An Adaptive Mammographic Image Enhancement in Orthogonal Polynomials Domain
X-ray mammography is the most effective method for
the early detection of breast diseases. However, the typical diagnostic
signs such as microcalcifications and masses are difficult to detect
because mammograms are of low-contrast and noisy. In this paper, a
new algorithm for image denoising and enhancement in Orthogonal
Polynomials Transformation (OPT) is proposed for radiologists to
screen mammograms. In this method, a set of OPT edge coefficients
are scaled to a new set by a scale factor called OPT scale factor. The
new set of coefficients is then inverse transformed resulting in
contrast improved image. Applications of the proposed method to
mammograms with subtle lesions are shown. To validate the
effectiveness of the proposed method, we compare the results to
those obtained by the Histogram Equalization (HE) and the Unsharp
Masking (UM) methods. Our preliminary results strongly suggest
that the proposed method offers considerably improved enhancement
capability over the HE and UM methods.
[1] S. A. Feig, "Decreased cancer mortality through mammographic
screening: Results of clinical trials", Radiology, vol. 167, pp. 659-665,
1988.
[2] R. M. Rangayyan, L. Shen, Y. Shen, J. E. L. Desautels, H. Byrant, T. J.
Terry, N. Horeczko, and M. S. Rose, "Improvement of sensitivity of
breast cancer diagnosis with adaptive neighborhood contrast
enhancement of mammograms", IEEE Trans. Inf. Technol. Biomed.,
vol. 1, no. 3, pp. 161-169, 1997.
[3] P. Sakellaropulos, L. Costaridou and G. Panayiotakis, "A wavelet-based
spatially adaptive method for mammographic contrast enhancement,"
Physics in Medicine and Biology, vol. 48, no. 6, pp.787-803, 2003.
[4] A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli,
"Mammographic Images Enhancement and Denoising for Breast Cancer
Detection Using Dyadic Wavelet Processing", IEEE Trans. Inst. and
Meas., vol. 57, no. 7, pp. 1422-1430, 2008.
[5] S. Dippel, M. Stahl, R. Wiemker, and T. Blaffert, "Multiscale Contrast
Enhancement for Radiographies: Laplacian Pyramid Versus Fast
Wavelet Transform", IEEE Trans. Med. Imaging, vol. 21, pp. 343 -
353, 2002.
[6] J. Scharcanski and C. Jung, "Denoising and enhancing digital
mammographic images for visual screening", Comput Med Imaging
Graph., vol. 30, no. 4, pp. 243-54, 2006.
[7] M. Malfait and D. Roose, "Wavelet based image denoising using a
Markov Random Field a priori model", IEEE Trans. Image Processing,
vol. 6, no. 4, pp. 549-565, 1997.
[8] H.D. Cheng and H. Xu, "A novel fuzzy logic approach to mammogram
contrast enhancement", Information Sciences, vol. 148, pp. 167-184,
2002.
[9] A. Papadopoulos, D.I. Fotiadis and L. Costaridou, "Improvement of
microcalcification cluster detection in mammography utilizing image
enhancement techniques", Computers in Biology and Medicine, vol. 38,
pp. 1045 - 1055, 2008.
[10] P. Heinlein, J. Drexl and W. Schneider, "Integrated wavelets for
enhancement of microcalcifications in digital mammography", IEEE
Trans. Medical Imaging, vol. 22, pp. 402-413, 2003.
[11] M. G. Linguraru, K. Marias, R. English and M. Brady, "A biologically
inspired algorithm for microcalcification cluster detection", Medical
Image Analysis, vol. 10, pp. 850-862, 2006.
[12] H. Li, K.J.U. Liu and S.C.B. Lo, Fractal modeling and segmentation for
the enhancement of microcalcifications in digital mammograms, IEEE
Trans. Med. Imaging, vol. 16, no.6, pp. 785-798, 1997.
[13] M. P. Sampat, G. J. Whitman, A. C. Bovik and M. K. Markey,
"Comparison of Algorithms to Enhance Spicules of Spiculated Masses
on Mammography", Journal of Digital Imaging, vol. 21, no. 1, pp. 9- 17,
2008.
