This paper presents an experimental "morphological analysis" retrieval system for mammograms, using Relevance-Feedback techniques. The features adopted are first-order statistics of the Normalized Radial Distance, extracted from the annotated mass boundary. The system is evaluated on an extensive dataset of 2274 masses of the DDSM database, which involves 7 distinct classes. The experiments verify that the involvement of the radiologist as part of the retrieval process improves the results, even for such a hard classification task, reaching the precision rate of almost 90%. Therefore, Relevance-Feedback can be employed as a very useful complementary tool to a Computer Aided Diagnosis system. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Tzikopoulos, S. D., Georgiou, H. V., Mavroforakis, M. E., & Theodoridis, S. (2010). Shape-based tumor retrieval in mammograms using relevance-feedback techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6352 LNCS, pp. 251–260). https://doi.org/10.1007/978-3-642-15819-3_33
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