tions, we showed that artifacts could be minimized. An additional advantage of this simple enhancement function was that its mathematical formulation included a generalization of traditional unsharp masking as a subset.
We then showed how an edge-preserving denoising stage (wavelet shrinkage) could be incorporated into the contrast enhancement framework, and introduced a method for adaptive threshold estimation. We then showed how denoising and enhancement operations should be carried out for two-dimensional images to avoid distortions due to filter orientation.
Contrast enhancement was also applied to features of specific interest to mammography, including masses, spicules, and microcalcifications. Multiresolution representations provide an adaptive mechanism for the local emphasis of such features blended into digitized mammograms. In general, improvements in image contrast based on multiscale processing were superior to those obtained using competitive algorithms of unsharp masking and adaptive histogram equalization.
Deslauriers-Dubuc interpolation (see Fig. 17) representations  on an interval enabled us to enhance arbitrary regions of interest. As shown in Fig. 21 and Fig. 22, this can provide radiologists an interactive capability for enhancing only suspicious regions of a mammogram. It also reduced the computational cost compared to processing an entire mam-mogram. These results suggest that wavelet-based image processing algorithms could play an important role in improving the imaging performance of digital mammography, as well as other clinical applications.
This work was sponsored in part by the Whitaker Foundation, and the U.S. Army Medical Research and Development
Command Grant number DAMD17-93-J-3003. The authors express sincere appreciation to Ralf Mekle and Yinpeng Jin for their time and efforts in editing and preparing the figures included in the final manuscript.
1. D. C. Wang, A. H. Vagnucci, and C. C. Li, "Digital image enhancement," Computer Vision, Graphics, and Image Processing, Vol. 24, pp. 363-381 (1983).
2. R. Hummel, "Histogram modification techniques," Computer Graphics and Image Processing, Vol. 4, pp. 209224 (1975).
3. W. Frei, "Image enhancement by histogram hyperboliza-tion," Computer Graphics and Image Processing, Vol. 6, pp. 286-294 (1977).
4. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. H. Romeny, J. B. Zimmerman, and K. Zuiderveld, "Adaptive histogram equalization and its variations," Computer Vision, Grpahics, and Image Processing, Vol. 39, pp. 355-368 (1987).
5. J. W. Oestmann, R. Greene, J. R. Rubens, E. Pile-Spellman, D. Hall, C. Robertson, H. J. Llewellyn, K. A. McCarthy, M. Potsaid, and G. White, "High frequency edge enhancement in the detection of fine pulmonary lines: parity between storage phosphor digital images and conventional chest radiograph," Invest. Radiol., Vol. 24, pp. 643-646 (1989).
6. M. Prokop, M. Galanski, J.W. Oestmann, U. Von Falkenhausen, H. Rosenthal, P. Reimer, J. NIschelsky, and J. Reichelt, "Storage of phosphor versus screenfile radiography: effect of varying exposure parameters and unsharp mask filtering on the detectability of cortical bone defects," Radiology, Vol. 177(1), pp. 109-113 (1990).
7. A. Rosenfeld and A. C. Kak, DigitaZ Picture Processing, 2nd ed. Academic Press, New York (1982).
8. L. D. Loo, K. Doi, and C. E. Metz, "Investigation of basic imaging properties in digital radiography: 4. Effect of unsharp masking on the detectability of simple patterns," Med. Phys., Vol. 12, pp. 209-214 (1985).
9. F. Neycenssac, "Contrast enhancement using the Laplacian-of-a-Gaussian filter," CVGIP: GraphicaZ ModeZs and /mage Processing, Vol. 55, pp. 447-463 (1993).
10. R. Gordon and R. M. Rangayyan, "Feature enhancement of film mammograms using fixed and adaptive neighborhoods," AppZied Optics, Vol. 23, pp. 560-564 (1984).
11. A. Beghdadi and A. L. Negrate, "Contrast enhancement technique based on local detection of edges," Comput. Vision Grpahics /mage Process., Vol. 46, pp. 162-174 (1989).
12. A. F. Laine, "Multiscale wavelet representations for mammographic feature analysis," in Proceedings of the National Cancer Institute Breast Imaging Workshop: State-of-the-Art and New Technologies, Image Enhancement Techniques: Computer Science, Bethesda, MD (1991).
13. A. F. Laine and S. Song, "Multiscale wavelet representations for mammographic feature analysis," presented at Mathematical Methods in Medical Imaging, Proceedings of SPIE, San Diego, CA, Vol. 1768, pp. 306-316 (1992).
14. A. F. Laine and S. Song, "Wavelet processing techniques for digital mammography," in Proceedings of SPIE: Conference on Visualization in Biomedical Computing, Chapel Hill, NC , Vol. 1808, pp. 610-624 (1992).
15. A. Laine, S. Song, and J. Fan, "Adaptive Multiscale Processing for Contrast Enhancement," in Proceedings of SPIE: Conference on Biomedical Imaging and Biomedical Visualization, San Jose, CA, Vol. 1905, pp. 521-532 (1993).
16. B. D. Jawerth, M. L. Hilton, and T. L. Huntsberger, "Local enhancement of compressed images," J. MathematicaZ Imageing and Vision, Vol. 3, pp. 39-49 (1993).
