CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS

130 CHAPTER 6 DETECTION OF MASS USING NOVEL SEGMENTATION, GLCM AND NEURAL NETWORKS A mass is defined as a space-occupying lesion seen in more than one projection and it is described by its shapes and margin properties. This chapter addresses the combination of novel segmentation through wavelet based edge detection and morphological operations. It also dwells on feature extraction through the formulation of GLCM for mass classification. 6.1 INTRODUCTION Mass is classified into two types: benign and malignant. A benign mass is a mass with a regular shape, i.e. smoothly marginated and considered as a normal one. But a malignant mass is a mass with irregular shape, i.e. characterized by an indistinct border that becomes more spiculated with time and is considered as cancerous one. Owing to the slight differences in the x- ray attenuation between mass and benign glandular tissue, they appear with low contrast and are very often blurred. The abnormal cases with masses are further classified as circumscribed masses, spiculated masses and ill-defined masses. Because of low contrast, they appear embedded in and camouflaged by varying densities of parenchymal tissue structures. Hence, it is very difficult not only to visually detect them but also to distinguish them in wavelet domain, i.e. from the coefficients related to suspicious masses to the ones related to the background. Thus, mass detection remains a significant

131 topic in breast cancer detection. This chapter tries to strengthen the mass detection rate by the proposed scheme. It consists of three major steps. The first involves the novel segmentation through addition of multiscale wavelet based edge detection and morphological operations. The second is the feature extraction from GLCM, and the third step is classification using neural network. This chapter is organized into four sections including this introduction section. Section 6.2 illustrates the functioning of the proposed mass detection scheme. Section 6.3 provides extensive experimental results obtained through the proposed method. Section 6.4 winds up with the summary of the findings. 6.2 PROPOSED METHOD OF MASS DETECTION The various steps involved in the implementation of the proposed mass detection method are illustrated via the flow chart in Figure 6.1. In the proposed scheme the mass suspected region is cropped and processed separately for segmenting the mass. During processing the cropped subimage is decomposed through multiscale wavelets and thereby combines it with morphological filtered image. Thus the resultant image will be mass segmented one. Five co-occurrence matrices are calculated from the resultant k image, four matrices in different orientations,( k 1,2,3,4) and the 4 fifth matrix is constructed as the mean of the preceeding four matrices calculated at different angles. Nineteen independent statistical features are extracted from the GLCM s and given as inputs to neural network. The BPN is used to classify it as benign or malignant mass. It mainly consists of four stages: RoI selection and preprocessing, segmentation, feature extraction through GLCM and classification.

132 Mammogram Image RoI Selection through Cropping Morphological Filtering Wavelet Decomposition Edge Detection Mass Segmented Image Formulation of GLCM Statistical Feature Extraction Classification Result Figure 6.1 Flow chart of Mass Detection System

133 6.2.1 RoI selection and preprocessing The images collected from MIAS database and hospital with masses findings are used in this experiment. All these images (MIAS database and hospital images) are digitized at a resolution of 1024 1024 pixels and 8 bit accuracy (gray level). Among the data sets, 50% of any of the image comprised a background with more of noise. Hence, to remove the unwanted portions using the mass location supplied by MIAS for each mammogram, the RoI is obtained by the crop operation in image processing. Cropping cuts off the unwanted portions of the image. Thus the noise and unwanted background are eliminated. The benefit of crop operation is its dimension can be flexibly chosen depending upon the mass radius in the image. Here a crop size of 256 256 is used. It is very difficult to isolate the breast tissue from its background as the breast tissue near the surface of the body is very fatty and appears very dark on the mammographic film. Hence it should be preprocessed. Here the preprocessing performed is two fold: Global thresholding and histogram equalization. These operations are similar to preprocessing operations performed and delineated in the previous chapter. 6.2.1.1 Global Thresholding Thresholding is performed to enhance the image. With reference to the thresholding values discussed in subsection 5.2.1.2 for microcalcification detection, the threshold values for mass RoI pixels are determined by trial and error basis. Experimentally threshold in this case is found to be 160 and above. Hence two threshold values are selected lower (160) and upper (255). The pixels with gray level between the lower threshold and the upper

