Paper Info
Title
Recognition of lesion correspondence on two mammographic views: a new method of false-positive reduction for computerized mass detection
Abstract
We used the correspondence of detected structures on two views of the same breast for false-positive (FP) reduction in computerized detection of mammographic masses. For each initially detected object on one view, we considered all possible pairings with objects on the other view that fell within a radial band defined by the nipple-to-object distances. We designed a "correspondence classifier" to classify these pairs as either the same mass (a TP-TP pair) or a mismatch (a TP-FP, FP-TP or FP-FP pair). For each pair, similarity measures of morphological and texture features were derived and used as input features in the correspondence classifier. Two-view mammograms from 94 cases were used as a preliminary data set. Initial detection provided 6.3 FPs/image at 96% sensitivity. Further FP reduction in single view resulted in 1.9 FPs/image at 80% sensitivity and 1.1 FPs/image at 70% sensitivity. By combining single-view detection with the correspondence classifier, detection accuracy improved to 1.5 FPs/image at 80% sensitivity and 0.7 FPs/image at 70% sensitivity. Our preliminary results indicate that the correspondence of geometric, morphological, and textural features of a mass on two different views provides valuable additional information for reducing FPs.
Year
DOI
Venue
2001
10.1117/12.431139
Proceedings of SPIE
Keywords
Field
DocType
mammography,computer-aided diagnosis,breast masses,detection
Mammography,Computer vision,Computer-aided diagnosis,Artificial intelligence,Classifier (linguistics),Mathematics
Conference
Volume
ISSN
Citations 
4322
0277-786X
1
PageRank 
References 
Authors
0.37
0
6
Name
Order
Citations
PageRank
Berkman Sahiner122466.72
Nicholas Petrick220942.63
Heang-Ping Chan340893.38
Sophie Paquerault4104.25
Mark A. Helvie511427.11
Lubomir M Hadjiiski616251.43