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Detection and Classification of Breast Cancer Nandi Nwe Win, Nang Aye Aye Htwe Abstract — Breast cancer is the second most lethal cancer for women in the world today. X-ray mammography is the most widely used method for early detection of breast cancer. To detect breast cancer region, Canny edge detection is used. To separate this region from all other background, thresholding method is used. This paper presents an implementation of detection and classification system for cancerous tissues. Malignant and benign abnormalities are selected from the segmented images. And then texture based features are extracted using Gray Level Difference Method (GLDM). For the purpose of pattern classification between malignant and benign samples, the optimum subset of texture features are modeled by using Artificial Neural Network (ANN).Detection and Classification of cancerous tissues is implemented with MATLAB programming language. Index Terms — Artificial Neural Network, Canny Operation Digital mammograms, Feature Extraction, Gray Level Difference (GLDM), Thresholding I. INTRODUCTION Breast cancer is one of the major causes for the increase in mortality among middle-aged women, especially in developed countries [1]. Breast cancer continues to be a public health problem in the world specifically in western and developed countries.The commonly used diagnostic technique including biopsy mammography, thermography and ultrasound image. Among these techniques mammography is the best approach for early detection. In early stage visual clues are subtle and varied in appearance, because diagnosis is difficult. The abnormalities are hiding by breast tissue structure. Breast cancer detection and classification of mammogram images is the standard clinical practice for the diagnosis of breast cancer. Mammography associated with clinical breast examination is the most efficient method for early detection of breast cancer. However, it is very difficult to interpret X-ray mammograms because of the small differences in the image densities of various breast tissues, which is particularly true for dense breasts. The interpretation of mammograms by radiologists is performed by a visual examination of films for the presence of abnormalities that indicate cancerous changes. Microcalcifications, one of the early indicators of breast cancer, are tiny granule-like deposits of calcium. The presence of clustered microcalcifications in X-ray Manuscript received Oct 15, 2011. Nandi Nwe Win, Department of Information Technology, Mandalay Technological University, (e-mail: [email protected]). Mandalay, Myanmar, 09-256269894 Nang Aye Aye Htwe, Department of Information Technology, Mandalay Technological University, Mandalay, Myanmar, 09-5661208 (e-mail: [email protected]). mammograms is considered an important indicator for the detection of breast cancer, especially for individual microcalcifications with diameters up to about 0.7 mm and with an average diameter of 0.3 mm [2]. Mammography is the only effective screening method for detection of breast cancer in early stage. The early detection of cancer can play a significant role in its treatment, making possible improvement in the quality of patient’s life and increase in survival rates. If diagnosed early, breast cancer is one of the most treatable forms of cancer. Recently, Digital Mammograms applied for the diagnostics of breast cancer. I. RELATED WORKS In the literature, various numbers of techniques are described to detect and classify the presence of breast cancer in digital mammograms. A lot of research has been done on the textural analysis on mammographic images. IndraKantaMaitra et al, [3] used GLDM features to identification of abnormal masses and their study included mammograms from the MIAS database. Wu et al. [4] applied neural networks directly to the images or the preprocessed images to recognize patterns that might include microcalcifications in digitized mammograms. They showed that using neural networks, clusters of microcalcifications were more accurately distinguished in the frequency domain than they were in the spatial domain. Gray Level Difference Matrix (GLDM) method, introduced by Weszka et al [5], has been widely used for analyzing medical images. An algorithm comprising of numerous phases to attain automatic detection of clusters was developed by Cairns et al. [6]. Papadopoulossa et al. [7] presented a hybrid intelligent system for the identification of microcalcification clusters in digital mammograms, which can be summarised in three-steps: edge detection, segmentation, feature extraction and classification. This