Abstract
An initial evaluation of Haar wavelets is presented in this study for the compression of mammographic images. Fifteen mammograms with 105 μm/pixel resolution and varying dynamic range (10 and 12 bits per pixel) containing clustered microcalcifications were compressed with two different rates. The quality and content of the compressed reconstructed images was evaluated by an expert mammographer. The visualization of the cluster was on the average good but degraded with increasing compression because of the discontinuities introduced by these types of wavelets as the compression rate increases. However, the artifacts in the decoded images were seen as totally artificial and were not misinterpreted by the radiologist as calcifications. The classification of the parenchymal densities did not change significantly but the morphology of the calcifications was increasingly distorted as the compression rate increased leading to lower estimates of the suspiciousness of the cluster and higher uncertainties in the diagnosis. The uncompressed and two sets of compressed images were also processed by a wavelet method to extract the calcifications. Despite the fact that the segmentation algorithm generated several false-positive signals in highly compressed images, all true clusters were successfully segmented indicating that the compression process preserved the features of interest. Our preliminary results indicated that wavelets could be used to achieve high compression rates of mammographic images without losing small details such as microcalcification clusters as well as detect the calcifications from either the uncompressed or compressed reconstructed data. Further research and application of multiresolution analysis to digital mammography is continuing.
Article PDF
Similar content being viewed by others
References
Chan HP, Vyborny CJ, MacMahon J, et al: Digital mammographhy ROC studies of the effects of pixel size and unsharp-mask filtering on the detection of subtle calcifications. Invest Radiol 22:581–589, 1987
Davies DH, Dance DR: The automatic detection of subtle calcifications in high resolution, high quality mammograms. Phys Med Biol 37:1385–1390, 1992
Bassett LW, Gambhir S: Breast imaging for the 1990s. Semin Oncol 18:80–86, 1991
Wilson DL: Compression for radiological images. Proc SPIE Conference on Medical Imaging VI: PACS Design and Evaluation 1654:130–139, 1992
Kuduvalli GR, Rangayyan RM: Performance analysis of reversible image compression techniques for high-resolution digital teleradiology. IEEE Trans Med Imag 11:430–445, 1992
Lelewer D, Hirshberg D: Data compression. ACM Computing Surveys 19:261–296, 1991
Richardson WB: Wavelets packets applied to mammograms. Proc IS&T/SPIE Annual Symposium on Electronic Imaging. Science & Technology, San Jose, CA; January 31–February 5, 1993
Magnin IE, Baudin O, Baskurt A, et al: An adaptive coding algorithm for a mammogram image database. Proc IS&T/SPIE Annual Symposium on Electronic Imaging, Science & Technology, San Jose, CA; January 31–February 5, 1993
MacMahon H, Doi K, Sanada S, et al: Data compression: Effect of diagnostic accuracy in digital chest radiography. Radiology 178:175–179, 1991
Chen J, Flynn MJ: The effect of block size on image quality for compressed chest radiographs. Proc SPIE Conference on Image Capture, Formating and Display, 1653:252–260, 1992
DeVore R, Jawerth B, Lucier B: Image compression through wavelet transform coding. IEEE Trans Inf Theory 38:719–746, 1992
Qian W, Clarke LP, Kallergi M, et al: Tree-structured nonlinear filter and wavelet transform for microcalcification segmentation in mammography. Proc IS&T/SPIE Annual Symposium on nElectronic Imaging, Science & Technology, San Jose, CA; January 31–February 5, 1993
Clarke LP, Kallergi M, Qian W, et al: Tree-structured filter and wavelet transform for microcalcification segmentation in digital mammography. Cancer Letters, 1994 (in press)
Qian W, Clarke LP, Kallergi M, et al: Tree structured nonlinear filters in digital mammography. IEEE Trans Med Imag, 1993 (submitted)
Mallat S: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans Patt Anal Machine Intel 11:674–693, 1989
Mallat S: Multifrequency channel decompositions of images and wavelet models. IEEE Trans Acoust Speech Signal Proc 37:2091–2110, 1989
Author information
Authors and Affiliations
Additional information
Supported in part by the Office of Naval Research (Contracts N00014-91-J-1152 and N00014-91-J-1076), the Army High Performance Computing Research Center at the University of Minnesota, and the H. Lee Moffitt Cancer Center & Research Institute at the University of South Florida.
Rights and permissions
About this article
Cite this article
Lucier, B.J., Kallergi, M., Qian, W. et al. Wavelet compression and segmentation of digital mammograms. J Digit Imaging 7, 27–38 (1994). https://doi.org/10.1007/BF03168476
Issue Date:
DOI: https://doi.org/10.1007/BF03168476