6533b825fe1ef96bd1281d7f

RESEARCH PRODUCT

SIFT Texture Description for Understanding Breast Ultrasound Images

Joan MartíJoan MassichJoan MassichSergi GanauMelcior SentísRobert MartíElsa PérezFabrice MeriaudeauDomenec PuigArnau Oliver

subject

medicine.diagnostic_testFeature transformbusiness.industryTexture DescriptorInformationSystems_INFORMATIONSTORAGEANDRETRIEVALComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transformPattern recognitionTexture (geology)ComputingMethodologies_PATTERNRECOGNITIONmedicineDegree of similarityComputer visionArtificial intelligencebusinessBreast ultrasoundMathematics

description

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.

yearjournalcountryeditionlanguage
2014-01-01
https://doi.org/10.1007/978-3-319-07887-8_94