Microcalcifications Detection System through Discrete Wavelet Analysis and Contrast Enhancement Techniques

This paper describes a method to detect microcalcifications in digital mammographic images using two-dimensional discrete wavelet transform and image enhancement techniques for removing noise as well as to obtain a better contrast. Calcifications are tiny deposits of calcium in breast tissues and they often represent important and common findings in a mammogram. The first step is to apply a segmentation process for eliminating some regions in the image, which are not useful for the mammographic interpretation. Then histogram modification technique is used to improve the contrast of the image and to clarify some details like microcalcifications. Finally DWT (discrete wavelet transform) must be applied for detecting the abnormality. Results were evaluated using the mammographic image analysis society (MIAS) mammographic databases.

[1]  Nico Karssemeijer,et al.  Noise equalization for detection of microcalcification clusters in direct digital mammogram images , 2004, IEEE Transactions on Medical Imaging.

[2]  Nico Karssemeijer,et al.  Thickness correction of mammographic images by means of a global parameter model of the compressed breast , 2004, IEEE Transactions on Medical Imaging.

[3]  S. Sehad,et al.  Artificial neural classification of clustered microcalcifications on digitized mammograms , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[4]  Wei Qian,et al.  Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography , 1993, Electronic Imaging.

[5]  Robin N. Strickland,et al.  Wavelet transforms for detecting microcalcifications in mammograms , 1996, IEEE Trans. Medical Imaging.

[6]  K Doi,et al.  An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms. , 1996, Academic radiology.

[7]  Dongming Zhao,et al.  Morphology on detection of calcifications in mammograms , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Heinz-Otto Peitgen,et al.  Scale-space signatures for the detection of clustered microcalcifications in digital mammograms , 1999, IEEE Transactions on Medical Imaging.

[9]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[10]  Jian Fan,et al.  Mammographic feature enhancement by multiscale analysis , 1994, IEEE Trans. Medical Imaging.

[11]  Mario Vento,et al.  Combining experts with different features for classifying clustered microcalcifications in mammograms , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  Hiroyuki Yoshida,et al.  Automated detection of clustered microcalcifications in digital mammograms using wavelet processing techniques , 1994, Medical Imaging.

[13]  Nicolaos B. Karayiannis,et al.  Detection of microcalcifications in digital mammograms using wavelets , 1998, IEEE Transactions on Medical Imaging.

[14]  Andrew F. Laine,et al.  Wavelets for contrast enhancement of digital mammography , 1995 .

[15]  K Doi,et al.  Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. , 1990, Investigative radiology.

[16]  Uday B. Desai,et al.  An unsupervised scheme for detection of microcalcifications on mammograms , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[17]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Nico Karssemeijer,et al.  Normalization of local contrast in mammograms , 2000, IEEE Transactions on Medical Imaging.