title: An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy.
abstract: Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists׳ diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the lung imaging database consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on 10 randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert.
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA, USA; Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA. Electronic address: shiwenshen@ucla.edu.
authors.forename: Shiwen
authors.initials: S
authors.lastname: Shen
authors.affiliation: Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
authors.forename: Alex A T
authors.initials: AA
authors.lastname: Bui
authors.affiliation: Department of Computer Science, University of California, Los Angeles, CA, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Cong
authors.affiliation: Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
authors.forename: William
authors.initials: W
authors.lastname: Hsu
mesh_terms: D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D060388:Support Vector Machine
keywords: Chain code; Computer aided diagnosis; Juxtapleural nodule; Lung segmentation; Support vector machine