Reduction of lymph tissue false positives in pulmonary embolism detection

Bernard Ghanem, Jianming Liang, Jinbo Bi, Marcos Salganicoff, Arun Krishnan

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

2 Scopus citations


Pulmonary embolism (PE) is a serious medical condition, characterized by the partial/complete blockage of an artery within the lungs. We have previously developed a fast yet effective approach for computer aided detection of PE in computed topographic pulmonary angiography (CTPA),1 which is capable of detecting both acute and chronic PEs, achieving a benchmark performance of 78% sensitivity at 4 false positives (FPs) per volume. By reviewing the FPs generated by this system, we found the most dominant type of FP, roughly one third of all FPs, to be lymph/connective tissue. In this paper, we propose a novel approach that specifically aims at reducing this FP type. Our idea is to explicitly exploit the anatomical context configuration of PE and lymph tissue in the lungs: a lymph FP connects to the airway and is located outside the artery, while a true PE should not connect to the airway and must be inside the artery. To realize this idea, given a detected candidate (i.e. a cluster of suspicious voxels), we compute a set of contextual features, including its distance to the airway based on local distance transform and its relative position to the artery based on fast tensor voting and Hessian "vesselness" scores. Our tests on unseen cases show that these features can reduce the lymph FPs by 59%, while improving the overall sensitivity by 3.4%.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2008 - Computer-Aided Diagnosis
StatePublished - 2008
Externally publishedYes
EventMedical Imaging 2008 - Computer-Aided Diagnosis - San Diego, CA, United States
Duration: Feb 19 2008Feb 21 2008

Publication series

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


OtherMedical Imaging 2008 - Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego, CA


  • Classification and classifier design
  • Detection
  • Feature extraction
  • Multiple instance learning
  • Segmentation
  • Tensor voting
  • X-ray CT

ASJC Scopus subject areas

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


Dive into the research topics of 'Reduction of lymph tissue false positives in pulmonary embolism detection'. Together they form a unique fingerprint.

Cite this