[14] A. R. Dominguez and A. K. Nandi, "Detection of masses in
mammograms via statistically based enhancement, multilevelthresholding
segmentation, and region selection", Comp. Med. Imaging
and Graph., vol. 32 pp. 304-315, 2008.
[15] J. Tang and E. Peli, "An Image Enhancement Algorithm in JPEG
Domain for Low-vision Patient", IEEE Transaction On Biomedical
Engineering, vol. 51, no.11, pp. 2013- 2023, 2004.
[16] L. Ganesan and P. Bhattacharyya, "Edge Detection in Untextured and
Textured Images-A Common Computational Framework", IEEE Trans.
Syst. Man Cybern, vol. 27, no. 5 pp. 823-834, 1997.
[17] R. Krishnamoorthi, "Transform coding of monochrome images with a
statistical design of experiments approach to separate noise", Pattern
Recognition Letters, vol. 28 , pp. 771-777, 2007.
[18] M. S. Bartlett, "Properties of sufficiency and statistical tests",
Proceedings of the Royal Statistical Society Series A, vol. 160, pp. 268-
282, 1937.
[19] J. Suckling, J. Parker, D. R. Dance, S. Astley, I. Hutt, C. R. M. Boggis, I.
Ricketts, E. Stamatakis, N. Cerneaz, S. L. Kok, P. Taylor, D. Betal, and
J. Savage, "The mammographic image analysis society mammogram
database," in Proc. 2nd Int. Workshop Digital Mammography, York,
U.K., pp. 375-378, 1994.
[20] R. M. Haralick, K. Shanmugan and I. Dinstein, "Textural features for
image classification", IEEE Trans. On Systems, Man and Cybernetics,
vol. 3, pp. 610-621, 1973.
[1] S. A. Feig, "Decreased cancer mortality through mammographic
screening: Results of clinical trials", Radiology, vol. 167, pp. 659-665,
1988.
[2] R. M. Rangayyan, L. Shen, Y. Shen, J. E. L. Desautels, H. Byrant, T. J.
Terry, N. Horeczko, and M. S. Rose, "Improvement of sensitivity of
breast cancer diagnosis with adaptive neighborhood contrast
enhancement of mammograms", IEEE Trans. Inf. Technol. Biomed.,
vol. 1, no. 3, pp. 161-169, 1997.
[3] P. Sakellaropulos, L. Costaridou and G. Panayiotakis, "A wavelet-based
spatially adaptive method for mammographic contrast enhancement,"
Physics in Medicine and Biology, vol. 48, no. 6, pp.787-803, 2003.
[4] A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli,
"Mammographic Images Enhancement and Denoising for Breast Cancer
Detection Using Dyadic Wavelet Processing", IEEE Trans. Inst. and
Meas., vol. 57, no. 7, pp. 1422-1430, 2008.
[5] S. Dippel, M. Stahl, R. Wiemker, and T. Blaffert, "Multiscale Contrast
Enhancement for Radiographies: Laplacian Pyramid Versus Fast
Wavelet Transform", IEEE Trans. Med. Imaging, vol. 21, pp. 343 -
353, 2002.
[6] J. Scharcanski and C. Jung, "Denoising and enhancing digital
mammographic images for visual screening", Comput Med Imaging
Graph., vol. 30, no. 4, pp. 243-54, 2006.
[7] M. Malfait and D. Roose, "Wavelet based image denoising using a
Markov Random Field a priori model", IEEE Trans. Image Processing,
vol. 6, no. 4, pp. 549-565, 1997.
[8] H.D. Cheng and H. Xu, "A novel fuzzy logic approach to mammogram
contrast enhancement", Information Sciences, vol. 148, pp. 167-184,
2002.
[9] A. Papadopoulos, D.I. Fotiadis and L. Costaridou, "Improvement of
microcalcification cluster detection in mammography utilizing image
enhancement techniques", Computers in Biology and Medicine, vol. 38,
pp. 1045 - 1055, 2008.
