Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Abstract

We have been developing a computer-aided diagnostic (CAD) scheme for lung nodule detection in order to assist radiologists in the detection of lung cancer in thin-section computed tomography (CT) images. Our database consisted of 117 thin-section CT scans with 153 nodules, obtained from a lung cancer screening program at a Japanese university (85 scans, 91 nodules) and from clinical work at an American university (32 scans, 62 nodules). The database included nodules of different sizes (4-28 mm, mean 10.2 mm), shapes, and patterns (solid and ground-glass opacity (GGO)). Our CAD scheme consisted of modules for lung segmentation, selective nodule enhancement, initial nodule detection, feature extraction, and classification. The selective nodule enhancement filter was a key technique for significant enhancement of nodules and suppression of normal anatomic structures such as blood vessels, which are the main sources of false positives. Use of an automated rule-based classifier for reduction of false positives was another key technique; it resulted in a minimized overtraining effect and an improved classification performance. We employed a case-based four-fold cross-validation testing method for evaluation of the performance levels of our computerized detection scheme. Our CAD scheme achieved an overall sensitivity of 86% (small: 76%, medium-sized: 94%, large: 95%; solid: 86%, mixed GGO: 89%, pure GGO: 81%) with 6.6 false positives per scan; an overall sensitivity of 81% (small: 69%, medium-sized: 91%, large: 91%; solid: 79%, mixed GGO: 88%, pure GGO: 81%) with 3.3 false positives per scan; and an overall sensitivity of 75% (small: 60%, medium-sized: 88%, large: 87%; solid: 70%, mixed GGO: 87%, pure GGO: 81%) with 1.6 false positives per scan. The experimental results indicate that our CAD scheme with its two key techniques can achieve a relatively high performance for nodules presenting large variations in size, shape, and pattern.

Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging

“…On the other hand, the solid nodules have a very similar density to the underlying artificial broncho-vascular bundles, and are thus probably more difficult to detect. However, studies using CT acquisitions of real patients have also shown a higher detection rate for ground-glass nodules compared to solid nodules, for both human readers and CAD software [29,30]. This is an interesting point since small solid pulmonary nodules are less likely to be malignant than large solid nodules and ground-glass nodules [31], and thus, small solid nodules show a higher false-positive rate [32].…”

Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT

“…We finally classified the 2D nodule candidates into nodule and non-nodule based on the "optimal" features by use of a piecewise linear classifier. 17 If at least 17 (70% of 24) of the 24 2D nodule candidates of a 3D nodule candidate were classified as true nodules, the 3D nodule candidate was retained as a true nodule. Otherwise, it was discarded as a false positive.…”

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

“…The basic concept of CAD was proposed by The University of Chicago, in the mid-1980s, whose idea it was to provide a computer output as a "second opinion" to assist radiologists in interpreting images, so that the accuracy and consistency of radiological diagnosis could be improved, and also the image reading time could be reduced [7][8][9]. Since then, a number of researchers have developed computeraided diagnostic CAD schemes for detection and classification of various kinds of abnormalities such as microcalcifications and masses in mammograms [10][11][12][13], pulmonary nodules and diffuse lung diseases in CT [14][15][16][17][18], and colonic polyps in CT colonography [19][20][21][22]. Besides, the clinical usefulness of CAD schemes has been studied by performing the receiver operating characteristic (ROC) analysis in experiments similar to clinical situations [10,16,23,24].…”