Digital Mammography

1. Front Matter

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2. Breast Density

   1. A New Step-Wedge for the Volumetric Measurement of Mammographic Density

      • Jennifer Diffey, Alan Hufton, Susan Astley

      Pages 1-9

   2. Assessing Ground Truth of Glandular Tissue

      • Christina Olsén, Fredrik Georgsson

      Pages 10-17

   3. Volumetric Breast Density Estimation on Mammograms Using Breast Tissue Equivalent Phantoms – An Update

      • Bindu J. Augustine, Gordon E. Mawdsley, Norman F. Boyd, Martin J. Yaffe

      Pages 18-25

   4. An Alternative Approach to Measuring Volumetric Mammographic Breast Density

      • Christopher Tromans, Michael Brady

      Pages 26-33

   5. Breast Density Dependent Computer Aided Detection

      • Styliani Petroudi, Michael Brady

      Pages 34-38

   6. Evaluation of Effects of HRT on Breast Density

      • Styliani Petroudi, Kostantinos Marias, Michael Brady

      Pages 39-45

3. CAD

   1. Modeling the Effect of Computer-Aided Detection on the Sensitivity of Screening Mammography

      • Robert M. Nishikawa

      Pages 46-53

   2. Use of Prompt Magnitude in Computer Aided Detection of Masses in Mammograms

      • Nico Karssemeijer

      Pages 54-60

   3. Current Screening Practice: Implications for the Introduction of CAD

      • Lucy Tomlinson, Nathalie Hurley, Caroline Boggis, Julie Morris, Emma Hurley, Sue Astley

      Pages 61-67

   4. Mammographic Mass Detection Using Unsupervised Clustering in Synergy with a Parcimonious Supervised Rule-Based Classifier

      • Michel Bruynooghe

      Pages 68-75

   5. Computerized Classification Can Reduce Unnecessary Biopsies in BI-RADS Category 4A Lesions

      • Leichter Isaac, Lederman Richard, Buchbinder Shalom, Srour Yossi, Bamberger Philippe, Sperber Fanny

      Pages 76-83

   6. Addressing Image Variability While Learning Classifiers for Detecting Clusters of Micro-calcifications

      • Glenn Fung, Balaji Krishnapuram, Nicolas Merlet, Eli Ratner, Philippe Bamberger, Jonathan Stoeckel et al.

      Pages 84-91

   7. Computer-Aided Detection of Breast Cancer Using an Ultra High-Resolution Liquid Crystal Display: Reading Session Analysis

      • Yoshifumi Kuroki, Shigeru Nawano, Hidefumi Kobatake, Nachiko Uchiyama, Kazuo Shimura, Kouji Matano

      Pages 92-96

4. Clinical Practice

   1. Mammography Reading with Computer-Aided Detection (CAD): Performance of Different Readers

      • Susan M. Astley, Stephen W. Duffy, Caroline R. M. Boggis, Mary Wilson, Nicky B. Barr, Ursula M. Beetles et al.

      Pages 97-104

   2. The Impact of Integration of Computer-Aided Detection and Human Observers

      • Nachiko Uchiyama, Noriyuki Moriyama, Takayuki Yamada, Noriaki Ohuchi

      Pages 105-110

   3. Improving Access to Mammography in Rural Areas

      • Elizabeth A. Krupinski

      Pages 111-117

   4. Dual Modality Surgical Guidance for Non-palpable Breast Lesions

      • Patricia Goodale Judy, Priya Raghunathan, Mark B. Williams

      Pages 118-124

   5. Mammography Reading with Computer-Aided Detection (CAD): Single View vs Two Views

      • Olorunsola F. Agbaje, Susan M. Astley, Maureen G. C. Gillan, Caroline R. M. Boggis, Mary Wilson, Nicky B. Barr et al.

      Pages 125-130

   6. Automated Breast Tissue Measurement of Women at Increased Risk of Breast Cancer

      • H. G. Patel, S. M. Astley, A. P. Hufton, M. Harvie, K. Hagan, T. E. Marchant et al.

      Pages 131-136

This volume of Springer’s Lecture Notes in Computer Science series records th the proceedings of the 8 International Workshop on Digital Mammography (IWDM), which was held in Manchester, UK, June 18–21, 2006. The meetings bringtogetheradiversesetofresearchers(physicists,mathematicians,computer scientists, engineers), clinicians (radiologists, surgeons) and representatives of industry, who are jointly committed to developing technology, not just for its ownsake,but to supportclinicians inthe earlydetection andsubsequentpatient management of breast cancer. The conference series was initiated at a 1993 meeting of the SPIE in San Jose, with subsequent meetings hosted every two years by researchers around the world. Previous meetings were held in York, Chicago, Nijmegen, Toronto, Bremen, and North Carolina. It is interesting to re?ect on the changes that have occurred during the past 13 years. Then, the dominant technology was ?lm-screen mammography; now it is full-?eld digital mammography. Then, there were few screening programmes world-wide; now there are many. Then, there was the hope that computer-aided detection (CAD) of early signs of cancer might be possible; now CAD is not only a reality but (more importantly) a commercially led clinical reality. Then, algorithmswerealmostentirelyheuristicwithlittleclinicalsupport;nowthereis arequirementforsubstantialclinicalsupportforanyalgorithmthatisdeveloped and published. However, upon re?ection, could we have predicted with absolute certainty what would be the key questions to be addressed over the subsequent (say) six years? No! That is the nature, joy, and frustration of research. There are more blind alleys to explore than there are rich veins that bring gold (in all senses of that analogy!).

• CAD
• Performance
• Textur
• algorithms
• biomedical image processing
• biomedical modeling
• breast cancer
• classification
• computed tomography (CT)
• digital tomosynthesis
• image analysis
• image compression
• image registration
• learning
• proving

• Division of Imaging Science and Biomedical Engineering, Stopford Building, University of Manchester, Manchester

  Susan M. Astley

• Wolfson Medical Vision Lab, University of Oxford, UK

  Michael Brady

• Imaging Science and Biomedical Engineeering, University of Manchester, Manchester, UK

  Chris Rose

• Department of Computer Science, University of Wales, Aberystwyth, UK

  Reyer Zwiggelaar

Policies and ethics