Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regular grid. The high-level texture descriptor is build as a Bag-of-Features (BoF) of SIFT descriptors. Experimental results are provided showing the validity of the proposed approach for describing the tissues in breast US images. © 2014 Springer International Publishing.
CITATION STYLE
Massich, J., Meriaudeau, F., Sentís, M., Ganau, S., Pérez, E., Puig, D., … Martí, J. (2014). SIFT texture description for understanding breast ultrasound images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8539 LNCS, pp. 681–688). Springer Verlag. https://doi.org/10.1007/978-3-319-07887-8_94
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