MIT International Journal of Electronics and Communication Engineering Vol. 3, No. 1, Jan. 2013, pp

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1 MIT International Journal of Electronics and Communication Engineering Vol. 3, No. 1, Jan. 013, pp A Novel Technique to Detect Abnormal Masses from Digital Mammogram Saurabh Verma saurav.v84@gmail.com Mansi Vashisht mansi.jain7@gmail.com Kumar Manu kmanu.engg.ec@gmail.com ABSTRACT Mammography is at present the best available technique for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. In some cases, subtle signs that can also lead to a breast cancer diagnosis, such as architectural distortion and bilateral asymmetry, are present. Breast abnormalities are defined with wide range of features and may be easily missed or misinterpreted by radiologists while reading large amount of mammographic images provided in screening programs. Extracting the region within the breast is done by demarcation of the breast contour and pectoral muscle. This limits the search for abnormal regions only within the breast region by eliminating the background of the mammogram. In this paper we submit a fully automated process for detection of abnormal masses by using image orientation, Noise suppression, Gaussian smoothening, anatomical segmentation of Breast Region of Interest (ROI), feature extraction step, Support Vector Machine (SVM) and standard deviation of region. We use our proposed Anatomical Segmentation of Breast ROI (ASB) algorithm to differentiate various regions within the breast. After segregating the different breast regions we use our proposed Support Vector Machine to isolate normal and abnormal regions in the breast tissue. Keywords: Gaussian smoothening, Anatomical segmentation of Breast ROI (ASB) and Seeded region growing algorithm (SRGA), Feature extraction step and Support Vector Machine (SVM). I. INTRODUCTION Detection and diagnosis of breast cancer in its early stage increases the chances for successful treatment and complete recovery of the patient. Screening mammography is currently the best available radiological technique for early detection of breast cancer [1]. It is an x-ray examination of the breasts in a woman who is asymptomatic. The diagnostic mammography examination is performed for symptomatic women who have an abnormality found during screening mammography. Nowadays, in most hospitals the screen film mammography is being replaced with digital mammography. With digital mammography the breast image is captured using a special electronic x-ray detector which converts the image into a digital mammogram for viewing on a computer monitor or storing. Each breast is imaged separately in craniocaudal (CC) view and mediolateral-oblique (MLO) view shown in Figure 1(a) and Figure 1(b), respectively. The American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) suggests a standardized method for breast imaging reporting []. Terms have been developed to describe breast density, lesion features and lesion classification. Screening mammography enables detection of early signs of breast Figure 1: Two Basic Views of Mammographic Image: (a) craniocaudal (CC) view, (b) mediolateral-oblique (MLO) view cancer such as masses, calcifications, architectural distortion and bilateral asymmetry. A mass is defined as a space occupying lesion seen in at least two different projections []. If a potential mass is seen in only a single projection it should be called Asymmetry or Asymmetric Density until its three-dimensionality is confirmed. Masses have different density (fat containing masses, low density, isodense, high density), different

2 MIT International Journal of Electronics and Communication Engineering Vol. 3, No. 1, Jan. 013, pp margins (circumscribed, micro lobular, obscured, indistinct, spiculated) and different shape (round, oval, lobular, irregular). Round and oval shaped masses with smooth and circumscribed margins usually indicate benign changes. On the other hand, a malignant mass usually has a spiculated, rough and blurry boundary. However, there exist atypical cases of macrolobulated or spiculated benign masses, as well as microlobulated or well-circumscribed malignant masses [3]. A round mass with circumscribed margins is shown in Figure (a). Calcifications are deposits of calcium in breast tissue. Calcifications detected on a mammogram are an important indicator for malignant breast disease but are also present in many benign changes. Benign calcifications are usually larger and coarser with round and smooth contours []. Malignant calcifications tend to be numerous, clustered, small, varying in size and