Cascaded regression for CT slice localization

Olga C. Avila-Montes, Uday Kurkure, Ryo Nakazato, Daniel S. Berman, Damini Dey, Ioannis A. Kakadiaris

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

1 Scopus citations

Abstract

Automated computational tools are needed to estimate the position of a slice of interest within a contiguous stack of slices. Such estimation is useful to retrieve relevant slices from a volume of slices in clinical analysis or it can be used as an initialization step to other post-processing and image analysis techniques. In this paper, we present a novel method to determine the location of a slice of interest within a given volume by formulating it as a regression problem. The input variables for the regression are obtained from simple intensity features computed from a pyramid representation of the slice. We assess the performance of the proposed method by comparing the estimated positions of slices of interest in CT data with manual annotations. Our method was validated on a dataset of 45 volumes and promising results were obtained for 5 different target slices, the average error being 2 slices.

Original languageEnglish (US)
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages1881-1884
Number of pages4
DOIs
StatePublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: Mar 30 2011Apr 2 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/TerritoryUnited States
CityChicago, IL
Period3/30/114/2/11

Keywords

  • image retrieval
  • non-contrast CT
  • regression

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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