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Analysis of Breast MRI images using Wavelets For Detection of Cancer

International Conference on Electronics, Information and Communication Engineering
© 2011 by IJCA Journal
ICEICE - Number 4
Year of Publication: 2011
Manojkumar D. Bohare
Alice N. Cheeran
Vidya G. Sarode

Manojkumar D Bohare, Alice N Cheeran and Vidya G Sarode. Article: Analysis of Breast MRI images using Wavelets for Detection of Cancer. IJCA Special Issue on Electronics, Information and Communication Engineering ICEICE(4):1-3, December 2011. Full text available. BibTeX

	author = {Manojkumar D. Bohare and Alice N. Cheeran and Vidya G. Sarode},
	title = {Article: Analysis of Breast MRI images using Wavelets for Detection of Cancer},
	journal = {IJCA Special Issue on Electronics, Information and Communication Engineering},
	year = {2011},
	volume = {ICEICE},
	number = {4},
	pages = {1-3},
	month = {December},
	note = {Full text available}


Breast cancer is the second leading cause of cancer death after lung cancer among women. The greatest effect on reducing mortality in breast cancer comes from the detection and treatment of invasive cancer when it is as small as possible. Accurate preoperative diagnosis of breast lesion is essential for optimal treatment planning. In order to avoid unnecessary patient distress, it is important to achieve the definite diagnosis without delay and with as few biopsies as possible. Nowadays, when breast cancer is one of the most frequently diagnosed malignancies among women, cost-effective ways for its diagnosis are necessary. Various methods are being performed on mammographic images to detect it at the early stage. This paper describes the Analysis of the breast MR images with the help of wavelet transform. The first step is to apply histogram modification technique to improve the contrast of the image. Then de-noising and filtering are used to remove unwanted data. Finally DWT is used to separate the frequencies and IDWT and thresholding is used for the final detection of cancer.


  • C. Imaginis, Breast cancer: Statistics on incidence survival, and screening fact and figures of breast cancer.
  • N. Lee et al., “Fatty and fibro-glandular tissue volumes in the breasts of women 20-83 years old: Comparison of X-ray mammography and computer-assisted MR imaging,” Amer. J. Roentgenol., vol. 168, pp. 501–506, 1997.
  • Chan, H.P., Doi, K., Vyborny, C.J. et al. ”Improvement in radiologists detection of clustered micro calcifications on mammograms: the potential of computer-aided diagnosis”, Acad.Radiol., 1990, 25, p. 1102.
  • Qian W, Clarke LP, Kallergi M, Li H, Velthuizen R, Clark RA, et al. “Tree-structured nonlinear filter and wavelet transform for micro calcification segmentation in mammography”. SPIE Biomed Image Process Biomed Visual 1993;12(4):634–42.
  • R.N. Strickland and H.I. Hahn, "Wavelet transform for detecting micro calcifications in mammograms" IEEE Trans. Med. Imaging, vol.15, no.2, pp.218–229, 1996.
  • A. Laine, S. Schuler, J. Fan, and W. Huda. Mammographic feature enhancement by multi-scale analysis. IEEE Transactions on Medical Imaging, 13(4):725–740, December 1994.
  • A. Laine, J. Fan, and W. Yang. Wavelets for contrast enhancement of digital mammography. IEEE Engineering in Medicine and Biology, 14(5):536–550, September / October 1995.
  • S. Mallat,”A theory for multi-resolution Signal Decomposition : The Wavelet Representation”, IEEE Trans. Pattern. Machine Intell., Vol. 11, No. 7, pp 674-693, Jul. 1989.
  • I. Daubechies, “ Orthogonal Bases of Compactly Supported Wavelet”, Comm. On Pure and Applied Mathematics, Vol. 41, pp. 906-966, 1988.
  • Leiner, B.J.; Lorena, V.Q.; Cesar, T.M.; Lorenzo, M.V.; , "Micro calcifications Detection System through Discrete Wavelet Analysis and Contrast Enhancement Techniques," Electronics, Robotics and Automotive Mechanics Conference, 2008. CERMA '08 , vol., no., pp.272-276, Sept. 30 2008-Oct. 3 2008.
  • S. G. Mallat, “Multi frequency channel decomposition of images and wavelet models,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp. 2091–2110, Dec. 1989.