134 threshold are retained and others are set to zero. Hence the thresholding result in representing the breast tissue between the gray level thresholds and the background by gray level zero. 6.2.1.2 Histogram Equalization The image is subjected to histogram equalization to improve the image quality particularly to enhance the gray level near the edge. Histogram equalization is applied to assign the intensity values of pixel in the input image such that the output image contains uniform distribution of gray values. 6.2.2 Image Segmentation Image segmentation tries to isolate the breast tissue from the background image. It is done by combining morphological filtered image with multiscale wavelet based edge detected image. 6.2.2.1 Edge detection The main problem in the edge detection process is that it is very confusing to detect the edge. As the mass features are buried in the mammogram of low contrastness, it is very difficult to distinguish them in the wavelet domain. Further, in using wavelet at dyadic scales, a lesion may be too blurred on one scale and too fragment on the next. Hence it is not sufficient for the detection of mass (Laffont et al 2001). To overcome this problem, the steps followed are two fold: preprocessing, and the subimage is subjected to multiscale wavelet transform. The multiscale wavelet transform is selected, since it is a popular technique for multiresolution representation. Also it does not introduce phase distortions in the decomposed images. And no bias is introduced in the horizontal and vertical directions as would occur

135 with a separable transform. Multiscale edge detectors smooth the signal at various scales and detect sharp variation points as edge curves. The multiscale wavelet based edge detection methodology is as follows: Let the term 2-D smoothing function be described by any function ( xy, ) whose integral over x and y is equal to 1 and converges to 0 at infinity. For example, ( xy, ) can be a Gaussian function. Let suppose that ( xy, ) is differentiable with respect to x and y and define respectively as 1 ( xy, ) and 2 ( x, y). The image f(x,y) is smoothed at different scales s by a convolution with ( xy, ). Then compute the gradient vector ( f* )( xy, ). s s Edge points can be located from the two components Wf( xy, ) and 1 s W f( xy, ) of the wavelet transform, that are computed through the gradient 2 s vector with respect to x and y respectively. For discrete images the modulus maxima evolution across scales, recognized into chains of local maxima recover the edge curves. 6.2.2.2 Morphological Filtering Morphological filters are nonlinear signal transformations that locally modify the geometric features of the signals. The crux of mathematical morphology can be represented by a combination of two simple operations, namely erosion and dilation. The process of expanding the binary image from its original shape is called dilation. Erosion is the counter process of dilation, i.e. it shrinks the image. Let Z denote the set of integers and f(x,y) is a discrete image whose domain set is given by {(x,y) N l N l } here N l, N 2 Z. A structuring element B is a subset in Z 2 with a simple geometrical shape. The erosion and dilation operations are mathematically expressed as given in equation (6.1) and (6.2).

136 ( f B)( x, y) max f ( x t, y t ), ( t, t ) B 1 2 1 2 (6.1) ( f B)( x, y) max f ( x t, y t ), ( t, t ) B (6.2) 1 2 1 2 On the other hand, the opening and closing are respectively defined as in equations (6.3) and (6.4) ( f B)( x, y) [( f B) B]( x, y) (6.3) ( f B)( xy, ) [( f B) B]( xy, ) (6.4) Opening is based on the morphological operations, erosion and dilation. Opening smoothens the inside of the object contour, breaks narrow strips and eliminates thin portions of the image, i.e. the opening operation removes the objects having sizes that are smaller than the structuring element. On the other hand, the closing operation is the opposite of the opening operation. It is a dilation operation followed by erosion. The closing operation fills the small holes and gaps in a single pixel object. It smoothes contours and maintains shapes and sizes of objects. Thus with a predefined structuring element, one can extract different image contents by taking the difference between the original image and the one processed by the opening operator. This process is called tophat operation. The morphological tophat operation is performed and followed by a subtraction. The texture without diagnostic relevant contents is extracted by a tophat operation, B [ f( xy, )] max[0, f( xy, ) ( f B)( xy, )] (6.5) 1 1 where B1denotes the tophat operation between the original image f(x,y) and a specified structuring element B 1. Its size should be chosen smaller than size of

