CLASSIFICATION OF ABNORMALITY IN B -MASS BY ARCHITECTURAL DISTORTION

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1 CLASSIFICATION OF ABNORMALITY IN B -MASS BY ARCHITECTURAL DISTORTION #1 Venmathi.A.R., * 2 D.C.Jullie Josphine #1.Dept of ECE, Kings Engineering College * 2. Dept of CSE,Kings Engineering college Abstract-The architectural distortion in breast masses (B-mass) helps in defining the classification of breast cluster abnormalities in mammograms into benign and malignant categories. This system is designed to help radiologists to reduce the risk of categorizingthe benign and malignant breast cancer biopsies. This system also reduces the false positive rates while the radiologist classifies the breast masses. Once a mass abnormality is detected and marked on a mammogram by a radiologist, two textural features, named denseness and architectural distortion, are extracted from the marked area. The denseness feature provides a measure of thickness of the marked area, whereas the architectural distortion feature provides a measure of its irregularity and shape. The measurements called skew and kurtosis defines the size and shape of the architectural distortions. From the parametric variations the classification of benign and malignant is possible but these features are then fed into a neural network classifier to confirm the previous results. The system uses the MIAS database proven masses. Keywords: Classification, Mass abnormality, Mammography, Texture features, Denseness,Architectural distortion. I Introduction The fundamental knowledge of the breast structures and some breast architectures are essential to understand the importance of breast cancer study. Biological researches say that Breast cancer is caused by a abnormal cellular division. Breast cancer is the most common malignancy of women and is the leading cause of cancer deaths among them. Early detection is an effective way to diagnose and also to provide a way to manage breast cancer for a full recovery. Therefore, early detection of breast cancer can play an important role in reducing the risk of death. Computer-aided diagnosis (CAD) has been a helpful tool in the early detection of breast cancer byscreening mammogram, using markings on suspicious regions thus allowingreduced death rate among women with this disease. These systems use computer technologies to detect abnormalities in mammograms and the use of these results by radiologists for diagnosis play an important role, once characterize lesions through automatic image analysis. The CAD performance can vary because some lesions are more difficult to detect than others, this is because they have similar characteristics to normal mammary tissue. However, it is important to continue working in order to decrease the number of failures. In this area, mammography has an important role to detect lesions in initial stages. Mammography has proven to be the most effective tool for detecting breast cancer in its earliest and most treatable stage, so it continues to be the primary imaging modality for breast cancer screening and diagnosis. Furthermore, this exam allows the detection of other pathologies and may suggest the nature such as normal, benign or malignant. The introduction of digital mammography in breast imaging Classification of breast density is important for using measurements of 179

2 mammographic density patterns in computer aided-detection. II Literature Study The focus of this work is classification of masses. There have been studies on the classification of masses using various image features such as fractal dimension, and texture features. Mammograms can be useful for diagnosing breast cancer. For example, mammographic density is a well-established biomarker for breast cancer [3]. In addition to the problem in the above application,a recent study has found that the image feature (i.e. the mean pixel intensity value) of pectoral muscle itself in a mammogram may be an independent risk factor for breast cancer [6]. This again, calls for an appropriate method to correctly identify the boundary of pectoral muscle in a MLO view mammogram. A traditional and straightforward method to identify the pectoral muscle is visual assessment. This requires a human reader to look at the mammogram and manually identify the pectoral muscle region. However, identifying pectoral muscle manually can be labor intensive and time consuming. This is especially a problem in a large scale screening study. There has been a trend advocating fully automated method for mammogram analysis (O. Menut, R. Rangayyan, and J. Desautels), and this requires pectoral muscle identification also to be fully automated. Several authors have proposed automatic pectoral muscle identification method. Therefore, in this paper, we have developed two new textural features called skew and kurtosis of the irregular masses to capture the denseness andirregularity of a marked mass abnormality area. These texture features are then used as the input to a neuralnetwork classifier. III MIAS Data base A mammogram is a low dose x-ray of the breast. The mammogram images used in this experiment are taken from the MIAS (Mammogram Image Analysis Society). The database contains 322 mammogram images in MLO (mideolateral oblique view). The original MIAS Database images are clipped so that every image is 1024 pixels 1024 pixels. In this work, 180 the images were cropped to size 256x256 pixels. The MIAS database was provided with the diagnosis from experts. A set of 52 images is used wherein 26 images are normal, 26 images are Microcalcified. IV Materials and Methods Breast Distortions Fibro Adenoma Fibro Adenomas are the most common breast tumors in pubertal females. There are two types of Fibro Adenoma, classified as: common and giant,characterized both by glandular and stoma elements. There is no pain or tenderness for this kind of tumors due to fibro adenomas and their size do not change with the menstrual cycle. Women aged 20s are the most common people affected with this disease. A rapid growth sometimes occurs, but usually that growth is extremely slow. A giant fibro adenoma should measure over 2.5 cm in diameter. Mammary Dysplasia Mammary dysplasia also can be called as fibrocystic changes (FCC). In reality, these alterations not indicate a disease. These alterations are defined as benign masses. The age group of this lesion is between 20 to50 years. Normally, fibrocystic changes are associated to the cyclic levels of ovarian hormones.during ovulation and before menstruation; the hormone level changes often lead the breast cells to develop into nodules or cysts, which feel like a lump when touched. The signs of fibrocystic changes include increased density of the breasts, and fluctuation in the size of cystic areas, increased tenderness, and occasionally spontaneous nipple discharge Mastitis and breast abscess They are rare pathologies. Often these infections can happen in postpartum situations. It is composed of neutrophilic granulocytes, seen mostly in lactating women. Chronic mastitis may be due to infectionof storingmother s milk in the ducts. The spoiled milk causes infection and sometimes due to negligence it may formed in to dense mass that can lead to further infections and seriousness. Breast abscess arises when mastitis was treated inadequately and exist milk retention. Breast Density Study

