Title | ||
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Improvement of mammographic lesion detection by fusion of information from different views |
Abstract | ||
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In screening mammography, two standard views, craniocaudal (CC) and medio-lateral oblique (MLO), are commonly taken, and radiologists use information from the two views for lesion detection and diagnosis. Current computer-aided diagnosis (CAD) systems are designed to detect lesions on each view separately. We are developing a CAD method that utilizes information from the two views to reduce false-positives (FPs). Our two-view detection scheme consists of two main stages, a one-view pre-screening stage and a two-view correspondence stage. The one-view and two-view scores are then fused to estimate the likelihood that an object is a true mass. In this study, we analyzed the effectiveness of the proposed fusion scheme for FP reduction and its dependence on the number of objects per image in the pre-screening stage. The preliminary results demonstrate that the fusion of information from the CC and MLO views significantly reduced the FP rate in comparison to the one-view scheme. When the pre-screening stage produced 10 objects per image, the two-view fusion technique reduced the FP rate from an average of 2.1 FPs/image in our current one-view CAD scheme to 1.2 FPs/image at a sensitivity of 80%. The results also indicate that the improvement in the detection accuracy was essentially independent of the number of initial objects per image obtained at the pre-screening stage for this data set. |
Year | DOI | Venue |
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2001 | 10.1117/12.431080 | Proceedings of SPIE |
Keywords | DocType | Volume |
computer-aided diagnosis,masses detection,classification,fusion | Conference | 4322 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
1 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sophie Paquerault | 1 | 10 | 4.25 |
Nicholas Petrick | 2 | 209 | 42.63 |
Heang-Ping Chan | 3 | 408 | 93.38 |
Berkman Sahiner | 4 | 224 | 66.72 |