Classification of kidney and liver tissue using ultrasound backscatter data

Fereshteh Aalamifar, Hassan Rivaz, Juan J. Cerrolaza, James Jago, Nabile Safdar, Emad M. Boctor, Marius G. Linguraru

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Ultrasound (US) tissue characterization provides valuable information for the initialization of automatic segmentation algorithms, and can further provide complementary information for diagnosis of pathologies. US tissue characterization is challenging due to the presence of various types of image artifacts and dependence on the sonographers skills. One way of overcoming this challenge is by characterizing images based on the distribution of the backscatter data derived from the interaction between US waves and tissue. The goal of this work is to classify liver versus kidney tissue in 3D volumetric US data using the distribution of backscatter US data recovered from end-user displayed Bmode image available in clinical systems. To this end, we first propose the computation of a large set of features based on the homodyned-K distribution of the speckle as well as the correlation coefficients between small patches in 3D images. We then utilize the random forests framework to select the most important features for classification. Experiments on in-vivo 3D US data from nine pediatric patients with hydronephrosis showed an average accuracy of 94% for the classification of liver and kidney tissues showing a good potential of this work to assist in the classification and segmentation of abdominal soft tissue.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2015
Subtitle of host publicationUltrasonic Imaging and Tomography
EditorsNeb Duric, Johan G. Bosch
ISBN (Electronic)9781628415094
StatePublished - 2015
EventMedical Imaging 2015: Ultrasonic Imaging and Tomography - Orlando, United States
Duration: Feb 22 2015Feb 23 2015

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2015: Ultrasonic Imaging and Tomography
Country/TerritoryUnited States


  • Tissue characterization
  • computer-aided diagnosis
  • kidney
  • liver
  • machine learning
  • ultrasound imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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