A hybrid approach to segmentation of diseased lung lobes.
Where this comes from
- Record sourced from PubMed, PMID 24968339.
- Also identified by DOI 10.1109/JBHI.2014.2332955.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Complete segmentation of diseased lung lobes by automatically identifying fissure surfaces is a nontrivial task, due to incomplete, disrupted, and deformed fissures. In this paper, we present a novel algorithm employing a hybrid two-dimensional/three-dimensional approach for segmenting diseased lung lobes. Our approach models complete fissure surfaces from partial fissures found in individual computed tomography (CT) images. Evaluated using 24 patients' lungs with a variety of different diseases, our algorithm produced root-mean square errors of 2.21 ± 1.21, 2.51 ± 1.36, and 2.38 ± 1.27 mm for segmenting the left oblique fissure (LOF), right oblique fissure (ROF) and right horizontal fissure (RHF), respectively. The average accuracies for segmenting the LOF, ROF, and RHF are 86.59%, 84.80%, and 82.62%, using our ±3-mm percentile measure. These results indicate the feasibility of developing an automatic algorithm for complete segmentation of diseased lung lobes.
Medical subject headings
- Image Processing, Computer-Assisted
- Lung
- Lung Diseases
- Tomography, X-Ray Computed