Estimation of Breast Density and Feature Extraction of Mammographic Images

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1 IJIRST International Journal for Innovative Research in Science & Technology Volume 2 Issue 11 April 2016 ISSN (online): Estimation of Breast Density and Feature Extraction of Mammographic Images Anusree Mohan Dr. Devesh D Nawgaje M.E. Student Professor Department of Electronics and Telecommunication Engineering Department of Electronics and Telecommunication Engineering S.S.G. M. C.E., Shegaon, India, S.S.G. M. C.E., Shegaon, India, Abstract Breast cancer is one of the leading causes of fatality in women. Mammogram is the effectual modality for early detection of breast cancer. Increased mammographic breast density is a moderate independent risk factor for breast cancer, Radiologists have estimated breast density using four broad categories (BI-RADS) swearing on visual assessment of mammograms. But if we can measure breast density quantitatively, we can provide most accurate and a reliable density measures. Breast density and Lesion feature extraction plays important role in determining cancer risk. Breast contour helps to find the position of the nipple, as its position is important for registration of left and right breasts, to detect bilateral asymmetry. The shape of the mass border helps radiologists to judge whether mass is malignant or benign. Novel algorithms are designed for 1) Breast Density Estimation 4) Extraction of features[15] from the mass. These features help to further investigate in a clinical evaluation for classification to detect the cancer in early stages. Keywords: Breast density, Mass, Malignant, Benign, Feature I. INTRODUCTION Breast cancer is the conducing cause of death in women and more so in urban areas in India. It accounts for about 25% to 33% of all cancers in women. Mammography is the efficacious technique for detecting breast cancer in early stages. About 50% breast cancer patients in India confront in stages 3 and 4 [1], so there is urgent need to diagnose the breast cancer in early stages. Inadequate image quality makes radiologists difficult to detect subtle signs of breast cancer like masses, micro calcifications. Several image processing techniques have been developed to improve the detection of abnormal features in breast mammograms to increase survival rate and chances of complete recovery. Breast density is a significant measure which indicates presence of abnormality. It is very difficult task to detect malignant lesions in dense breast. Wolfe in [2] inferred that there is a relation between parenchymal pattern and breast cancer. Automated approach to detect breast parenchymal density qualitatively helps radiologists Masses are the most common asymmetric signs of cancer and appear brighter than the surrounding tissue [3]. Most benign masses possess well-defined sharp borders, while malignant tumors often have ill-defined, micro lobulated, or speculated borders and further extraction of these features helps for classification. Bilateral asymmetry is an asymmetry of the breast parenchyma between left and right breast, may indicate breast cancer in its early stage. Many techniques have been developed for the detection of bilateral asymmetry, quality assessment of breast density, breast contour extraction that assists radiologists for early detection of breast cancer. In [4] they applied minimum cross entropy to get threshold values to segment main core of glandular region. In [5] they estimated breast density values by segmenting breast region with statistical approach and concluded that breast cancer patients have higher breast density. Extraction of breast contour is also very important to find the position of the nipple, as its position is important for mass detection in the next stages and alignment of left and right breasts. Extraction of breast border in [6] is done using polynomial modeling. In [7] segmentation of mass is done using region growing technique, where Harris corner technique is used to get the seed value. Feature extraction of suspicious regions helps doctors to detect cancer in early stages [8]. In [9] mass was segmented using isocontour map and texture features, shape features are extracted for further classification. Wavelet features are extracted for circular lines of extracted mass [10]. In [11] bit planes 6 and 7 were considered for the extraction of statistical features and logical mapping is done for mean and standard deviation. Morphological features were calculated for micro calcification [12]. We designed algorithms for image enhancement, segmentation and calculated bilateral asymmetry [13]. We segmented Mass region and superimposed mass boundary on the mass [14] so that radiologists can observe mass lesion exactly and extracted geometric features, Wavelet features, and Texture features from the mass. II. IMPLEMENTATION USING LAB VIEW AND MATLAB Estimation of Breast Density In this paper, first we enhanced the image then decomposed the image using wavelet transform. Then performed thresholding and converted the image into black and white image. Then performed morphological operation and segmented the image. Applied All rights reserved by 837

2 convolution to the segmented image. We found out maximum area and length and size of segmented region. Density is then estimated using length and size of image. We processed seven cases, of which five are cancer patients and two are benign cases. Algorithm: 1) Read the image using mat lab 2) Enhance the image 3) Image decomposition using wavelet transform 4) Perform thresholding 5) Convert into black and white image 6) Perform morphological opening with structuring element disk of radius 1 and 200 7) Segment the image by subtracting the bw image with obtained opened images. 8) Perform convolution of the images. 9) Calculate length and size of the output image Breast density = length of output image X 100 Size of output image Fig. 1: (a)cancer mammogram (b)dense region of (a) Fig. 2: a) Benign Mammogram b) Dense Region of (a) Original Image Convolution Image Unprocessed Image Density: Fig. 3: Benign mammogram and its estimated Density All rights reserved by 838

