet filter :Haar Wavelet filter : Biorthogonal N B M T N B M T N B M T N B M T N B M T N B M T N: Normal Images B: Benign Images M: Malignant Images T:Total 148 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

21 Classification Algorithms for Detection of Abnormalities in Mammogram Images Table 6.13: Classification of mammogram images using Daubechies wavelet filters in DWT(Bray Curtis). Coef. Wavelet filter : Daubechies In % Db4 Db8 Db16 N B M T N B M T N B M T 25 N B M T N B M T N B M T N B M T N: Normal Images B: Benign Images M: Malignant Images T:Total Table 6.14: Classification of mammogram images using Haar & Biorthogonal wavelet filters in DWT(Bray Curtis). Coef. In % Wavelet Filter : Haar Wavelet Filters : Biorthogonal N B M T N B M T N B M T N B M T N B M T N B M T N: Normal image B: Benign image M: Malignant image T: Total Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 149

22 From the Tables 6.11 to 6.14, we evaluated the classification accuracy corresponding to different wavelet filters, different values of α and the measures Euclidean and Bray Curtis. Results obtained are summarized in Table 6.15 to Further Sensitivity and Specificity are also evaluated for each experiment and is given in Table 6.19 and 6.2. Table 6.15: Successful classification rate (in %) of mammogram images using discrete Daubechies wavelet decomposition (Euclidean) Db4 Db8 Db16 Coef. In % Perfor Perfor N B M N B M N B M mance mance N: Normal B: Benign M: Malignant Perfor mance Table 6.16: Successful classification rate (in %) of mammogram images using Haar & Biorthogonal discrete wavelet decomposition (Euclidean) Coef. Haar Biorthogonal In % N B M Performance N B M Performance N : Normal B: Benign M:Malignant Table 6.17: Successful classification rate (in %) of mammogram images using discrete Daubechies wavelet Decomposition (Bray Curtis) Coef. Db4 Db8 Db16 In % N B M Perfor mance N B M Perfor mance N B M Perfor mance N : Normal B: Benign M:Malignant 1 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

23 Classification Algorithms for Detection of Abnormalities in Mammogram Images Table 6.18:-Successful classification rate (in %) of mammogram images using discrete Haar & Biorthogonal wavelet decomposition (Bray Curtis) Coef. Haar Biorthogonal In % N B M Performance N B M Performance N : Normal B: Benign M:Malignant Table 6.19: Performance of the classifiers evaluated based on Sensitivity and Specificity parameters using different Wavelet filters in DWT (Euclidean) Wavelet Wavelet Filter: Wavelet Wavelet Wavelet Filter: Coef Filter: Db4 Db8 Filter: Db16 Filter: Haar Bio In % SN SP SN SP SN SP SN SP SN SP SN = Sensitivity SP=Specificity Table 6.2: Performance of the classifiers evaluated based on Sensitivity and Specificity parameters using different Wavelet filters in DWT (Bray Curtis) Wavelet Filter: Wavelet Filter: Wavelet Filter: Wavelet Filter: Wavelet Filter: Coef Db4 Db8 Db16 Haar Biorthogonal In % SN SP SN SP SN SP SN SP SN SP SN = Sensitivity SP=Specificity An analysis of the performance of the different wavelet filters based on Table 6.15 to 6.2 is given below. i) Euclidean distance with α = 25% Db8 wavelet filter gives the highest overall recognition rate 8.25%. Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 151