paper investigates the accuracy of a detection methodology that uses Haralick Texture Features as an input to ANN (Artificial Neural Networks) to classify the images into benign or malignant[8]. Weidong Xu et al. proposed a new algorithm based on ANN for detecting masses automatically [9]. III. BACKGROUND THEORY In this paper, there are four main parts: image acquisition, edge detection, image segmentation, feature extraction and classification. A. Image Acquisition Digital mammograms are used as the standard inputs into the proposed framework. Mammography dataset obtained from the Mammographic Image Analysis Society (MIAS) database. MIAS mammography images are digitized at 200 micron pixel edge, with a size of 1024 ×1024 pixels. Each pixel in the grayscale mammogram image represents the pixel 1 All Rights Reserved © 2012 IJSETR International Journal of Science, Engineering and Technology Research (IJSETR) Volume 1, Issue 1, July 2012 intensity in the range of [0, 255] (8-bit). Breast images in MIAS database as shown in Figure 1. extraction method for our implementation. An example of gray level difference method is as shown in Figure 4(a) and (b). Figure 1. Breast images in MIAS database B. Canny Edge Detection The Canny edge detection is known as the optimal edge detector. Canny edge detection aims at enhancing the many edge detectors already published at that time. It is important that edges occurring in images should not be missed and that there be no responses to non-edges. Canny method is a better method to find edges by isolating noise from the image without disturbing the feature of edges in the image. The experimental result of tested breast image by using Canny method as shown in Figure 2. Figure 4. (a) Original image (b) GLDM for Original image (distance=1, direction=0). The Grey-Level Difference Method is constructed based on the statistics of the second order joint conditional probability density function p (i | d). Where i is the grey level (i.e. intensity) difference between two pixels. And then the feature vectors can be derived the following the feature as shown in Table 1. TABLE I DESCRIPTION OF TEXTURE FEATURES Feature Figure 2. Edge Detection Using Canny Method C. Image Segmentation The goal of Image Segmentation is to find regions that represent objects or meaningful parts of objects. Segmentation divides an image into its constituent regions or objects. Thresholding has been used for segmentation as it is most suitable for the present application in order to obtain an image with ‘1’ representing the breast tumor and ‘0’ representing the background. The segmented breast as shown in Figure 3. 1 Contrast 2 Mean 3 Formula Entropy 4 Inverse Difference Moment 5 Angular Second Moment 6 Area Figure 3. Image Segmentation Using Thresholding D. Texture Features Extraction Using Gray Level Difference Method (GLDM) Texture Feature extraction is a very important process in the area of classification. Texture features have been widely used in mammogram classification. The texture features are ability to distinguish between abnormal and normal cases. Gray Level Difference Method (GLDM) is a good feature A complete set of 360 features are used for the classification of breast image. Resulting feature vectors are shown in Figure 5. Finally, these sets of features are used to classify the breast images. 2 All Rights Reserved © 2012 IJSETR input layer. The 20 hidden layer and the output layer produce either 1 (Benign) or 0 (Malignant). IV. SYSTEM DESIGN AND IMPLEMENTATION RESULT A. Design of the Proposed System In this system, Canny Method, Thresholding Technique, Gray Level Difference Method and Artificial Neural Network are applied to implement Breast Cancer Detection and Classification System. In image acquisition step, we have used the images from MIAS database. The total 80 mammograms have been used for training and testing. These images are already processed. After applying GLDM feature extractor following value are Contrast, Angular Second moment, Entropy, Mean, Inverse Difference Moment and Area. ANN Classifier is applied to these features which classify the input image as malignant or benign. Overall block diagram of the system is shown in Figure 7. Input: Image Acquisition Edge Detection Image Segmentation Texture Feature Extraction Classification Artificial Neural Network Digital Mammogram Result : Malignant or Benign Figure 5. Extracted Features Figure 7. Overall Block Diagram of the System E. Classification Neural network is the best tool in pattern classification application and composed of three layers as shown in Figure 6. Input Layer Hidden Layer B. Implementation Results