137 the mass. If B2 denotes the mass pattern enhanced image by background correction by the second tophat operation, B [ f( xy, )] max[0, f( xy, ) ( f B)( xy, )] (6.6) 2 2 where B 2 represents the predefined structuring element whose size is greater than mass. In this work, the size of the cropped image is 256 X 256, and since all the mass findings in the data base has radius less than 175, the values of structuring elements B 1 and B 2 are chosen as 3 X 3 and 200 X 200 respectively. For each image the dilation, erosion, opening and closing, and tophat operations are carried out respectively for the structuring elements B 1 and B 2. At last the resultant morphological filtered image is obtained using the equation (6.7). Rxy (, ) max(0, ) (6.7) B2 B1 An example of the dilation, erosion, opening and closing operations for the hospital image with mass findings, with structuring element greater than the mass are depicted in Figure 6.2 and its morphological output is given in Figure 6.3. Finally the segmented image is obtained by integrating the edge detected image with the morphological filtered image. The image results obtained through various stages of segmentation for the hospital image with mass is illustrated in Figure 6.3.

138 Figure 6.2 Image Results Obtained in Various Morphological Operations with Structuring Element Greater than Mass

139 Figure 6.3 Image Results Obtained in Various Stages of Mass Segmentation

140 6.2.3 Feature Extraction through GLCM Texture based features are extracted from the mass after segmentation. Texture features are used to distinguish benign mass from malignant tumors. The idea of forming GLCM for segmented image is that it will capture the mass efficiently, as most masses have textured patterns. For example, the spiculated mass has armed structures at the boundaries, and upon wavelet based edge detection these armed patterns are well preserved. The procedure for forming the co-occurrence matrix is explained in the following subsection. 6.2.3.1 Gray level co-occurrence matrix (GLCM) One way of describing texture, in texture analysis process is to consider the relative positions of pixels in the image. GLCM gives relative positions of matrix elements in the matrix. GLCM is defined as a matrix where each element (i,j) in GLCM specifies the numbers of times that the pixels with value i occurred adjacent to a pixel with value j. The adjacent cooccurrence may take any one of the following positions: horizontal =0 or vertical = 90 or right diagonal = 45 or left diagonal = 135. This subsection explains formulation of GLCM for any given matrix. Consider an image I, with L possible intensity levels. Let Q be an operator that defines the position of two pixels relative to each other. G be a matrix whose element g i,j is the number of times that pixel pairs with intensities p i and p j occur in I in the position specified by Q, where 1 i,j L. The size of the GLCM matrix G is determined by the number of possible intensity levels in the image. For an image with 8 bit resolution,

141 each pixel intensity is represented using 8 bits and possibly there will be 256 gray levels. Hence the size of the GLCM will be 256 256. Thus for different orientations 4 matrices of size 256 256 will be formed which will increase the computation complexity. Hence in order to reduce the computation complexity, quantization is performed on the intensities which reduce the dimension of GLCM. For example: GLCM with dimension 256 256 can be reduced to 8 8, by quantizing the values into 8 bands, i.e. by letting the first 32 intensity levels equal to, the next 32 values from 33 to 64 equal to 2 and so on. The example of GLCM for horizontal position = 0 is depicted in Figure 6.4. The given matrix is of size 5 5, in which the matrix elements range from 0 to 7. Hence its GLCM will be of size 8x8. In the GLCM matrix the element at coordinate (1,2) is represented as 2 since the pixel 1 adjacent to 2 occurred in two different locations in the given matrix in horizontal position. 0 1 2 3 7 1 3 6 4 5 1 7 3 1 2 0 5 4 3 6 7 3 4 5 1 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 0 0 0 1 0 0 0 0 2 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 2 1 0 0 0 1 0 2 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 Figure 6.4 Example of GLCM for Horizontal Position = 0 In this thesis, five co-occurrence matrices are constructed from the segmented image in four different spatial orientations: horizontal, right diagonal, vertical and left diagonal (0 0, 45 0, 90 0 and 135 0 ). A fifth matrix is constructed as the average of preceding four matrices. The use of GLCM will extract mass textured features with high correlation. Here 19 statistical