24 Out of 98 normal mammogram images 73 (74.49 %) images are correctly classified with db8 wavelet filter. % classification accuracy is obtained for benign type with db8 as well as db16wavelet filter. Out of 26 malignant images, 23 (88.46 %) images could be correctly identified with Biorthogonal wavelet filter and is much better than the values obtained for all other wavelet filters. Using α = 25% wavelet coefficients, we obtained 88.46% sensitivity in Biorthogonal wavelet filter followed by 73.8% sensitivity for all other wavelet filter. Using α = 25% wavelet coefficients, the highest specificity obtained is 87.% for db8 and db16 wavelet filter. ii) Euclidean distance with α = % Except Biorthogonal (78.4%), all other wavelet filters gave 82.72% recognition rate. Out of 98 normal images 74 (75.51%) images are correctly classified with all wavelet filters except Biorthogonal. All the 38 (%) benign images in the dataset are classified exactly with all wavelet filters except Biorthogonal. As in the case of α = %, the classification accuracy obtained for malignant type with Biorthogonal is 88.46% whereas other wavelet filters gave only 84.62%. Using α = %, obtained highest sensitivity (88.46%) using Biorthogonal wavelet filters. 152 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

25 Classification Algorithms for Detection of Abnormalities in Mammogram Images The highest Specificity obtained is 86.76% for all wavelet filters except Biorthogonal filter. It is observed that the performance does not improve with higher values of α (75% and %). With Euclidean distance and DWT, α = % is found to be ideal. Inclusion of more coefficients to the feature vector found to have a negative impact on the classification accuracy. Of the wavelet filter db8 is the best choice if overall classification accuracy is the criteria whereas Biorthogonal is found to be the best choice for detecting malignant cases. The Figures 6.9 to 6.12 show the variation of classification performance with different α for DWT and Euclidean distance measure. Classification performance in% db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.9: Classification performance of mammogram images using 25% DWT wavelet transofrmation coefficients ( Euclidean distance) Cl as s i fi cat i o n pe r fo rman ce i n % db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.1: Classification performance of mammogram images using % DWT wavelet transofrmation coefficients ( Euclidean distance) Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 153

26 C l a s s i f i c a t io n p e r f o r m a n c e i n % db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.11: Classification performance of mammogram images using 75% DWT wavelet transofrmation coefficients ( Euclidean distance) C l a s s i f ic a t i o n p e r f o r m a n c e i n % db4 db8 db16 Haar Bio rtho gonal Nor ma l Beni gn Malign ant Over all Figure 6.12: Classification performance of mammogram images using % DWT wavelet transofrmation coefficients ( Euclidean distance) iii) Bray Curtis distance measure The best results obtained are summaried below: The highest classification accuracy 81.48% is obtained with α = 75% and wavelet filter db8 and db16. Out of 98 normal mammogram images 73 (74.49%) images are classified correctly with the different filters except Biorthogonal for α = 25%, % and 75%. 154 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

27 Classification Algorithms for Detection of Abnormalities in Mammogram Images Almost all benign images are classified correctly with all wavelet filters for α = 25%, % and 75%. Out of 26 malignant images 21 (8.77 %) are correctly classified with wavelet filters except db4 (α = 75%). The optimum sensitivity obtained is 8.77% for all wavelet filters except db4 filter using α = 75%. The highest specificity obtained is 86.76% for all Daubechies filters withα = 25%, % and &75%). By comparing the overall performance of the classification with different α and Bray Curtis measure, α = 75% make excellent results compared to other α values. The performance of the classification of mammogram images using different wavelet filters, Bray Curtis distance measure and different α values are shown in Figure 6.13 to C l a s s i f i c a t io n p e r f o r m a n c e i n % db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.13: Classification performance of mammogram images using 25% DWT wavelet transofrmation coefficients (Bray Curtis) Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 155

28 Classification performance in% db4 db8 db16 Haa r Bio rtho gonal Nor ma l Beni gn Malign ant Over all Figure 6.14: Classification performance of mammogram images using % DWT wavelet transofrmation coefficients (Bray Curtis) Classification performance in% db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.15: Classification performance of mammogram images using 75% DWT wavelet transofrmation coefficients (Bray Curtis) Classification performance in% db4 db8 db16 Haar Bio rthog onal Nor ma l Beni gn Malign ant Over all Figure 6.16: Classification performance of mammogram images using % DWT wavelet transofrmation coefficients (Bray Curtis) 156 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