17. A. F. Laine, S. Schuler, J. Fan, and W. Huda, "Mammographic feature enhancement by multiscale analysis," IEEE Transactions on MedicaZ Imaging, Vol. 13, pp. 725-740 (1994).
18. J. Lu and D. M. Healy Jr., "Contrast enhancement of medical images using mulitscale edge representation," in Proceedings of SPIE: Wavelet applications, Orlando, FL, Vol. 2242, pp. 711-719 (1994).
19. S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Transactions on Pattern AnaZysis and Machine InteZZigence, Vol. 11, pp. 674-693 (1989).
20. S. Mallat and S. Zhong, "Characterization of signals from multiscale edges," IEEE Transactions on Pattern AnaZysis and Machine InteZZigence, Vol. 14, pp. 710-732 (1992).
21. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. Pattern AnaZ. Machine InteZZ., Vol. 12, pp. 629-639 (1990).
22. S. Mallat and W. L. Hwang, "Singularity detection and processing with wavelets," IEEE Transactions on Information Theory, Vol. 38, pp. 617-643 (1992).
23. D. L. Donoho, "Nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data," Proc. Symposia AppZied Math., Vol. 47(1), pp. 173205 (1993).
24. H. Voorhees and T. Poggio, "Detecting textons and texture boundaries in natural images," presented at Proc. First International Conference on Computer Vision pp. 250258 (1987).
25. W. T. Freeman and E. H. Adelson, "The design and use of steerable filters," IEEE Transactions on Pattern AnaZysis and Machine InteZZigence, Vol. 13, pp. 891-906
26. P. Perona, "Deformable kernels for early vision," presented at Proc. IEEE Comput. Soc. Conf. Comput. Vision and Pattern Recogn., Maui (1991).
27. D. Shy and P. Perona, "X-Y separable pyramid steerable scalable kernels," presented at Proc. IEEE Comput. Soc. Conf. Comput. Vision and Pattern Recogn., Seattle, WA (1994).
28. E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, "Shiftable multiscale transforms," IEEE Transactions on In/ormation Theory, Vol. 38, pp. 587-607
29. J. Lim, Two-DimensionaZ SignaZ and Image Processing. Prentice-Hall, Englewood Cliff, NJ, 1990.
30. A. F. Laine, J. Fan, and S. Schuler, "A framework for contrast enhancement by dyadic wavelet analysis," in Second InternationaZ Workshop on DigitaZ Mammography, York, UK, July 10-12, 1994, pp. 91-100.
31. A. Cohen and I. Daubechies, "Wavelets on the interval and fast wavelet transforms," AppZied and ComputationaZ Harmonic AnaZysis, Vol. 1(1), pp. 54-81 (1993).
32. B. Jawerth and W. Sweldens, "An overview of wavelet based multiresolution analysis," SIAM Peview, Vol. 36, pp. 377-412 (1994).
33. L. S. Davis and A. Rosenfeld, "Noise cleaning by iterated local averaging," IEEE Transactions on Systems Man and Cybernetics, Vol. 8, pp. 705-710 (1978).
34. G. Deslauriers and S. Dubuc, "Symmetric iterative interpolating processes," Constructive Approximation, Vol. 5, pp. 49-68 (1989).
35. D. L. Donoho, "Smooth wavelet decomposition with blocky coefficient kernels," Pecent Advances in WaveZet AnaZysis, pp. 1-43. Academic Press, Boston, 1994.
36. S. Dubuc, "Interpolation through an iterative scheme," J. Math. AnaZ. AppZ., Vol. 114, pp. 185-204 (1986).
37. S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Transactions on Pattern Analysis and Machine inference, Vol. ll(7), pp. 674-693 (1989).
38. S. Mallat, "Multiresolution approximations and wavelet orthonormal bases ofL2(R)," Transactions o/t^e American MatfematicaZ Society, Vol. 315, pp. 69-87 (1989).
39. S. Mallat, "Multifrequency channel decompositions of images and wavelet models," IEEE Transactions on Acoustics, Speech, and SignaZ Processing, Vol. ASSP-37, pp. 2091-2110 (1989).
40. S. Mallat and S. Zhong, "Signal characterization from multiscale edges," presented at 10th International Conference on Pattern Recognition, Atlantic City, NJ, pp. 891-896 (1990).
41. W. M. Morrow, R. B. Paranjape, and R. M. Rangayyan, "Region-based contrast enhancement of mammograms," IEEE Transactions on MedicaZ imaging, Vol. 11, pp. 392406 (1992).
42. M. Nagao and T. Matsuyama, "Edge preserving smoothing," Computer Graphics and /mage Processing, Vol. 9, pp. 394-407 (1979).
43. A. Scheer, F. R. D. Velasco, and A. Rosenfeld, "Some new image smoothing techniques," IEEE Transactions on Systems Man and Cybernetics, Vol. 10, pp. 153-158 (1980).
44. P. G. Tahoces, J. Correa, M. Souto, and C. G. and, "Enhancement of chest and breast radiographs by automatic spatial filtering," IEEE Transactions on MedicaZ Imaging, Vol. 10, pp. 330-335 (1991).
45. Y. Xing, W. Huda, A. F. Laine, and J. Fan, "Simulated phantom images for optimizing wavelet-based image processing algorithms in mammography," presented at Mathematical Methods in Medical Imaging III, Proceedings of the SPIE, San Diego, CA, Vol. 2299, pp. 207-217 (1994).
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