Table of Contents Owing to space limitations, only a selection of the published papers are listed hereunder.
Preface. Classification and detection of microcalcifications. Dense feature maps for detection of calcifications (W.P. Kegelmeyer, Jr., M.C. Allmen). Three-dimensional reconstruction of microcalcification clusters within excised breast lesions (S.P. Bates et al.). Detection of microcalcifications using wavelets (R.N. Strickland, H.I. Hahn). Use of multiple images/wavelets. A framework for contrast enhancement by dyadic wavelet analysis (A. Laine, J. Fan, S. Schuler). Registering time sequences of mammograms using a two-dimensional image unwarping technique (M. Sallam, K.W. Bowyer). Mammogram analysis by comparison with previous screenings (D. Brzakovic et al.). Image acquisition and transmission. Developments in digital mammography with monochromatic and narrow energy band X-rays (R. Beccherle et al.). Comparison of digital X-ray cameras for stereotactic breast needle biopsy: an observer performance study (E.A. Krupinski, H. Roehrig). Classification and detection of masses. Recognition of stellate lesions in digital mammograms (N. Karssemeijer). The detection of abnormalities in mammograms (S.L. Kok, J.M. Brady, L. Tarassenko). Computer detection of lesions missed by mammography (R.A. Schmidt et al.). Image processing and segmentation. Enriching digital mammogram image analysis with a description of the curvi-linear structures (N. Cerneaz, M. Brady). Generating ROC curves for artificial neural networks (K.S. Woods, K.W. Bowyer). A novel approach to aligning mammograms (E.A. Stamatakis et al.).Image databases. A training and assessment package for digital mammography (H.J. Sumsion, G.J.S. Parkin, A.R. Cowen). The application of a computed radiography database to a mammographic reporting environment (H.J. Sumsion et al.). Radiologist performance. Prompting as an aid to diagnosis in mammography (I.W. Hutt, S.M. Astley, C.R.M. Boggis). To err is human, to compute divine? (C.J. Savage et al.). Index of authors.