29 Classification Algorithms for Detection of Abnormalities in Mammogram Images Finally the overall performance of the classification algorithms employing DWT and the two distance measures are summarized in Table Table 6.21: Overall performance of the classification algorithm employing DWT with different α values. Distance Wavelet Coefficients Normal Benign Malignant Performance measure (α) In % Euclidean Bray Curtis From Table 6.21 it is evident that Euclidean distance measure is better than Bray Curtis in the classification of mammogram based on DWT features. The Figure 6.17 shows the overall performance of the classification algorithms in DWT with the two different distance measures. Classification performance in% Nor ma l Beni gn Malign ant Over all E ucl idean Br ay Cu rtis Figure 6.17: Overall performance of the classification accuracy in DWT based on two different distance mesures Comparative Performance Evaluation of SWT and DWT A comparative analysis of the performance of SWT and DWT is carried out on the basis of the classification accuracy obtained. The results clearly show that the classification of mammogram images using DWT is far better than SWT for abnormal images in the dataset. This is because the Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 157

30 DWT coefficients have the ability to characterize the varying nature of pixel intensities around the abnormal area of the image. Even though SWT coefficients are redundant in nature, they under perform in characterizing the variation of pixel intensity in abnormal area of ROI. The outcome of the experiments is projected in the following Figures 6.18 to Classification performance in% Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S tati onar y W avele t Tr ansfo rm ation Discr ete W avele t Tr ansform ation Classification performance in% Figure 6.18: Classification performance of Daubechies db4 Wavelet (Euclidean distance) Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S tationar y W avele t Tr ansform ation Discr ete W avele t Tr ansform ation Figure 6.19: Classification performance of Daubechies db4 Wavelet (Bray Curtis distance) 158 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

31 Classification Algorithms for Detection of Abnormalities in Mammogram Images Classification performance in% Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S ta ti onar y W avele t Tr ansfo rm ation Discr ete W avelet Tr ansform ation Figure 6.2: Classification performance of Daubechies db8 Wavelet (Euclidean distance) C la s s i f ic a t i o n p e r f o r m a n c e i n % Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S ta tionar y W avele t Tr ansform ation Discr ete W avele t Tr ansform ation Figure 6.21: Classification performance of Daubechies db8 Wavelet (Bray Curtis distance) Classification performance in% Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S tati oanr y W avele t Tr ansfo rm ation Discr ete W avelet Tr ansform ation Figure 6.22: Classification performance of Daubechies db16 Wavelet (Euclidean distance) Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 159

32 Classification performance in% Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S tati onar y W avele t Tr ansfo rm ation Disc rete W avele t Tr ansform ation Classification performance in% Figure 6.23: Classification performance of Daubechies db16 Wavelet (Bray Curtis distance) Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S tati onar y W avele t Tr ansfo rm ation Disc rete W avele t Tr ansform ation Classification performance in% Figure 6.24: Classification performance of Haar wavelet (Euclidean Distance) Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malignant Per for manc e S tati onar y W avele t Tr ansfo rm ation Discr ete W avelet Tr ansform ation Figure 6.25: Classification performance of Haar Wavelet(Bray Curtis) 16 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

33 Classification Algorithms for Detection of Abnormalities in Mammogram Images Classification performance in% Classification performance in% Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S tati onar y W avele t Tr ansfo rm ation Disc rete W avele t tra nsfor mati on Figure 6.26:Classification performance of Biorthogonal Wavelet (Euclidean Distance) Nor mal Beni gn Malign ant Per for manc e Nor ma l Beni gn Malign ant Per for manc e S tati onar y W avele t Tr ansfo rm ation Disc rete W avele t Tr ansform ation Figure 6.27 : Classification performance of Biorthogonal Wavelet(Bray Curtis) Summary of Experiments with Wavelet Features and Distance Measures Based on the experiments carried out with SWT and DWT ( to ), we arrived at the following: The accuracy obtained in Euclidean distance measure is better than the Bray Curtis distance measure in all cases. Discrete Wavelet Transformation results in more distinguishing feature vector than Stationary Wavelet Transformation and hence outperforms DWT in classifications. Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 161