shape, angular, irregularly shaped and branching in orientation [1]. Calcifications are generally very small and they may be missed in the dense breast tissue. Another issue is that they sometimes have low contrast to the background and can be misinterpreted as noise in the inhomogeneous background [4]. Fine pleomorphic clustered calcifications with high probability of malignancy are shown in Figure (b). Figure : Examples of Abnormalities: (a) round mass with circumscribed margins, (b) fine pleomorphic clustered calcifications Detection of abnormal masses within the breast as well as breast image segmentation is a very important feature in image analysis. Detection of abnormal masses within the breast is an important factor which can improve clinical diagnosis of mammographic diseases. It is essential to extract the abnormal masses in mammogram so that we can perform computerized analysis of digital mammograms. This will limit the search for abnormalities to the anatomical region of the breast, leaving out the background and other regions of the same. It also facilitates the use of comparative analysis for comparison of corresponding digital mammograms. Hence in this research work, we have proposed novel step-by-step algorithms for mammogram segmentation to detect the abnormal masses. Before stepping into the main part of our algorithm, a district set of pre-processing techniques are used for better results. The proposed methods have presented significant results and shown that the proposed methods are more accurate and reliable compared with other common methods. II. LITERATURE REVIEW The mammograms and their analysis by the way of image processing, encompass many disciplines such as statistics, mathematics, computer and medical science. Several studies are going on to improve the quality of CAD systems. One of the main method emphases on accuracy by improving the segmentation process and identifying significant image features. Previous studies have suggested that improving the accuracy of mass and non-mass region segmentation could also significantly improve the performance of CAD in abnormal masses detection and characterization. Several automated and semi-automated methods to solve this problem have been developed. These include using a density-weighted contrast enhancement algorithm that combines adaptive filtering and edge detection [5], an adaptive multilayer topographic regional growth algorithm [6], a gray-level based iterative and linear segmentation algorithm [7], a dynamic programming approach [8], dynamic contour modelling [9] etc. to segment mass lesions from surrounding breast tissue. One important feature in automated mass detection and classification is mass boundary spiculation level [10]. Kegelmeyer et al [11] detected spiculated masses using local edge orientation and Laws texture features but it is not applicable for the detection of nonspiculated masses. Comer et al.[1] and Li et al. [13] used Markov random fields to classify the different regions in a mammogram based on texture. Huo et al [14] developed a new spiculation-sensitive pattern recognition technique to quantify the degree of speculation of a lesion and classified masses as malignant or benign. Nakayama et al. [15] used a filter bank for the detection of nodular and linear patterns. A lesion segmentation algorithm was developed by Sameti et al. [16] used fuzzy sets to partition the mammographic image data. The results indicated that combining texture features with morphologic features extracted from automatically segmented mass regions was an effective approach for the automated characterization of mammographic masses. Recent studies of interactive CAD methods have also suggested that the accurate detection and classification of mass boundary spiculation levels plays an important role in improving the visual similarity of similar reference mass regions selected by interactive CAD methods [17]. Ayman Abu Baker in [18] The main purpose for this technique is to study the properties of true positive (TP) and false positive (FP) detected regions in the mammogram images by analyzing their wavelet features and support vector machine (SVM). The combine between wavelet feature and support vector machine (SVM) will be used to reduce number of the detected FP regions. III. PROPOSED METHODS Digital Mammograms are medical images requires a preparation phase to improve the quality of the image. Our objective during this process is in preparing the image and makes it ready for

