Preprocessing and Enhancement for Mammogram Images Using Unified Approach
 |
International Journal of Computer & Organization Trends (IJCOT) | 
|
| © 2013 by IJCOT Journal | ||
| Volume-3 Issue-1 |
||
| Year of Publication : 2013 | ||
| Authors : A.Srikarthik , R.Sivakumar |
Citation
A.Srikarthik , R.Sivakumar Article: Preprocessing and Enhancement for Mammogram Images Using Unified Approach . International Journal of Computer & organization Trends (IJCOT), V3(1):32-36 Jan - Feb 2013. Published by Seventh Sense Research Group.
Abstract
Breast cancer is one of the foremost causes for the increase in mortality among women, especially in developed countries. Micro - classifications in breast tissue is one of the most incident signs considered by radiologist for an early identification of breast cancer is one of the most common forms of cancer between women. Mammography has been shown to be the most successful and reliable method for early signs of breast cancer such as masses, bilateral asymmetry architectural distortion and calcifications . In this paper the thresholding method is applied for the breast boundary identification and a new proposed modified tracking algorithm is introduced for pectoral muscle fortitude in Mammograms.
References
[1] Bick, M.L. Giger , R.A. Schmidt, R.M. Nishikawa, D.E. Wolverton, K. Doi, Automated segmentation of digitized mammograms, Acad. Radiol. 2 (1995) 1 - 9.
[2] R. Chandrasekhar, Y. Attikiouzel, Automatic breast border segmentation by background modeling and subtraction, in: M.J. Yaffe (Ed.), Proceedings of the 5th International Workshop on Digital Mammography, Medical Physics Publishing, Toronto, Canada, 2000, pp. 560 - 565.
[3] L.P. Clarke, M. Kallergi, W. Qian, H.D. Li, R.A. Clark, M.L. Silbiger, Tree - structured non - linear filter an d wavelet transform for microcalcification segmentation in digital mammography, Cancer Lett. 7 (1994) 173 - 181.
[4] S. Detounis, Computer aided detection and second reading utility and implementation in a high volume breast clinic, Appl. Radiol. 3 (9) (2004) 8 - 15.
[5] R.J. Ferrari, R.M. Rangayyan, J.E.L. Desautels, R.A. Borges, A.F Frere, Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets, IEEE Trans. Med. Imag. 20 (9) (2001) 953 - 964.
[6] M. Karnan, K. Thangavel , “ Automatic detection o f the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications” Computer Methods and Programs in bio medicine , Elsevier Ltd 8 7 ( 2 0 0 7 ) 12 - 20 .
[7] Lobregt, S. & Viergever, M . A. (1995),”A discrete dynamic contour model”, IEEE Trans. Med. Imaging 14(1), 12 – 24[8] A.J. Mendez, P.G. Tahocesb, M.J. Lado, M. Souto, J.L. Correa, J.J. Vidal, Automatic detection of breast border and nipple in digital mammograms, Comput. Meth. P rog. Biomed. 49 (1996)253 - 262.
[9] Griffiths & Tenenbaum,”From algorithmic to subjective randomness, In advances in neural information processing”, 2004.
[10] H Sheshadri, A Kandaswamy,”Breast Tissue Classification Using Statistical Featur e Extraction Of Mammograms”, Medical Imaging and Information Sciences vol. 23 2006 .
[11] K. Thangavel, M. Karnan, R. Siva Kumar, A. Kaja Mohideen, Automatic detection of microcalcification in mammograms — a review, Int. J. Graph. Vision Image Process. 5 ( 5) (2005)31 - 61.
[12] K. Thangavel, M. Karnan, CAD system for preprocessing and enhancement of digital mammograms, Int. J. Graph. Vision Image Process. 9 (9) (2006) 69 - 74
Keywords
Mammography, CAD , modified tracking algorithm