The week's pick
Random Articles
Reseach Article
Pre-processing of Mammography Image for Early Detection of Breast Cancer
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 144 - Number 3 |
| Year of Publication: 2016 |
| Authors: Aziz Makandar, Bhagirathi Halalli |
10.5120/ijca2016910153
|
Aziz Makandar, Bhagirathi Halalli . Pre-processing of Mammography Image for Early Detection of Breast Cancer. International Journal of Computer Applications. 144, 3 ( Jun 2016), 11-15. DOI=10.5120/ijca2016910153
Abstract
Breast cancer is one of the most prevalent causes of death among women worldwide. Hence, the early detection helps to save the life of the women. Mammography is the basic screening test for breast cancer. It consist many artefacts, which negatively influences in detection of the breast cancer. Therefore, removing artefacts and enhancing the image quality is a required process in Computer Aided Diagnosis (CAD) system. The accuracy and efficiency of the CAD is increased by providing exact Region of Interest (ROI). Extracting ROI is a challenging task in preprocessing because the presence of pectoral muscle influences the detection of abnormality. Here, the proposed show that the wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) techniques efficiently aids for enhancing the quality of the image, thereby it also removes the unwanted background and the pectoral muscle by using thresholding and modified region growing technique respectively. Furthermore, the proposed algorithm was tested on mini-MIAS database; the result obtained was compared with completeness and correctness for pectoral muscle removal and was reported as 98% and 97% respectively. Collectively, these results suggest that the proposed method is well suited for improving the quality of mammography image for Auto-CAD system.
References
- National Cancer Institute (NCI) Web site, http://www.cancernet.gov
- Union for International Cancer Control, http://timesofindia.indiatimes.com/city/indore/37-pros-face-risk-of-cancer-Survey/articleshow/29830667.cms
- Indra Kanta Maitra, Sanjay Nag, Samir Kumar Bandyopadhyay, Technique for preprocessing of digital mammogram, computational methods and programs in biomedicine 107 (2012), pp. 175–188.
- Aziz Makandar and Bhagirathi Halalli, A Review on Preprocessing Techniques for Digital Mammography images, International Journal of Computer Applications (IJCA) National conference on Digital Image and Signal Processing, DISP 2015, pp.23-27.
- Jwad Nagi, Automated Breast Profile Segmentation for ROI Detection Using Digital Mammograms, IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur.
- Samir Kumar Bandyopadhyay, Pre-processing of Mammogram Images, International Journal of Engineering Science and Technology, Vol. 2(11), 2010, pp. 6753-6758.
- Barghout, Lauren, and Lawrence W. Lee, Perceptual information processing system, Paravue Inc. U.S. Patent Application 10/618,543.
- 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.
- 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.
- D.NarainPonraj, M.Evangelin Jenifer, P. Poongodi, J. Samuel Manoharan, A Survey of the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer, Journal of Emerging Trends in Computing and Information Sciences, VOL. 2, NO. 12, December 2011, pp.656-664.
- Maciej A. Mazurowski, Joseph Y. Lo, Brian P. Harrawood, Georgia D. Tourassi, Mutual information- based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis, Journal of Biomedical Informatics (2011).
- K. MeenakshiSundaram, D. Sasikala, P. Aarthi Rani, A Study On Preprocessing A Mammogram Image Using Adaptive Median Filter, IJIRSET, Vol. 3, Issue 3, March 2014.
- 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.
- R.C., Gonzalez, R.E. Woods, Digital image processing, 2007. Third edition, pp.118-529
- Linda G. Shapiro and George C. Stockman (2001), Computer Vision, New Jersey, Prentice-Hall, pp 279-325.
- Aziz Makandar and Bhagirathi Halalli, “Breast Cancer Image Enhancement using Median Filter and CLAHE,” International Journal of Scientific & Engineering Research, Volume 6, Issue 4, pp. 462-465, 2015.
- Makandar, Aziz Ur Rahaman, and K. Karibasappa, Wavelet Based Medical Image Compression Using SPHIT, Journal of Compute and Mathematical Sciences Vol 1.7(2010): pp. 769-924.
- B. Senthilkumar, G. Umamaheswari, A Novel Edge Detection Algorithm for the Detection of Breast Cancer, European Journal of Scientific Research, ISSN 1450-216X Vol.53 No.1 (2011), pp.51-55.
- Makandar, Aziz, and Bhagirathi Halalli, Image Enhancement Techniques using Highpasand Lowpass Filters, International Journal of Computer Applications109.14 (2015): 21-27.
- Antonis Daskalakis, et al, An efficient CLAHE-based, spot-adaptive, image segmentation technique for improving microarray genes’ quantification, in: 2nd International Conference on Experiments/Process/SystemModelling/ Simulation and Optimization, Athens, 4–7 July, 2007.
- Samir Kumar Bandyopadhyay, Pre-processing of Mammogram Images, International Journal of Engineering Science and Technology, Vol. 2(11), 2010, pp. 6753-6758.
- Farhan Akram, Jeong Heon Kim, Inteck Whoang, and Kwang Nam Choi, A Preprocessing Algorithm for the CAD System of Mammograms Using the Active Contour Method, Applied Medical Informatics Vol. 32, No. 2 2013, pp: 1-13.
Index Terms
Keywords