Comput. Biol. Med. - An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy.

Tópicos

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Resumo

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.

Resumo Limpo

computeraid detect diagnosi cad wide investig improv radiologist diagnost accuraci detect character lung diseas well assist process increas sizabl volum imag lung segment requisit preprocess step cad scheme paper propos parameterfre lung segment algorithm aim improv lung nodul detect accuraci focus juxtapleur nodul bidirect chain code method combin support vector machin svm classifi use select smooth lung border minim oversegment adjac region autom method test comput tomographi ct studi lung imag databas consortium lidc repres juxtapleur nodul approach obtain reinclus rate segment accuraci valid random select ct seri find averag oversegment ratio undersegment rate compar manual segment refer standard done expert

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