3 MIT International Journal of Electronics and Communication Engineering Vol. 3, No. 1, Jan. 013, pp further processing by removing the irrelevant and unwanted parts in the background of the mammogram. 3.1 Mammogram Pre-processing Image Orientation: The mammogram image needs to be transformed. The position of the chest wall encompassing the pectoral muscle needs to be positioned on the upper left corner within the image. The chest wall location can be determined by delineation of breast tissue close to the skin-air boundary where the pixel intensity decreases gradually. We extract the vertical centroid of the image and assume the asymmetric region lying nearest to the right side of the vertical centroid represents the breast tip. We turn over the image in the horizontal direction if required to position the asymmetric region beneath the vertical centroid, to obtain an image that provides an image where both the left and the right breast images are position universally. Noise Suppression: Different types of noises are present that appears in mini MIAS images. High intensity noise is characterized by higher quantum of optical concentrations, such as labels or scanning artifacts. The markings that have persisted or left behind by tapes, shadows, horizontal running shreds represent other types of noises within the image. Such noise must be replaced by black pixels. Gaussian Smoothening: The Gaussian smoothing operator is a -D convolution operator that is used to `blur images and eliminate detail and noise. It uses a convolution kernel that is the shape of a Gaussian hump or bell-shaped. In -D, an isotropic (i.e. circularly symmetric) Gaussian has the form: x + y x + y - s 1-1 G( x, y) = e s e ps ps The objective of using Gaussian smoothing is to use this -D distribution as a `point-spread function using a convolution Figure 3: Mammogram Before and After Gaussian Smoothening (MIAS 184.L) kernel. Images contain a two dimensional array of discrete pixels we need to produce a discrete estimate of the Gaussian function before we can implement the convolution filter. 3. Anatomical Segmentation of Breast ROI This paper is based on the image segmentation method, the inputs are images and final yields are the features extracted from the input images. Segmentation divides image into its integral regions or objects. Segmentation is a vital tool and has a significant role in image analysis. Our ultimate aim to perform segmentation is to obtain the regions of interest (ROI) depending on the image and its characters. The approach is to partition an image based on abrupt variations in intensity levels at different regions, such as edges in an image and partitioning image into regions that show similar intensities and also based on some predefined criteria. After obtaining the breast ROI, we need to differentiate and partition the anatomical regions within the breast ROI. During this process, we identify each region or segment as a closed object and determine the arithmetic Mode value for the pixel intensity, in that region. This paper proposes a new algorithm to identify and isolate different regions within the breast ROI and detect abnormalities, if present, within the breast region. After the pixel is located we draw another vertical line from top to bottom passing through the rightmost pixel thus partitioning the image only to the breast ROI. This process optimizes the algorithm and increases the processing efficiency. At this stage we try to locate all edge paths that are circular or terminate either on the left base line or the bottom of the image, forming a closed structure. This process removes all noise and discrete objects from the edge map that are inconsequential to the image. We start by locating all the edge paths that originates from the top margin line namely the first row of the image. Figure 4: Original Mammogram along with Edge Map Showing Breast Region of Interest and Anatomical Regions after ASB (MIAS 184.L) 3.3 Support Vector Machine (SVM) Support Vector Machines (SVM) is a supervised learning technique that can be used for classification and regression. SVMs have a firm statistical foundation and are guaranteed to converge to a global minimum during training. They are also considered to have better generalization capabilities than neural networks. SVM is known to be an excellent tool for binary classification problems, similar to the one here, by seeking the optimal separating hyper plane that provides efficient separation of the data and maximizes the margin.

