460 related articles for article (PubMed ID: 16193773)
1. Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting.
Ge Z; Sahiner B; Chan HP; Hadjiiski LM; Cascade PN; Bogot N; Kazerooni EA; Wei J; Zhou C
Med Phys; 2005 Aug; 32(8):2443-54. PubMed ID: 16193773
[TBL] [Abstract][Full Text] [Related]
2. Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features.
Shah SK; McNitt-Gray MF; Rogers SR; Goldin JG; Suh RD; Sayre JW; Petkovska I; Kim HJ; Aberle DR
Acad Radiol; 2005 Oct; 12(10):1310-9. PubMed ID: 16179208
[TBL] [Abstract][Full Text] [Related]
3. Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system.
Gurcan MN; Sahiner B; Petrick N; Chan HP; Kazerooni EA; Cascade PN; Hadjiiski L
Med Phys; 2002 Nov; 29(11):2552-8. PubMed ID: 12462722
[TBL] [Abstract][Full Text] [Related]
4. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network.
Suzuki K; Li F; Sone S; Doi K
IEEE Trans Med Imaging; 2005 Sep; 24(9):1138-50. PubMed ID: 16156352
[TBL] [Abstract][Full Text] [Related]
5. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.
Way TW; Hadjiiski LM; Sahiner B; Chan HP; Cascade PN; Kazerooni EA; Bogot N; Zhou C
Med Phys; 2006 Jul; 33(7):2323-37. PubMed ID: 16898434
[TBL] [Abstract][Full Text] [Related]
6. Computer-aided detection of solid lung nodules in lossy compressed multidetector computed tomography chest exams.
Raffy P; Gaudeau Y; Miller DP; Moureaux JM; Castellino RA
Acad Radiol; 2006 Oct; 13(10):1194-203. PubMed ID: 16979068
[TBL] [Abstract][Full Text] [Related]
7. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.
Messay T; Hardie RC; Rogers SK
Med Image Anal; 2010 Jun; 14(3):390-406. PubMed ID: 20346728
[TBL] [Abstract][Full Text] [Related]
8. A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database.
Schilham AM; van Ginneken B; Loog M
Med Image Anal; 2006 Apr; 10(2):247-58. PubMed ID: 16293441
[TBL] [Abstract][Full Text] [Related]
9. A novel approach to nodule feature optimization on thin section thoracic CT.
Samala R; Moreno W; You Y; Qian W
Acad Radiol; 2009 Apr; 16(4):418-27. PubMed ID: 19268853
[TBL] [Abstract][Full Text] [Related]
10. Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels.
Saien S; Hamid Pilevar A; Abrishami Moghaddam H
Comput Biol Med; 2014 Nov; 54():188-98. PubMed ID: 25303113
[TBL] [Abstract][Full Text] [Related]
11. Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography.
Suzuki K; Yoshida H; Näppi J; Armato SG; Dachman AH
Med Phys; 2008 Feb; 35(2):694-703. PubMed ID: 18383691
[TBL] [Abstract][Full Text] [Related]
12. A new method for spherical object detection and its application to computer aided detection of pulmonary nodules in CT images.
Zhang X; Stockel J; Wolf M; Cathier P; McLennan G; Hoffman EA; Sonka M
Med Image Comput Comput Assist Interv; 2007; 10(Pt 1):842-9. PubMed ID: 18051137
[TBL] [Abstract][Full Text] [Related]
13. Automated detection of lung nodules in CT images using shape-based genetic algorithm.
Dehmeshki J; Ye X; Lin X; Valdivieso M; Amin H
Comput Med Imaging Graph; 2007 Sep; 31(6):408-17. PubMed ID: 17524617
[TBL] [Abstract][Full Text] [Related]
14. Feature subset selection for improving the performance of false positive reduction in lung nodule CAD.
Böröczky L; Zhao L; Lee KP
IEEE Trans Inf Technol Biomed; 2006 Jul; 10(3):504-11. PubMed ID: 16871718
[TBL] [Abstract][Full Text] [Related]
15. Computer-aided detection of lung nodules using outer surface features.
Demir Ö; Yılmaz Çamurcu A
Biomed Mater Eng; 2015; 26 Suppl 1():S1213-22. PubMed ID: 26405880
[TBL] [Abstract][Full Text] [Related]
16. Automated lung nodule classification following automated nodule detection on CT: a serial approach.
Armato SG; Altman MB; Wilkie J; Sone S; Li F; Doi K; Roy AS
Med Phys; 2003 Jun; 30(6):1188-97. PubMed ID: 12852543
[TBL] [Abstract][Full Text] [Related]
17. Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.
Hirose T; Nitta N; Shiraishi J; Nagatani Y; Takahashi M; Murata K
Acad Radiol; 2008 Dec; 15(12):1505-12. PubMed ID: 19000867
[TBL] [Abstract][Full Text] [Related]
18. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images.
Aoyama M; Li Q; Katsuragawa S; Li F; Sone S; Doi K
Med Phys; 2003 Mar; 30(3):387-94. PubMed ID: 12674239
[TBL] [Abstract][Full Text] [Related]
19. Computer-aided detection of lung nodules by SVM based on 3D matrix patterns.
Wang Q; Kang W; Wu C; Wang B
Clin Imaging; 2013; 37(1):62-9. PubMed ID: 23206609
[TBL] [Abstract][Full Text] [Related]
20. Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching.
Shi J; Sahiner B; Chan HP; Hadjiiski L; Zhou C; Cascade PN; Bogot N; Kazerooni EA; Wu YT; Wei J
Med Phys; 2007 Apr; 34(4):1336-47. PubMed ID: 17500464
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
