A novel computer-aided lung nodule detection system for CT images

Tan, Maxine; Deklerck, Rudi; Jansen, Bart; Bister, Michel; Cornelis, Jan

doi:10.1118/1.3633941

Cited by 206 publications

(139 citation statements)

References 62 publications

Supporting

Mentioning

132

Contrasting

Unclassified

Order By: Relevance

“…Other investigators have also presented similar results, such as, Riccardi et al [14] have recently presented a sensitivity of 71% with 6.5 FP/patient, and Tan et al [15] who reported 87.5% sensitivity at 4 FP detections per scan.…”

Section: Discussionsupporting

confidence: 63%

“…It has been shown that CAD for lung nodule detection could increase radiologists' performance [5][6][7] . Different approaches have been proposed for automatic detection of lung cancer on CT around the world: while some have evaluated different marketed CAD systems, other investigators proposed other methods and processing techniques [5][6][7][8][9][10][11][12][13][14][15] .…”

mentioning

confidence: 99%

See 1 more Smart Citation

Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

Bayarri

Suárez-Cuenca²,

Tahoces³

et al. 2012

JBGC

View full text Add to dashboard Cite

The purpose of this study was to evaluate the diagnostic performance of a computer-aided diagnosis (CAD) system, on the detection of pulmonary nodules in multidetector row computed tomography (MDCT) images, by using two different MDCT scanners. The computerized scheme was based on the iris filter. We have collected CT cases of patients with pulmonary nodules. We have included in the study one hundred and thirty-two calcified and noncalcified nodules, measuring 4-30 mm in diameter. CT examinations were performed by using two different equipments: a CT scanner (SOMATOM Emotion 6), and a dual-source computed tomography system (SOMATOM Definition) (Siemens Medical System, Forchheim, Germany), with the following parameters: collimation, 6x1.0mm (Emotion 6); and 64×0.6mm (Definition); 100-130 kV; 70-110 mAs. Data were reconstructed with a slice thickness of 1.25mm (Emotion 6) and 1mm (Definition). True positive cases were determined by an independent interpretation of the study by three experienced chest radiologists, the panel decision being used as the reference standard. Free-response Receiver Operating Characteristic curves, sensitivity and number of false-positive per scan, were calculated. Our CAD scheme, for the test set of the study, yielded a sensitivity of 80%, with an average of 5.2 FPs per examination. At an average false positive rate of 9 per scan, our CAD scheme achieved sensitivities of 94% for all nodules, 94.5% for solid, 80% for non-solid, 84% for spiculated, and 97% for non-spiculated nodules. These encouraging results suggest that our CAD system, advocated as a second reader, may help radiologists in the detection of lung nodules in MDCT.

show abstract

Section: Discussionsupporting

confidence: 63%

mentioning

confidence: 99%

Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

Bayarri

Suárez-Cuenca²,

Tahoces³

et al. 2012

JBGC

View full text Add to dashboard Cite

show abstract

“…Dehmeshki et al [18] enhanced this method www.ijacsa.thesai.org by adding a shape-based methodology for nodule detection from spherical elements. Tan et al [19] utilized the three classifiers such as artificial neural network and genetic algorithm for lung nodule detection. To validate the proposed model, results were compared with SVM and fixed-topology neural networks based models.…”

Section: Introductionmentioning

confidence: 99%

“…The CAD system proposed by Tan et al [19] utilized three classier; two of them were artificial neural network and genetic algorithms and compared the results of these classifier with SVM and fixed-topology neural networks. Sua´ rez-Cuenca et al [20] have explored discriminant analysis (LDA), three types of support vector machines (SVM), artificial neural network (ANN) and quadratic discriminant analysis (QDA).…”

Section: Introductionmentioning

confidence: 99%

An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics

Jahangir¹,

Imran²,

Abbas³

et al. 2016

ijacsa

View full text Add to dashboard Cite

Abstract-This paper proposed a computer-aided detection (CAD) system to automatically detect pulmonary nodules from thoracic computed tomography (CT) images. Automatically detect pulmonary nodules is a difficult job because of the large deviation in size, shape, location and density of nodules. The proposed CAD scheme applies multiple 3D disk-shaped laplacian filters to enhance the shape of spherical regions. Optimal multiple thresholding and 3D distance mapping is used to extract regions of interest and separate nodules. Finally, rule-based pruning removes easily dismissible false positive structures. The proposed system provides an overall nodule detection rate of 80% with an average of 12.2 false positives per scan. The experimental results reveals that the proposed CAD can attain a comparatively high performance.

show abstract

“…Recently, systems for Computer-Aided Detection (CAD) have been gaining in popularity [1][2][3]. A significant focus on lung cancer is observed because it is by far the leading cause of cancer death among both men and women in the United States [4].…”

Section: Introductionmentioning

confidence: 99%

Analysis of a feature-deselective neuroevolution classifier (FD-NEAT) in a computer-aided lung nodule detection system for CT images

Tan

Deklerck

Jansen

et al. 2012

Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

Systems for Computer-Aided Detection (CAD), specifically for lung nodule detection received increasing attention in recent years. This is in tandem with the observation that patients who are diagnosed with early stage lung cancer and who undergo curative resection have a much better prognosis. In this paper, we analyze the performance of a novel feature-deselective neuroevolution method called FD-NEAT to retain relevant features derived from CT images and evolve neural networks that perform well for combined feature selection and classification. Network performance is analyzed based on radiologists' ratings of various lung nodule characteristics defined in the LIDC database. The analysis shows that the FD-NEAT classifier relates well with the radiologists' perception in almost all the defined nodule characteristics, and shows that FD-NEAT evolves networks that are less complex than the fixed-topology ANN in terms of number of connections.

show abstract

A novel computer-aided lung nodule detection system for CT images

Abstract: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.

Cited by 206 publications

References 62 publications

Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

An Improved Pulmonary Nodule Detection Scheme based on Multi-Layered Filtering and 3d Distance Metrics

Analysis of a feature-deselective neuroevolution classifier (FD-NEAT) in a computer-aided lung nodule detection system for CT images

Contact Info

Product

Resources

About