3 Original Image Convolution Image Unprocessed Image Density: Fig. 4: malignant mammogram and its estimated density Table - 1 BD PN1 PN2 PN3 PN4 PN5 PN6 PN7 CCL 47.3% 40% 28% 36% 31% 32.1% 77.37% CCR 61% 38.5% 47% 40% 36.5% 72% 45.9% Figure 1(a) and Figure 1(b) gives cancer mammogram and its segmented glandular region, Figure 2(a) and Figure 2(b) gives benign mammogram and its segmented breast region. In Table I PN1, PN2 PN3, PN6 are the patients who have malignant masses, PN7 has cancer calcification, PN 4, PN5 are the patients who have benign masses, diagnosed by radiologist. III. OBSERVATIONS We could observe that 1) Breasts having mass, have high density for malignant and benign cases, 2) Cancer patients have high breast density (>60%) where ever mass is present. PN7 has high breast density of 77.37% in the breast, who has cancer calcifications. Segmentation of Mass and Border Extraction Mask of the lesion is obtained by applying manual threshold using histogram and morphological operations, which is of unsupervised, it doesn t require any seed. Thresholding, morphological dilation and opening are carried out using matlab Fig. 4: Flow chart of segmentation All rights reserved by 839

4 Feature Extraction from the Segmented Mass We extracted masses of six patients who have masses of which PN 1, PN2, PN 3, and PN 6 have malignant masses and PN 4, PN 5 have benign masses. We estimated features such as euler no, orientation, extent, perimeter, convex area, filled area, eccentricity, major axis length, minor axis length and equivalent diameter of the image. Table - 2 Features PN1 PN2 PN3 PN4 PN5 Euler no Orientation Extent Perimeter Convex area Filled area Eccentricity Major axis length Minor axis length Equivalent daimeter Among the above features contrast, correlation, sum of variances, sum average could delineate malignant and benign masses. Table I gives the names of haralick features and Table II gives values of haralick features to the extracted mass of six patients. Figure 10(a), 10(b) and 10(c) give plot of haralick features of patients. Determination of Class: After estimating the density of breast and calculating the various parameters as shown above we can further determine the type i.e, whether it is benign or malignant. This requires first training the algorithm and then testing. Creation of Database: Implementation of the algorithm is done by training the algorithm with known mammogram images. In training the known mammogram image is segmented first. Then the density and feature extraction is done using the proposed algorithm. The extracted information is stored in database. Evolution of Database: During evaluation new or old image is evaluated. In testing the known or unknown mammogram image is segmented first. Then the density and feature extraction is done using the proposed algorithm. Fig. 6: Flowchart of Evolution All rights reserved by 840

5 IV. CONCLUSION In this study, images of 14 patients are given by the hospital of which 6 patients have the lesions and 1 patient have cancer calcifications, where PN1, PN2, PN3, PN6, PN7are malignant and PN4, PN5 are benign. We extracted border of the mammogram both in CC view and MLO view to detect nipple location for registration. The border of the mass is extracted from segmented mass to define the shape of the border. We calculated breast density of seven mammograms of which cancer patients having mass, have high breast density. We computed parameters from the extracted mass region. These parameters help to classify benign and malignant masses. In future we would like to develop a model to classify malignant and benign breast images, based on the parameters like bilateral asymmetry, breast density, border of the mass and Haralick parameters. REFERENCES [1] [2] Wolfe Mammographic Parenchymal Patterns and Breast Cancer Risk [3] [4] S. Tzikopoulos, H. Georgiou, M. Marvoforakis and S. Theodoridis, Full Automated scheme for breast density estimation and asymmetry detection of mammograms. [5] L. Li, Z. Wu, L. Chen, F. George, Z. Chen, A. Salem and M. Kallegiri,(2005), Breast Tissue Density and CAD Cancer Detection in Digital Mammography, IEEE EMBS Int conf.,sep 1-4, 2005 [6] H. Mirzaalian, M. R. Ahmadzadeh and F. Kolahdoozan, (2006), Breast Contour on Digital Mammogram, ICTTA in Information and Communication Technologies, Vol 1, pp: , [7] B. Senthilkumar, G. Umamaheswari and J. Karthik, (2010), A novel region growing segmentation algorithm for the detection of breast cancer, IEEE Int conf. in computational intelligence and computing research, pp 1-4, Dec [8] H. Al-Shamlan and A. El-Zaart, (2010), Feature extraction values for breast cancer mammography images, IEEE Int conf on bioinformatics and biomedical technology, pp , April [9] W. Han, J. Dong, Y. Guo, M. Zhang and J. Wang, (2011), Identification of masses in digital mammogram using an optimal set of features, IEEE.conf. ontrust, Security and Privacy in computing and Communications, pp , Nov [10] J. K. Dash and L. Sahoo, (2012), Wavelet Based Features ofcircular Scan Lines for Mammographic Mass Classification, IEEE confrecent Advances in Information Technology (RAIT), pp 58-61, March [11] M. Tayel and A. Mohsen, (2011), Statistical Measures and Criteria for ROI Identification in Breast Mammograms, IEEE colloquim Humanities, Science and Engineering, pp , Dec All rights reserved by 841