Mammographic Image Analysis method for early detection of Breast Cancer

Bhagwati Charan Patel*, G. R. Sinha**
* Associate Professor, Shri Shankaracharya College of Engg. & Tech., Bhilai, (C.G.), India.
** Professor & Head, Shri Shankaracharya College of Engg. & Tech., Bhilai, (C.G.), India.
Periodicity:August - October'2011
DOI : https://doi.org/10.26634/jfet.7.1.1672

Abstract

Cancer has become one of the biggest threats to human life for many years, and is expected to become the leading cause of death over the next few decades. Mammography is a specific type of imaging that uses a low-dose x-ray system to examine breasts. A mammography exam, called a mammogram, is used to aid in the early detection and diagnosis of breast cancer facilitated with digital mammography can increase survival rate and chances for patient's complete recovery. Early detection performed on X-ray mammography is the key to improve breast cancer prognosis by the detection of any lesions or cysts in breasts.  In this paper we have present segmentation technique based on the extraction of catchments basins through a topographic representation of the mammography breast image. We have first carried out a preprocessing step which removes or attenuates the curvilinear structures present in a mammogram and corresponding to the blood vessels, veins, milk ducts, speculations and fibrous tissue. Multiple image enhancement steps were needed to exaggerate the differences between the frequency-domain images of normal and cancerous tissues. Mathematical morphology stresses the role of “shape” in image pre-processing, segmentation and object description. In this paper, the authors describe cancer detection system based on the analysis of Mammogram, which can be used by the doctors to decide whether further biopsy is needed, or not. The system will act as a decision support system and uses image processing techniques to analyze the mammograms. The application takes the input as mammogram image and reports the presence of suspicious region, if any. The paper also presents the results of experiment conducted on a large set of mammogram images.

Keywords

Mammogram; Breast Image; Segmentation; Image Enhancement; Topology.

How to Cite this Article?

Patel , B. C. and Sinha , G.R. (2011). Mammographic Image Analysis Method For Early Detection of Breast Cancer. i-manager’s Journal on Future Engineering and Technology, 7(1), 10-16. https://doi.org/10.26634/jfet.7.1.1672

References

[1].Cancer Facts & Figures American Cancer Society (2010).http://www.cancer.org/Research/CancerFactsFig ures/CancerFactsFigures/cancer-facts-and-figures- 2010.
[2].Rae, J.M., Creighton, C.J., Meck, J.M., Haddad, B.R. & Johnson, M.D (2007). “MDA-MB- 435 cells are derived from M14 melanoma cells—a loss for breast cancer, but a boon for melanoma research”. Breast Cancer Res. Treat. Vol. 104, pp.13–19.
[3]. F.H.K. Gohari, and C.J. Smith (1981). “Histological and ultrastructural morphology of 7,12 dimethylbenz(alpha)” . Diagn Histopathol, Vol.4, No.4, pp. 307-33.
[4]. Fitzke, F.W. (1997). “Fourier Transform Analysis of Human Corneal Endothelial Specular Photomicrographs”. Exp Eye Res, 65.
[5]. Hieken, T., Harrison, J. Herreros, J., and Velasco, J. (2001). “Correlating sonography, mammography, and pathology in the assessment of breast cancer size”. American Journal of Surgery, Vol.182, No. 4, pp. 351-354.
[6]. V. Ruiz, A.G. (1996). “Optical Fourier techniques for medical image processing and phase contrast imaging”, Signal Processing VIII, Theories & Applications; Proceedings of EUSIPCO, Vol. 1, pp. 367-372.
[7]. J. Dengler, S. Behrens, & J.F. Desaga, (1993). “Segmentation of Micro calcifications in Mammograms”. IEEE Med. Imag, pp.623-634.
[8]. Masters, B.R. (2008). “Diagnostic digital image processing of human corneal endothelial cell patterns”. SPIE, pp. 1360-1378 .
[9]. S.E. Umbaugh, (1998). “Pattern recognition using multilayer neural-genetic algorithm”, Computer Vision and Image Processing, Prentice-Hall, Englewood Cliffs, NJ, USA, pp. 237 – 247.
[10]. O. Yli-Harja, J. Astola, and Y. Neuvo, (2007). “Analysis of the properties of median and weighted median filters using threshold logic and stack filter representation,” IEEE Trans. Signal Processing, Vol. 39, No. 2, pp. 395–410.
[11]. S.-J. Ko and Y. H. Lee, (1999). “Center weighted median filters and their applications to image enhancement,” IEEE Trans. Circuits and Systems, Vol. 38, No. 9, pp. 984–993.
[12]. P. Maragos, and R. Schafer, (2007). “Morphological Filters–Part II: Their Relations to Median, Order-Statistic, and Stack Filters,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. 35, No. 8, pp. 1170–1184.
[13]. A Polesel, G Ramponi, V J Mathews, (2000). “Image enhancement via adaptive unsharp masking.” in IEEE Transactions on Image Processing. Vol. 9, No. 3, pp. 505- 510.
[14]. Thomas Luft, Carsten Colditz, & Oliver Deussen (2006). “Image enhancement by unsharp masking the depth buffer” in ACM Transactions on Graphics, Vol. 25, No. 9, pp. 237-242.
[15]. R.C. Gonzalez and R.E. Woods, (2002). 'Digital Image Processing', Addison-Wesley Publishing Company.
[16]. H. Lee and R. H. Park, (1990). “Comments on an optimal threshold scheme for image segmentation”, IEEE Trans. Syst. Man Cybern, SMC-20, pp.741-742.
[17]. S. U. Le, S. Y. Chung, and R. H. Park, (1990). ''A comparative performance study of several global thresholding techniques for segmentation,'' Graph. Models Image Process. Vol. 52, pp.171–190.
[18]. R. Gonzales, & R. Woods, (1992). 'Digital Image Processing', Addison-Wesley Publishing Company, pp 81 – 125.
[19]. Gandhi and S.A. Kassam, (1991). “Design and performance of combination filters for signal restoration”, IEEE Trans. Signal Processing, Vol. 39, pp. 1524-1540.
[20]. R. Gonzalez and R. Woods (1992). “Digital Image Processing”, Addison-Wesley Publishing Company, pp 167 – 168.
[21]. C. A. Waring, X.W Liu. (2005). “Face Detection Using Spectral Histograms and SVMs”. IEEE Trans Systems, Man, and Cybernetics-Part B: C Cybernetics. Vol. 35, No.3,pp.467– 476.
[22]. T. Chen and H. R. Wu, (2001). “Space variant median filters for the restoration of impulse noise corrupted images,” IEEE Transactions on Circuits and Systems II, Vol.48, pp. 784–789.
[23]. Konstantinos N. Plataniotis and Anastasios N. Venetsanopoulos, (2000). “Color Image Processing and Applications,” Springer Verlag. Vol. 13, No.2, pp. 279-284.
[24]. M. Zhao, and G. de Haan, (2007). “Towards an Overview of Spatial Up-conversion Techniques”, Proceedings of ISCE'02, pp. E13-E16.
[25]. J.S. Lee, (2008). ” Refined filtering of image noise using local statistics”, Computer Vision, Graphics, and Image Processing, pp. 583-586.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.