4 MIT International Journal of Electronics and Communication Engineering Vol. 3, No. 1, Jan. 013, pp In other words, SVM takes the closest vectors from both classes, assuming they are linearly separable, and maximizes the distance between them by a hyper plane. On the other hand, if the data are not linearly separable, using kernel functions, SVM will map the data into a higher dimensional feature space where the data can become linearly separable. IV. EXPERIMENTAL RESULT Case 1: Fatty Tissue with Abnormalities Results obtained by applying the proposed algorithms on MIAS image mdb08 comprised predominantly Fatty tissues where abnormalities are present. V. CONCLUSION This Paper presented a methodology for detection of abnormal masses from digital mammogram, which can also be used in the development of a CAD tool. Such methodology used for both purposes is subdivided into pre-processing, reduction of mass candidates, and classification of segmented structures into mass or non-mass and Support Vector Machine classification. The results indicate that the use of these techniques in the detection of masses is promising, since it achieves accuracy rates of over 98.%. This will lead to a natural development of a CAD system capable of assisting health professionals in the painstaking task of tracing mammograms in search of mass abnormalities. REFERENCES Figure 5: MIAS mdb08.l: (a) Original Mammogram, (b) Anatomical Regions after SRGA, (c) Mammogram Showing Presence of Abnormal Region and (d) Ground Truth (GT) of Abnormal Region Table 1: Common Measures Used in the Evaluation of Our Proposed Methods Measures Computation Our Technique Accuracy TN + TP / TN + TP + FP + FN Error rate FP + FN / FP + FN + TP + TN Sensitivity TP / TP + FN True Negative Fraction/ Rate False Positive Fraction/ Rate TN / TN + FP K-Mean SVM Change in % TNF/R [1] Medindia, Breast Cancer In India Rising Rapidly, January, 006. [] American College of Radiology (ACR): ACR Breast Imaging Reporting and Data System, Breast Imaging Atlas, 4th edn., Reston, VA, USA (003). [3] Rangayyan, R.M., Ayres, F.J., Desautels, J.E.L.: A Review of Computer-Aided Diagnosis of Breast Cancer: Toward the Detection of Subtle Signs. Journal of the Franklin Institute 344(3-4), (007). [4] Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-Aided Detection and Diagnosis in Mammography. In: Bovik, A.C. (ed.) Handbook of Image and Video Processing. Elsevier Academic Press, Amsterdam (005). [5] Petrick N, Chan HP, Wei D, et al. Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification, Med Phys 1996; 3: [6] Zheng B, Chang YH, Gur D. Computer Detection of Masses in Digitized Mammograms Using Single-image Segmentation and a Multilayer Topographic Feature Analysis, AcadRadiol 1995; : [7] Catarious DM, Baydush AH, Floyd CE. Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system, Med Phys 004; 31: [8] Timp S, Karssemeijer N. A New D Segmentation Method Based on Dynamic Programming Applied to Computer Aided Detection in Mammography, Med Phys 004; 31: [9] Te Brake GM, Karssemeijer N. Segmentation of Suspicious Densities in Digital Mammograms, Med Phys 001; 8: [10] Vyborny CJ, Doi T, O Shaughnessy KF, et al. Breast Cancer: Importance of Spiculation in Computer-aided Detection, Radiology 000; 15: [11] Kegelmeyer WP, Pruneda JM, Bourland PD, et al. Computer- Aided Mammographic Screening for Spiculated Lesions, Radiology 1994; 191: [1] M.L. Comer, S. Liu, and E. J. Delp, Statistical Segmentation of Mammograms in Digital Mammography, K. oi, Ed., International Congress Series. Amsterdam, the Netherlands: Elsevier, 1996, pp

5 MIT International Journal of Electronics and Communication Engineering Vol. 3, No. 1, Jan. 013, pp [13] H.D. Li, M. Kallergi, L.P. Clarke, and V.K. Jain, Markov Random Field for Tumor Detection in Digital Mammography, IEEE Trans. Med. Imag., Vol. 14, pp , June [14] Huo Z, Giger ML, Vyborny CJ. Analysis of Spiculation in the Computerized Classification of Mammographic Masses, Med Phys 1995; : [15] Ryohei Nakayama and Yoshikazu Uchiyama Development of New Filter Bank for Detection of Nodular Patterns and Linear Patterns in Medical Images, Systems and Computers in Japan, Vol. 36, No. 13, 005. [16] M. Sameti and R.K. Ward, A Fuzzy Segmentation Algorithm for Mammogram Partitioning in Digital Mammography, K. Doi, Ed., International Congress Series. Amsterdam, the Netherlands: Elsevier, 1996, pp [17] Zheng B, Lu A, Hardesty LA, et al. A Method to Improve Visual Similarity of Breast Masses for an Interactive Computer-aided Diagnosis Environment, Med Phys 006; 33: [18] Karssemeijer N te Brake G. Detection of stellate distortions in mammogram.ieee Trans. On Medical Imaging, 1996; 15(5): [19] J.A. Hartigan, M.A. Wong, A k-means clustering algorithm, Applied Statistics 8 (1979) URL org/view/ /di99334/99p04867/0 [0] C.J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Kluwer Academic Publishers (1998).