140 related articles for article (PubMed ID: 17354734)
1. Automated detection of small-size pulmonary nodules based on helical CT images.
Zhang X; McLennan G; Hoffman EA; Sonka M
Inf Process Med Imaging; 2005; 19():664-76. PubMed ID: 17354734
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. The GGO lesions detected by computer-aided detection system on chest MDCT images.
Lee JW; Jeong JW; Lee S; Yoo DS; Kim S
Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():1983-5. PubMed ID: 17945689
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).
Suzuki K
Phys Med Biol; 2009 Sep; 54(18):S31-45. PubMed ID: 19687563
[TBL] [Abstract][Full Text] [Related]
8. Efficacy of computer-aided detection system and thin-slab maximum intensity projection technique in the detection of pulmonary nodules in patients with resected metastases.
Park EA; Goo JM; Lee JW; Kang CH; Lee HJ; Lee CH; Park CM; Lee HY; Im JG
Invest Radiol; 2009 Feb; 44(2):105-13. PubMed ID: 19034026
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT?
Suzuki K; Doi K
Acad Radiol; 2005 Oct; 12(10):1333-41. PubMed ID: 16179210
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT.
Iwano S; Nakamura T; Kamioka Y; Ikeda M; Ishigaki T
Comput Med Imaging Graph; 2008 Jul; 32(5):416-22. PubMed ID: 18501556
[TBL] [Abstract][Full Text] [Related]
14. Quantitative nodule detection in low dose chest CT scans: new template modeling and evaluation for CAD system design.
Farag AA; El-Baz A; Gimelfarb G; El-Ghar MA; Eldiasty T
Med Image Comput Comput Assist Interv; 2005; 8(Pt 1):720-8. PubMed ID: 16685910
[TBL] [Abstract][Full Text] [Related]
15. Pulmonary nodule volumetric measurement variability as a function of CT slice thickness and nodule morphology.
Petrou M; Quint LE; Nan B; Baker LH
AJR Am J Roentgenol; 2007 Feb; 188(2):306-12. PubMed ID: 17242235
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Robust pulmonary nodule segmentation in CT: improving performance for juxtapleural cases.
Okada K; Ramesh V; Krishnan A; Singh M; Akdemir U
Med Image Comput Comput Assist Interv; 2005; 8(Pt 2):781-9. PubMed ID: 16686031
[TBL] [Abstract][Full Text] [Related]
18. Pulmonary nodule detection in CT images with quantized convergence index filter.
Matsumoto S; Kundel HL; Gee JC; Gefter WB; Hatabu H
Med Image Anal; 2006 Jun; 10(3):343-52. PubMed ID: 16542867
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Solid, part-solid, or non-solid?: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system.
Jacobs C; van Rikxoort EM; Scholten ET; de Jong PA; Prokop M; Schaefer-Prokop C; van Ginneken B
Invest Radiol; 2015 Mar; 50(3):168-73. PubMed ID: 25478740
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]