title: Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor.
abstract: Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan.
authors.affiliation: Gwangju Institute of Science and Technology (GIST), School of Information and Mechatronics, 123 Cheomdan-gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea(1). Electronic address: taznux@gist.ac.kr.
authors.forename: Wook-Jin
authors.initials: WJ
authors.lastname: Choi
authors.forename: Tae-Sun
authors.initials: TS
authors.lastname: Choi
mesh_terms: D000465:Algorithms; D001331:Automation; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed
keywords: CAD; CT; Feature extraction; Pulmonary nodule detection