• Progress in Medical Physics
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• v.23 no.4
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• pp.261-268
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• 2012

Breast cancer is the second leading cause of women cancer death in Korea. The key for reducing disease mortality is early detection. Although digital mammography (DM) has been credited as one of the major reasons for the early detection to decrease in breast cancer mortality observed in the last 20 years, DM is far from perfect for several limitations. Digital breast tomosynthesis (DBT) is expected to overcome some inherent limitations of conventional mammography caused by overlapping of normal tissue and pathological tissue during the standard 2D projections for the improved lesion margin visibility and early breast cancer detection. In this study, we compared a DM system and DBT system acquired with different thickness of breast phantom. We acquired breast phantom data with same average glandular dose (AGD) from 1 mGy to 4 mGy under same experimental condition. The contrast, micro-calcification measurement accuracy and observer study were conducted with breast phantom images. As a result, the higher accuracy of lesion detection with DBT system compared to DM system was demonstrated in this study. Furthermore, the pain of patients caused by severe compression can be reduced with DBT system. In conclusion, the results indicated that DBT system play an important role in breast cancer detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.374-386
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• 2004

The mammogram provides the way to observe detailed internal organization of breasts to radiologists for the early detection. This paper is mainly focused on efficiently detecting the Microcalcification's Region Of Interest(ROI)s. Breast cancers can be caused from either microcalcifications or masses. Microcalcifications are appeared in a digital mammogram as tiny dots that have a little higher gray levels than their surrounding pixels. We can roughly determine the area which possibly contain microcalifications. In general, it is very challenging to find all the microcalcifications in a digital mammogram, because they are similar to some tissue parts of a breast. To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks. The filtering process with boundary conditions removes easily-distinguishable tissues while keeping all microcalcifications so that it cleans the thresholded mammogram images and speeds up the later processing by the average of 86％. The first neural network shows the average of 96.66％ recognition rate. The second neural network performs better by showing the average recognition rate 98.26％. By removing all tissues while keeping microcalcifications as much as possible, the next parts of a CAD system for detecting breast cancers can become much simpler.