A quality enhanced preprocessing method for mammogram ROI extraction

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Early detection of breast cancer is necessary because it is considered as one of the most common reason of cancer death among women. Nowadays, the basic screening test for detection of breast cancer is Mammography which con-sists of various artifacts. These artifacts leads to wrong results in detection of breast cancer. Therefore, Computer Aided Diagnosis (CAD) system mainly focus in removal of artifacts and mammogram quality enhancement. By this procedure, exact Region of Interest (ROI) can be obtained. This is a challenging procedure because detection of pecto-ral muscle and cancer region is difficult. Here a comparative study of different preprocessing and enhancement tech-niques are done by testing proposed system on mammogram mini-MIAS database. Result obtained shows that sug-gested system is efficient for CAD system.

     

     


  • Keywords


    Mammogram; Computer Aided Diagnosis; Region of Interest.

  • References


      [1] R Siegel, Naishadham D, Jemal A. Cancer Statistics, 2013.CA: Cancer J Clinicians 2013; 63: 11-30.

      [2] JS Mandelblatt, Cronin KA, Bailey S, Berry DA, de Koning HJ, Draisma G, Huang H, Lee SJ, Munsell M, Plevritis SK. Effects of mammography screening under different screening schedules, model estimates of potential benefits and harms. Annal Internal Medicine 2009; 151:738-747.

      [3] Sundaram KM, Sasikala D, Rani PA. A Study on Preprocessing a Mammogram Image Using Adaptive Median Filter. Int J Innovative Res Sci Eng Technol 2014;3: 10333-10337.

      [4] Acharya RU, Ng EYK, Chang YH, Yang J, Kaw GJL. Computer-Based Identification of Breast Cancer Using Digitized Mammograms. J Medical Systems 2008; 32:499-507.Bland, P.H. and C.R. Meyer, Roboust threedimensional object definition in CT and MRI. Med. Phys., 1996, 23(1), p. 99-107

      [5] Mustra M, Grgic M. Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Signal Processing 2013; 93: 2817-2827.

      [6] Mencattini A, Salmeri M. Noise estimation in mammographic images for adaptive denoising. EFOMP European Conference on Medical Physics 2007.

      [7] Liu CC, Tsai CY, Liu J, Yu CY, Yu SS. A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis. Computer and Mathematics with Applications 2012;64(5), pp.100-1107.

      [8] Ferrari RJ, Rangayyan RM, Desautels J E, Borges RA, Frere AF, Automatic identification of the pectoral muscle in mammograms, IEEE Transactions on Medical Imaging 2004;23(2), pp.232-245.

      [9] Bhateja V, Verma A, Rastogi K, Malhotra C, Satapathy SC. Performance Improvement of Decision Median Filter for Suppression of Salt and Pepper Noise. Advances in Signal Processing and Intelligent Recognition Systems 2014, Springer: 287-297.

      [10] U. Bick, M.L. Giger, R.A. Schmidt, R.M. Nishikawa, D.E. Wolverton, and K. Doi, “Automated Segmentation of Digitized Mammograms”, Academic Radiology, vol. 2, no. 2, pp. 1–9, 1995.

      [11] Naveed N, Hussain A, Arfan JM, Choi TS. Quantum and impulse noise filtering from breast mammogram images. Comput Methods Programs Biomed 2012; 108: 1062-1069.

      [12] Qian, W., Clarke, L.P., Kallergi, M. and Clark, R.A. “Tree- Structured Nonlinear Filters in Digital Mammography,” IEEE Transactions on Medical Imaging, Vol. 12, No. 1, pp. 25–36, 1994.

      [13] Li, H., Liu, K.J.R. and Lo, S.C.B. “Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms,” IEEE Transactions on Medical Imaging, Vol.16, No. 6, pp. 785–798, 1997.

      [14] K Thangavel, M Karnan, R Sivakumar, AK Mohideen, “Automatic detection of microcalcification in mammograms-a review”, International Journal on Graphics, Vision and Image Processing, 5 (5), 31-61 2005

      [15] Kovalerchuk, B., Traintaphyllou E.J.F. Ruiz and J. Clayton, J. “Fuzzy Logic in Computer-Aided Breast Cancer Diagnosis: Analysis of Lobulation,” Artificial Intelligence in Medicine, Vol. 11, pp. 75–85, 1997.

      [16] Kobatake, H., Murakarni, M., Takeo, H. and Nawano, S. “Computerized Detection of Malignant Tumors on Digital Mammograms,” IEEETransactions on Information Technology in Biomedicine, Vol. 18, No. 5, pp. 369–378, 1999.

      [17] Ferrari, R.J., De Carvalho, F., Marques, P.M.A. and Frere. A.F. “Computerized classification of breast lesions: shape and texture analysis using an artificial neural network,” 7th international conference on Image Processing and its applications, pp. 517–521,1999.

      [18] Bhangale, T., Desai, U.B. and Sharma, U. “An unsupervised scheme for detection of microcalcifications on mammograms,” IEEE International Conference on Image Processing, pp. 184–187, 2000.

      [19] Rogova, G.L., Stomper, P.C. and Ke, C. “Microcalcification texture analysis in a hybrid system for computer aided mammography,” SPIE, Vol. 3661, pp. 1426–1433, 1999.

      [20] Chang, R.F., Wu, W.J., Tseng, C.C., Chen, D.R. and Moon, W.K. “3-D Snake for US in Margin Evaluation for Malignant Breast Tumor Excision Using Mammotome,” IEEE Transactions on Information Technology in Biomedicine, Vol. 7, no. 3, pp. 197–201, 2003.

      [21] J. Suckling, J. Parker, D.R. Dance, S. Astley, I. Hutt, C.R.M. Boggis, I. Ricketts, E. Stamatakis, N. Cernaez, S.L. Kok, P.Taylor, D. Betal, J. avage, The mammographic image analysis society digital mammogram database,in: Proceedings of the 2nd International Workshop on Digital Mammography, York, England, 10-12 July 1994, Elsevier Science, Amsterdam, 1994, pp. 375–378.

      [22] http://peipa.essex.ac.uk/info/mias.html.


 

View

Download

Article ID: 16575
 
DOI: 10.14419/ijet.v7i2.25.16575




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.