[10] P. Heinlein, J. Drexl and W. Schneider, "Integrated wavelets for
enhancement of microcalcifications in digital mammography", IEEE
Trans. Medical Imaging, vol. 22, pp. 402-413, 2003.
[11] M. G. Linguraru, K. Marias, R. English and M. Brady, "A biologically
inspired algorithm for microcalcification cluster detection", Medical
Image Analysis, vol. 10, pp. 850-862, 2006.
[12] H. Li, K.J.U. Liu and S.C.B. Lo, Fractal modeling and segmentation for
the enhancement of microcalcifications in digital mammograms, IEEE
Trans. Med. Imaging, vol. 16, no.6, pp. 785-798, 1997.
[13] M. P. Sampat, G. J. Whitman, A. C. Bovik and M. K. Markey,
"Comparison of Algorithms to Enhance Spicules of Spiculated Masses
on Mammography", Journal of Digital Imaging, vol. 21, no. 1, pp. 9- 17,
2008.
[14] A. R. Dominguez and A. K. Nandi, "Detection of masses in
mammograms via statistically based enhancement, multilevelthresholding
segmentation, and region selection", Comp. Med. Imaging
and Graph., vol. 32 pp. 304-315, 2008.
[15] J. Tang and E. Peli, "An Image Enhancement Algorithm in JPEG
Domain for Low-vision Patient", IEEE Transaction On Biomedical
Engineering, vol. 51, no.11, pp. 2013- 2023, 2004.
[16] L. Ganesan and P. Bhattacharyya, "Edge Detection in Untextured and
Textured Images-A Common Computational Framework", IEEE Trans.
Syst. Man Cybern, vol. 27, no. 5 pp. 823-834, 1997.
[17] R. Krishnamoorthi, "Transform coding of monochrome images with a
statistical design of experiments approach to separate noise", Pattern
Recognition Letters, vol. 28 , pp. 771-777, 2007.
[18] M. S. Bartlett, "Properties of sufficiency and statistical tests",
Proceedings of the Royal Statistical Society Series A, vol. 160, pp. 268-
282, 1937.
[19] J. Suckling, J. Parker, D. R. Dance, S. Astley, I. Hutt, C. R. M. Boggis, I.
Ricketts, E. Stamatakis, N. Cerneaz, S. L. Kok, P. Taylor, D. Betal, and
J. Savage, "The mammographic image analysis society mammogram
database," in Proc. 2nd Int. Workshop Digital Mammography, York,
U.K., pp. 375-378, 1994.
[20] R. M. Haralick, K. Shanmugan and I. Dinstein, "Textural features for
image classification", IEEE Trans. On Systems, Man and Cybernetics,
vol. 3, pp. 610-621, 1973.
@article{"International Journal of Medical, Medicine and Health Sciences:55735", author = "R. Krishnamoorthy and N. Amudhavalli and M.K. Sivakkolunthu", title = "An Adaptive Mammographic Image Enhancement in Orthogonal Polynomials Domain", abstract = "X-ray mammography is the most effective method for
the early detection of breast diseases. However, the typical diagnostic
signs such as microcalcifications and masses are difficult to detect
because mammograms are of low-contrast and noisy. In this paper, a
new algorithm for image denoising and enhancement in Orthogonal
Polynomials Transformation (OPT) is proposed for radiologists to
screen mammograms. In this method, a set of OPT edge coefficients
are scaled to a new set by a scale factor called OPT scale factor. The
new set of coefficients is then inverse transformed resulting in
contrast improved image. Applications of the proposed method to
mammograms with subtle lesions are shown. To validate the
effectiveness of the proposed method, we compare the results to
those obtained by the Histogram Equalization (HE) and the Unsharp
Masking (UM) methods. Our preliminary results strongly suggest
that the proposed method offers considerably improved enhancement
capability over the HE and UM methods.", keywords = "mammograms, image enhancement, orthogonalpolynomials, contrast improvement", volume = "4", number = "1", pages = "27-9", }