of the Proposed System Detection and Classification of Cancerous Tissues System is proposed in this system. The user can see implementation window as shown in Figure 8 and Main Menu of the Proposed System as shown in Figure 9. Output Layer Contrast Mean Entropy . . output . Angular second moment Inverse difference moment Classification Benign Malignant Area Figure 6. Architecture of Artificial Neural Networks The classification process is divided into the training phase and the testing phase. The classifier is trained and tested on mammogram image. The classification accuracy depends on training. In the training phase known data are given. In the testing phase, unknown data are given and the classification is performed using the classifier after training. The accuracy of the classification depends on the efficiency of the training. Neural network are trained by experience, when fed an unknown input into neural network, it can generalize from past experience and produce a result. 6 features fed to neural Figure 8. Cover Window of the Proposed System 3 All Rights Reserved © 2012 IJSETR International Journal of Science, Engineering and Technology Research (IJSETR) Volume 1, Issue 1, July 2012 After detecting the breast image, it is needed to segment the image by using Thresholding Technique as shown in Figure 12. Figure 9. Main Menu of the Proposed System The first stage of the system is to select breast images. This can click load menu item from main menu of the proposed system. And then required breast photo is choose step by step as shown in Figure 10. Figure 12. Image Segmentation Using Thresholding Then feature points are extracted from the segmented image using gray level difference method (GLDM), as shown in Figure 13. Figure 10. Load of Breast Image In the next step, the breast image is Edge Detected using Canny Method, as shown in Figure 11. Figure 13. Extracted Features Points After extracting the features, the user runs the final result Figure 14 are results of breast classification with Malignant and Benign. Figure 14. Classification result of the program Figure 11. Edge Detection Using Canny Method To evaluate performance in this system, there are known image from a train data set and an unknown image from a test data set. The system’s accuracy of breast classification is described in Table 2. 4 All Rights Reserved © 2012 IJSETR TABLE II THE ACCURACY RATE OF BREAST CLASSIFICATION Images set Cancer Non-ca ncer Tot No Correct Prediction Accuracy rate Training set 30 30 60 60 100% Testing set 50 50 100 100 100% [9] gray-level cooccurrence matrix approach,” J. Geophys. Res., vol.93, pp. 12, 663–12681, Oct. 1988. Xu W, Li L, Xu P. A new ANN-based detection algorithm of the masses in digital mammograms. IEEE International Conference on Integration Technology. 26-30: 2007. V. CONCLUSIONS Breast cancer classification is a vital stage for the performance of the canny method of breast cancer detection. GLDM feature vector is calculated for each image cell and is used for better computation performance. It reduces the false positive rate by reducing the unnecessary biopsy and health care cost as well. ANN shows very good performance in medical diagnostic systems. Computational time is around 36 seconds for each breast classification. It was evaluated on 60 images containing malignant and benign masses with different size and shape. Using the ANN classifier, breast cancer diagnosis with a training accuracy of 100% and testing accuracy of 98% is achieved. ACKNOWLEDGMENT First of all, the author is grateful to her parents who specially offered strong moral and physical support, care and kindness. The author is highly grateful to Dr. Myint Thein, the Pro.Rector of the Mandalay Technological University for his permission for completion of this paper. The author is deeply thankful to Dr. Aung Myint Aye, Dr. Nang Aye Aye Htwe, Mandalay Technological University, for their overall supporting during the writing of this paper. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] R. G. Bird, R. G. Wallace, and B. C. Yankaskas, “Analysis ofcancers missed at screening mammography,” Radiology, vol. 184, pp. 613–617,1992. D. B. Kopans, Breast Imaging. Philadelphia, PA: J.B. Lippincoff, pp. 81–95,1989. IndraKantaMaitra, Sanjay Nag and Samir Kumar Bandyopadhyay,”Identification of abnormal masses in digital mammography images”, International Journal of Computer Graphics, Vol.2, No.1, 2011. Y. Wu, K. Doi, M. L. Giger, and R. M. Nishikawa, “Computerized detection of clustered microcalcifications in digital mammograms: Application of artificial neural networks,” Med. 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