IEEE J Biomed Health Inform - Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme.

Tópicos

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Resumo

Computer-aided detection (CADe) of pulmonary nodules is critical to assisting radiologists in early identification of lung cancer from computed tomography (CT) scans. This paper proposes a novel CADe system based on a hierarchical vector quantization (VQ) scheme. Compared with the commonly-used simple thresholding approach, the high-level VQ yields a more accurate segmentation of the lungs from the chest volume. In identifying initial nodule candidates (INCs) within the lungs, the low-level VQ proves to be effective for INCs detection and segmentation, as well as computationally efficient compared to existing approaches. False-positive (FP) reduction is conducted via rule-based filtering operations in combination with a feature-based support vector machine classifier. The proposed system was validated on 205 patient cases from the publically available online Lung Image Database Consortium database, with each case having at least one juxta-pleural nodule annotation. Experimental results demonstrated that our CADe system obtained an overall sensitivity of 82.7% at a specificity of 4 FPs/scan. Especially for the performance on juxta-pleural nodules, we observed 89.2% sensitivity at 4.14 FPs/scan. With respect to comparable CADe systems, the proposed system shows outperformance and demonstrates its potential for fast and adaptive detection of pulmonary nodules via CT imaging.

Resumo Limpo

computeraid detect cade pulmonari nodul critic assist radiologist earli identif lung cancer comput tomographi ct scan paper propos novel cade system base hierarch vector quantiz vq scheme compar commonlyus simpl threshold approach highlevel vq yield accur segment lung chest volum identifi initi nodul candid inc within lung lowlevel vq prove effect inc detect segment well comput effici compar exist approach falseposit fp reduct conduct via rulebas filter oper combin featurebas support vector machin classifi propos system valid patient case public avail onlin lung imag databas consortium databas case least one juxtapleur nodul annot experiment result demonstr cade system obtain overal sensit specif fpsscan especi perform juxtapleur nodul observ sensit fpsscan respect compar cade system propos system show outperform demonstr potenti fast adapt detect pulmonari nodul via ct imag

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