34 Stationary Wavelet Transformation gives slightly better results in classifying normal images. Classification accuracy obtained in the case of benign type images are percent for all the Wavelet filters used in the classification except Biorthogonal wavelet. Classification accuracy obtained for malignant type images are also high and same for most of the wavelet filters used in the classification. The percentage of wavelet transformation coefficients, α, influences the recognition accuracy Classification of Mammogram Images using GLCM Features The four different GLCM features such as Contrast, Energy, Homogeneity and Correlations in four different orientations are extracted as explained in section of chapter 3. These features are combined to form a unique feature vector for the classification of mammogram images. The classification is done using two different distance measures viz Euclidean and Bray Curtis. We extracted a set of 322 ROIs from the original mammogram images from the Mini-MIAS database. This database mainly contains three types of images normal, benign and malignant. ROIs of size 124x124 pixels around the origin of abnormality are extracted for both benign and malignant classes. In the case of normal images, ROIs are extracted around the center of the mammogram images. 1% of the ROIs are randomly selected for extracting the GLCM features and kept as training set. The GLCM features extracted in different orientations are combined to form a feature vector and subsequently they are used for constructing the class core vector for the 162 Wavelet and soft computing techniques in detection of Abnormalities in Medical Images

35 Classification Algorithms for Detection of Abnormalities in Mammogram Images training purpose. The class core vector of the GLCM feature is constructed using equation (6.6). N i 1 C m = Ai N i= 1 (6.6) where C m is the m th class core vector. N is the number of ROIs selected to produce the class core vector, A i is the set of 16 different features generated from GLCM and m is the number of classes of images in the dataset. In the testing part, each ROIs belonging to the test group is classified using the distance between the feature vector and class core vector. The system automatically classifies the test image by finding the minimum Euclidean distance between the feature vector of the test image and the class core vector of each class using equation (6.7) Dist( A, Cm ) = [ A( i) Cm ( i)] (6.7) i= 1 where A is the feature vector of the test image, and C m is the class core vector for each class m. For comparative analysis we also employed Bray Curtis distance measure defined in equation (6.8). 16 i A( i) C m i= 1 Dist (A, C m ) = 16 (6.8) i A( i) + C i= 1 m Results and Discussion We implemented the GLCM feature based classification algorithms and applied on the data samples taken from the Mini-MIAS dataset consisting of Normal, Benign and Malignant images. Class core vectors are created Wavelet and soft computing techniques in detection of Abnormalities in Medical Images 163

36 using 1 % of images from each class, selected at random. We have chosen 162 mammogram ROIs from the dataset which contains 98 normal, 38 benign and 26 malignant images for testing purpose. The classification results obtained using GLCM feature and the two different distance measures are shown in Table 6.22 and corresponding percentage (%) of accuracy of the classification is given in Table Table 6.22: Classification of mammogram images using GLCM feature in Euclidean and Bray Curtis distance measures Euclidean Distance Measure Bray Curtis Distance measure M B N Total M B N Total M B N N: Normal B: Benign M: Malignant The Table 6.22 is the confusion matrix generated based on the classification algorithm. Out of 98 normal images, the GLCM based classification algorithm correctly classified and labeled 77 images with Euclidean distance and 71 images with Bray Curtis distance measure. In respect of 38 benign images, 33 images are correctly classified and labeled by Euclidean distance and 3 images by Bray Curtis distance. Finally out of 26 malignant images, 24 images are correctly classified using the Euclidean distance where as 23 images are classified correctly in Bray Curtis distance. The overall performance of the classifications using GLCM features is plotted in Figure Table 6.23: Successful classification rate (in %) of mammogram images using GLCM features in Euclidean and Bray Curtis distance measures. Distance Measure Normal in % Benign in % Malignant in % Overall Performance(%) Sensitivity in % (SN) Specificity in % (SP) Euclidean Bray Curtis Wavelet and soft computing techniques in detection of Abnormalities in Medical Images