Abstract
Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities. We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure. Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms. For spicule/non-spicule classification in real mammograms we report an area Az = 0.764 under the receiver operating characteristic. © 2010 Springer-Verlag.
Original language | English |
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Pages (from-to) | 153-160 |
Number of pages | 7 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 6136 |
DOIs | |
Publication status | Published - 2010 |
Keywords
- classification
- dual-tree complex wavelet
- linear structures
- mammography
- random forests