ABSTRACT The proposed algorithm introduces a novel vague set approach to develop a selective but robust, flexible and intelligent contrast enhancement technique for mammograms. Wavelet based filtering analysis can produce Low Frequency (LF) and High Frequency (HF) subbands of the original input images. The extremely small size microcalcifications become visible under multiresolution techniques. LF subband is then fuzzified by conventional fuzzy c-means clustering (FCM) algorithm with justified number of clusters. HF components, representing the narrow protrusions and other fine details are also fuzzified by FCM with justified number of clusters. Vague set approach captures the hesitancies and uncertainties of truly affected masses/other breast abnormalities with normal glandular tissues. After highlighting the masses/microcalcifications accurately, both LF and HF subbands are transformed back to the original resolution by inverse wavelet transform. The results show that the proposed method can successfully enhance the selected regions of mammograms and provide better contrast images for visual interpretation.
Cite this paper
nullDas, A. and Bhattacharya, M. (2009) A novel vague set approach for selective contrast enhancement of mammograms using multiresolution. Journal of Biomedical Science and Engineering, 2, 575-581. doi: 10.4236/jbise.2009.28083.
 A. G. Haus and M. J. Yaffe, Eds (1993) A categorical course in physics, technical aspects of breast imaging, radiological society of North America, Presented at the 79 Scientific Assembly and Annual Meeting of RSNA.
G. T. Barnes and G. D. Frey, Eds (1991) Screen film mammography, imaging considerations and medical physics responsibilities, Madison, WI: Medical Physics Publishing.
R. C. Gonzalez and R. Woods, Digital image processing, Second Edition, Pearson Education.
D-Y. Tsai, and Y. Lee, (2004) Improved adaptive neigh- borhood pre-processing for medical image enhancement, LNCS-3314, 576–581.
P. F. Stetson, F. G. Sommer, and A. Macovski, (1997) Lesion contrast enhancement in medical ultrasound imaging, IEEE Trans. Medical Imaging, 16(4), 416–425.
D-C. Chang and W-R Wu, (1998) Image contrast enhan- cement based on a histogram transformation of local standard deviation, IEEE Trans. Medical Imaging, 17(4), 518–531.
A. F. Laine, S. Schuler, J. Fan, and W. Huda, (1994) Mammographic feature enhancement by multiscale analysis, IEEE Trans. Medical Imaging, 13(4), 725–740.
S. Lai, X. Li, and W. F. Bischof, (1989) On techniques for detecting circumscribed masses in mammograms, IEEE Trans. Med. Imag., 8(4), 377–386.
M. Nagao and T. Matsuyama, (1979) Edge preserving smoothing, computer graphics and image processing, 9 (4), 394–407.
A. Scheer, F. R. D. Velasco, and A. Rosenfield, (1980) Some new image smoothing techniques, IEEE Trans. Syst., Man, Cyber., SMC-IO, 3, 153–158.
L. S. Davis and A. Rosenfield, (1978) Noise cleaning by iterated local averaging, IEEE Trans. Syst., Man, Cyber., SMC, 8, 705–710.
A. C. Bovik, T. S. Huang, and D. C. Munson, Jr., (1987) The effect of median filtering on edge estimation and detection, IEEE Trans. Pattern Anal. Machine Intell., PAMI, 9(2), 181–194.
S. K. Pal and R. A. King, (1981) Image enhancement using smoothing with fuzzy set, IEEE Trans. Syst., Man, Cybern., SMC, 11, 494–501.
H. D. Cheng and H. Xu, (2002) A novel fuzzy logic approach to mammogram contrast enhancement, An Int. Journal on Information Sciences-Applications, 148(1-4), 167–184.
S. K. Pal, (1982) A note on the quantitative measure of image enhancement through fuzziness, IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI, 4(2).
C. Popa, A. Vlaicu, M. Gordan and B. Orza, (2007) Fuzzy contrast enhancement for images in the compressed domain, Proc. of the Int. Multiconference on Computer Science and Information Technology, 161– 170.
L. A. Zadeh, (1965) Fuzzy sets, information and control, 8, 338–353.
W. L. Gau, D. J. Buehrer, (1993) Vague sets, IEEE Trans. Systems, Man, and Cybernetics, 23, 610–614.