Paper
3 July 2001 Computerized lung nodule detection on thoracic CT images: combined rule-based and statistical classifier for false-positive reduction
Author Affiliations +
Abstract
We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified and suspicious structures are segmented. These structures may include true lung nodules or normal structures that consist mainly of vascular structures. We have designed rule-based classifiers to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We have performed a preliminary study using CT images from 17 patients with 31 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (26/31) with 2.53 (1549/612) FP objects per slice. When the LDA followed the rule-based classifier, the number of FP objects per slice decreased to 1.75 (1072/612) at the same sensitivity. These preliminary results demonstrate the feasibility of our approach for nodule detection and FP reduction on CT images. The inclusion of rule-based classification leads to an improvement in detection accuracy for the CAD system.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Metin Nafi Gurcan, Nicholas Petrick, Berkman Sahiner, Heang-Ping Chan, Philip N. Cascade, Ella A. Kazerooni, and Lubomir M. Hadjiiski "Computerized lung nodule detection on thoracic CT images: combined rule-based and statistical classifier for false-positive reduction", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431145
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications and 4 patents.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Lung

Computed tomography

Image segmentation

Computer aided diagnosis and therapy

CAD systems

Lung cancer

Computing systems

Back to Top