False positive reduction in lung GGO nodule detection with 3D volume shape descriptor

Ming Yang*, Senthil Periaswamy, Ying Wu

*Corresponding author for this work

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

9 Scopus citations

Abstract

Lung nodule detection, especially ground glass opacity (GGO) detection, in helical computed tomography (CT) images is a challenging Computer-Aided Detection (CAD) task due to the enormous variances in nodules' volumes, shapes, appearances, and the structures nearby. Most of the detection algorithms employ some efficient candidate generation (CG) algorithms to spot the suspicious volumes with high sensitivity at the cost of low specificity, e.g. tens even hundreds of false positives per volume. This paper proposes a learning based method to reduce the number of false positives given by CG based on a new general 3D volume shape descriptor. The 3D volume shape descriptor is constructed by concatenating spatial histograms of gradient orientations, which is robust to large, variabilities in intensity levels, shapes, and appearances. The proposed method achieves promising performance on a difficult mixture lung nodule dataset with average 8.1% detection rate and 4.3 false positives per volume.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesI437-I440
DOIs
StatePublished - Aug 6 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Computer aided analysis
  • Computer vision
  • Lung nodule detection
  • Medical imaging
  • Shape descriptor

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'False positive reduction in lung GGO nodule detection with 3D volume shape descriptor'. Together they form a unique fingerprint.

  • Cite this

    Yang, M., Periaswamy, S., & Wu, Y. (2007). False positive reduction in lung GGO nodule detection with 3D volume shape descriptor. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 (pp. I437-I440). [4217110] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 1). https://doi.org/10.1109/ICASSP.2007.366710