Bone texture characterization with fisher encoding of local descriptors

Yang Song, Weidong Cai, Fan Zhang, Heng Huang, Yun Zhou, David Dagan Feng

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

Abstract

Bone texture characterization is important for differentiating osteoporotic and healthy subjects. Automated classification is however very challenging due to the high degree of visual similarity between the two types of images. In this paper, we propose to describe the bone textures by extracting dense sets of local descriptors and encoding them with the improved Fisher vector (IFV). Compared to the standard bag-of-visual-words (BoW) model, Fisher encoding is more discriminative by representing the distribution of local descriptors in addition to the occurrence frequencies. Our method is evaluated on the ISBI 2014 challenge dataset of bone texture characterization, and we demonstrate excellent classification performance compared to the challenge entries and large improvement over the BoW model.

Original languageEnglish (US)
Title of host publicationProceedings - International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages5-8
Number of pages4
Volume2015-July
ISBN (Print)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Keywords

  • Bone texture
  • classification
  • feature encoding
  • Fisher vector

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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