The week's pick
Random Articles
Reseach Article
Automatic Identification and Elimination of Pectoral Muscle in Digital Mammograms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 75 - Number 14 |
| Year of Publication: 2013 |
| Authors: K. Vaidehi, T. S. Subashini |
10.5120/13179-0784
|
K. Vaidehi, T. S. Subashini . Automatic Identification and Elimination of Pectoral Muscle in Digital Mammograms. International Journal of Computer Applications. 75, 14 ( August 2013), 15-18. DOI=10.5120/13179-0784
Abstract
Computer aided detection/diagnosis aims at assisting radiologist in the analysis of digital mammograms. Digital mammogram has emerged as the most popular screening technique for early detection of breast cancer and other abnormalities in human breast tissue. The pectoral muscle represents a predominant density region in most mammograms and can affect/bias the results of image processing methods. This paper addresses the problem of eliminating the pectoral muscles from the mammogram so that further processing for detection and diagnosis of breast cancer is confined to the breast region alone. The proposed work is done in three steps. In the first step, the mammogram is oriented to the left to minimize computations. In the second step the top left quadrant of the mammogram which contains the pectoral muscle is extracted. Next, the pectoral muscle contour is computed using our proposed algorithm. Totally 120 mammogram images were taken up for the study. A comprehensive comparison with manually-drawn contours by the radiologist reveals the strength of the proposed method and shows that it can be effectively used as a preprocessing step in the design of CAD system for breast cancer.
References
- Consolidated Report of the PBCR's 2006-2008, NCRP (National Cancer Registry Program), www. breastcancerindia. net/bc/statistics/stat_global. htm
- Vaidehi, K. , and T. S. Subashini. "A global approach for detecting mass in Digital Mammograms. " International Journal of Advancements in Research & Technology, (2012).
- ABSICON 2012, The First International Congress of the Association of Breast Surgeons of India, (2012). www. raagalahari. com
- Eklund, G. W. , Gilda Cardenosa, and Ward Parsons. "Assessing adequacy of mammographic image quality. " Radiology 190, no. 2 (1994): 297-307.
- Georgsson, F. "A transformation of mammograms based on anatomical features: in digital mammography. " In Proceedings of the 5th International Workshop on Digital Mammography, Madison, WI, Medical Physics Publishing, pp. 721-726. (2001).
- Kwok, S. M. , R. Chandrasekhar, and Y. Attikiouzel. "Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection. " In Intelligent Information Systems Conference, The Seventh Australian and New Zealand 2001, pp. 67-72. IEEE, (2001).
- Karssemeijer, Nico. "Automated classification of parenchymal patterns in mammograms. " Physics in medicine and biology 43, no. 2 (1998): 365.
- Camilus, K. Santle, V. K. Govindan, and P. S. Sathidevi. "Pectoral muscle identification in mammograms. " Journal of Applied Clinical Medical Physics 12, no. 3 (2011).
- Nagi, Jawad, S. Abdul Kareem, FarrukhNagi, and S. Khaleel Ahmed. "Automated breast profile segmentation for ROI detection using digital mammograms. " In Biomedical Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on, pp. 87-92. IEEE, (2010).
- Ferrari, Ricardo J. , Rangaraj M. Rangayyan, JE Leo Desautels, R. A. Borges, and Annie France Frere. "Automatic identification of the pectoral muscle in mammograms. " Medical Imaging, IEEE Transactions on 23, no. 2 (2004): 232-245.
- Wang, Lei, Miao-liang Zhu, Li-ping Deng, and Xin Yuan. "Automatic pectoral muscle boundary detection in mammograms based on markov chain and active contour model. " Journal of Zhejiang University SCIENCE C 11, no. 2 (2010): 111-118.
- Domingues, I. , J. S. Cardoso, I. Amaral, I. Moreira, P. Passarinho, J. Santa Comba, R. Correia, and M. J. Cardoso. "Pectoral muscle detection in mammograms based on the shortest path with endpoints learnt by SVMs. " InEngineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pp. 3158-3161. IEEE, (2010).
- Liu, Chen-Chung, Chung-Yen Tsai, Jui Liu, Chun-Yuan Yu, and Shyr-Shen Yu. "A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis. " Computers & Mathematics with Applications 64, no. 5 (2012): 1100-1107.
- Subashini, T. S. , VennilaRamalingam, and S. Palanivel. "Automated assessment of breast tissue density in digital mammograms. " Computer Vision and Image Understanding 114, no. 1 (2010): 33-43.
- Akram, F. , Kim, J. H. , Whoang,I. , and Choi, K. N. , " A preprocessing algorithm for the CAD system of mammograms using the active contour method. " Applied Medical Informatics, 32(2) (2013) : 1-13.
- Liu, Li, Jian Wang, and Tianhui Wang. "Breast and pectoral muscle contours detection based on goodness of fit measure. " In Bioinformatics and Biomedical Engineering,(iCBBE) 2011 5th International Conference on, pp. 1-4. IEEE, 2011.
- Sultana, Alina, Mihai Ciuc, and Rodica Strungaru. "Detection of pectoral muscle in mammograms using a mean-shift segmentation approach. " InCommunications (COMM), 2010 8th International Conference on, pp. 165-168. IEEE, 2010.
Index Terms
Keywords