Automatic detection of 2D and 3D lung nodules in chest spiral CT scans

Ayman El-Baz, Ahmed Elnakib, Mohamed Abou El-Ghar, Georgy Gimel'Farb, Robert Falk, Aly Farag

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.

Original languageEnglish (US)
Article number517632
JournalInternational Journal of Biomedical Imaging
Volume2013
DOIs
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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