3 The above mentioned Breast densities are strong indicators for breast cancer, which shows the possibility for the detection of abnormalities in mammograms. There is a great difficulty in detecting breast tumors in the initial stage, because it tends to be an abnormal tissue. The distortion studies on the breast abnormalities give a solution to define the seriousness on the masses. The CAD systems outputs can be true positive (TP), false positive (FP), true negative (TN) and false negative (FN) in the context of detecting the presence or absence of abnormality. The result false positive may put the patient in trouble. However, when the results are a false negative, is a more worrying situation once the person has the lesion but the algorithm does not detect. The performance of a CAD system can be limited to the detection of obvious cancers with a moderate sensibility and a relative good specificity. These metrics are based on true/false, positives/negatives metrics. The sensitivity refers to how often the algorithm used reports that an abnormality exists in the instances where it actually exists. Where TP - True Positive TN True Negative FP False Positive FN False Negative Figure 1. malignant masses and Using the above states normal images and abnormal images correctly or wrongly classified 181 can be known accurately, using this four states Sensitivity, False Positive rate, accuracy, specificity and Precision are defined and calculated. 1.Sensitivity = TP / TP + FN this ratio defines correctness of True positive normal images 2.False Positive Rate = FP / FP + TN this ratio defines non accuracy of normal images Accuracy = TP + TN / TP + TN + FP + FN this ration expresses accuracy of the images classified. 3.Specificity = TN / TN+ FP Ratio gives how specific in classification for the region of interest 4.Precision = TP / TP + FP Precise region identified without error. Defining Edges Edge in an image represents a boundary across which the intensity level of pixel changes precipitously. Most of the shape information of an image is enclosed in edges [8]. Hence, first we detect these edges in an image and using image filters and then by enhancing those areas of image which contains edges as shown in Fig. 4.The statistical parameters namely mean, median, perimeter, standard deviation, and skewness are obtained from mammogram images, and their mathematical expressions are tabulated in Table 1. Table 1 Statistical data for normal, benign, and malignant images Features Normal Benign Malignant Mean Median Perimeter Standard Deviation Skewness Algorithm for detection Suspected regions after classification is detected based on the following seven features Number of pixel in the selected region to form an Area Selection of gray areas around the identified object

4 Finding the perimeter of the area with Gradient strength Difference between the maximum gray level in the region Finding the mean of the border region Finding Maximum gray level inside the region Finding maximum edge gradient K-Nearest Neighbor Classifier The statistical parameters for all the normal and abnormal images are calculated and from these parameters the conclusion is arrived that out of 26 abnormal images 15 are having benign microcalcifications and 11 are having malignant microcalcifications. These 26 images are divided into training and testing set for the classifier. The classifier results are compared with the previous results. Results and discussion The TP and TN rates of the classification of dense mammograms are presented in this session.the presented results correspond to the best results achieved with the KNNClassifierthat shows the concentrated region is dense or granular or not. 1. Fatty tissue 2. Granular tissue 3. Dense Tissue S.No MDB database Class 1 MDB MDB MDB MDB004 2 MDB005 3 V.CONCLUSION This paper has addressed the classification of mass abnormalities in mammograms into benign andmalignant categories with the help of parametric variations. Considering that the distributions of the commonly used texture features were used to capture denseness and architectural distortion differences between benign and malignant masses.these features correlate well with similar clinical differences. The denseness feature was designed to capture radiographic denseness of breast cancer, while the architectural distortion features to capture its irregularity. Extracted denseness and architectural distortion features from a marked abnormality area, are launchedin the classifier to differentiate their category.. This system can be used as an electronic second opinion to lower the number of benign biopsies, in particular for cases when its outcome agrees with the radiologist's assessment. VI REFERENCE [1] C. D Orsi and D. Kopans, Mammography interpretation: The BI-RADS method, American FamilyPhysician, Vol. 55, No. 5, April [2] J. Brett, J. Austoker, and G. Ong, "Do women who undergo further investigation for breast screeningsuffer adverse psychological consequences? A multi-center follow-up study comparing differentbreast screening result groups five months after their last breast screening appointment," AmericanJournal of Public Health, vol. 20, no 4, pp , [3] T. C. Wang, and N. B. Karayiannis, "Detection of microcalcifications in digital mammograms usingwavelets," IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp , [4]S. Baeg et al. / Electronic Letters on Computer Vision and Image Analysis 1(1):1-20, [5] W. Polakowski, D. Cournoyer, S. Rogers, M. DeSimio, and D. Ruck, Computer-aided breast cancerdetection and diagnosis of masses using difference of Gaussians and derivative-based

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