142 features are extracted from each matrix and in total 95 features are extracted per image from all the five matrices. Here mass features are extracted for 50 images that are used for training and analysis. Due to space inconsistency, examples of these features extracted for MIAS images for three different cases is given in Appendix 4. These texture features are used to train and test the neural network. 6.2.4 Classification The BPN is used for mass classification. The details regarding the BPN architecture and its algorithm can be found in the previous chapter. Here the network is constructed with 95 input neurons, 50 neurons in hidden layer- 1, 40 neurons in hidden layer-2 and one neuron in the output layer. The sigmoidal activation function and gradient descent training are adopted. The network classifies the given input as either normal or benign or malignant. 6.3 RESULTS AND DISCUSSION The tests are conducted with 50 images, 46 mammographic images collected from MIAS database and 4 images collected from hospital. The experiment is conducted in two phases: training and testing with original images and testing with the compressed reconstructed images. The details regarding the number of images taken for classification are given in Table 6.1. Among the 4 images collected from hospital, 2 images are with benign mass and another 2 with malignant mass. One in each case is used for training and another for testing. The details regarding the neural network parameters are given in Table 6.2. The network use supervised learning with Gradient Descent Back Propagation algorithm. The neurons in different layers use different activation functions. The first hidden layer neurons use tansigmoidal (Hyperbolic tangent) function, the second hidden layer neurons use

143 logsigmoidal (Logistic) function. The output layer neuron uses pure linear function. Table 6.1 Number of Images Taken for Classification and Result No. of Reconstructed No. of Original Images Image Category Images Tr Tt Er Tt Er Normal 14 5 0 5 0 Benign 14 5 1 5 2 Malignant 7 5 0 5 0 Tr Training, Tt Testing, Er Error (No. of images wrongly classified) Table 6.2 Neural Network Parameters Parameters Values No. of Input Neurons 95 No. of Hidden Neurons - Layer 1 50 No. of Hidden Neurons - Layer 2 40 No. of Output Neuron 1 Epochs 12,51,749 Training Time in Seconds 1,20,575 MSE 1e-3 The various results of neural network training are given in figures. Figure 6.5 represents the neural network training obtained for mass data. Figure 6.6 gives the performance plot, Figure 6.7 depicts the training state, Figure 6.8 shows the regression plot obtained for mass data training and its classification result is arrayed in Figure 6.9.

144 Figure 6.5 Results of Neural Network Training Obtained for Mass Data Figure 6.6 Performance Plot Obtained for Mass Data Training

145 Figure 6.7 Training State Obtained for Mass Data Training Figure 6.8 Regression Plot Obtained for Mass Data Training

146 Figure 6.9 Mass Classification Results Table 6.3 Mass Classification Results Classification Parameters Original Images Recontructed Images True positive (TP) 5 5 False Positive (FP) 0 0 True Negative (TN) 5 5 False Negative (FN) 1 2 Sensitivity (TPF) 83.33% 71.4% False Positive Fraction (FPF) 0 0 Specificity (1-FPF) 100% 100% The mass classification results for original and compressed reconstructed images are given in Table 6.3. Fifteen images (5 in each case)

147 in original and compressed reconstructed form are separately taken for testing. In classification, the sensitivity for compressed reconstructed image classification deviates by 7.28% as one image in benign case is wrongly classified as malignant compared to the original image classification result. And there is no deviation in specificity between classification of original and reconstructed images and it is 100% in both the cases. It is also observed that classification results obtained for compressed reconstructed images are closer to the results obtained for the original images. 6.4 SUMMARY In this chapter, a new mass detection scheme is implemented. A novel mass segmentation scheme is constructed using mutiscale wavelet based edge detection and morphological operations. The concept of feature extraction from GLCM s formulated for the mass segmented image is introduced to improve the classification rate. For a testing set of 15 images in original form, the proposed method obtains sensitivity of 83.33% and specificity of 100% and for compressed reconstructed images it achieves the sensitivity of 71.4% and specificity of 100%. Thus the extensive comparable classification results obtained for original images and compressed reconstructed images demonstrate the validation of the proposed compression and classification schemes.