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Edge Map Analysis in Chest X-rays for Automatic Pulmonary Abnormality Screening

Wednesday, September 16, 2015 — Poster Session I

3:30 p.m. – 5:00 p.m.
FAES Terrace
NLM
COMPBIO-14

Authors

  • S Kc
  • S Vajda
  • S Antani
  • GR Thoma

Abstract

We present a novel method for automatic screening pulmonary abnormalities using thoracic edge map in posteroanterior chest radiograph (CXR) images. Our particular motivator is the need for screening HIV+ populations in resource constrained regions for Tuberculosis (TB). The proposed method is motivated by the observation that abnormal CXRs tend to exhibit corrupted and/or deformed thoracic edge maps. We study histograms of thoracic edges for all possible orientations of gradients in the range [0,2π) at different numbers of bins and different pyramid levels. We have used two CXR benchmark collections made available by the U.S. National Library of Medicine, and have achieved a maximum abnormality detection accuracy (ACC) of 86.36% and area under the ROC curve (AUC) of 0.93 at one second per image, on average, which outperforms the reported state-of-the-art.

Category: Computational Biology