Title :
Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Hierarchical Vector Quantization Scheme
Author :
Hao Han ; Lihong Li ; Fangfang Han ; Bowen Song ; Moore, William ; Zhengrong Liang
Author_Institution :
Dept. of Radiol., Stony Brook Univ., Stony Brook, NY, USA
Abstract :
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.
Keywords :
cancer; computerised tomography; image classification; image filtering; image segmentation; knowledge based systems; lung; medical image processing; support vector machines; vector quantisation; CADe system; INC detection; INC segmentation; chest volume; computed tomography scans; computer-aided detection; false-positive reduction; feature-based support vector machine classifier; hierarchical vector quantization scheme; high-level VQ; initial nodule candidates; juxtapleural nodule annotation; low-level VQ; lung cancer; online Lung Image Database Consortium database; pulmonary nodules; thoracic CT images; thresholding approach; Computed tomography; Feature extraction; Image segmentation; Lungs; Support vector machine classification; Three-dimensional displays; Vectors; Computer-aided detection (CADe); computed tomography (CT) imaging; false positive (FP) reduction; lung nodules; vector quantization (VQ);
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
DOI :
10.1109/JBHI.2014.2328870