360 related articles for article (PubMed ID: 12674239)
21. Pulmonary nodules: a quantitative method of diagnosis by evaluating nodule perimeter difference to approximate oval using three-dimensional CT images.
Kamiya H; Murayama S; Kakinohana Y; Miyara T
Clin Imaging; 2011; 35(2):123-6. PubMed ID: 21377050
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. 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]
24. 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]
25. Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis.
Matsuki Y; Nakamura K; Watanabe H; Aoki T; Nakata H; Katsuragawa S; Doi K
AJR Am J Roentgenol; 2002 Mar; 178(3):657-63. PubMed ID: 11856693
[TBL] [Abstract][Full Text] [Related]
26. 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]
27. A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics.
Kaya A; Can AB
J Biomed Inform; 2015 Aug; 56():69-79. PubMed ID: 26008877
[TBL] [Abstract][Full Text] [Related]
28. Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience.
Shiraishi J; Abe H; Engelmann R; Aoyama M; MacMahon H; Doi K
Radiology; 2003 May; 227(2):469-74. PubMed ID: 12732700
[TBL] [Abstract][Full Text] [Related]
29. A novel lung nodules detection scheme based on vessel segmentation on CT images.
Jia T; Zhang H; Meng H
Biomed Mater Eng; 2014; 24(6):3179-86. PubMed ID: 25227026
[TBL] [Abstract][Full Text] [Related]
30. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme.
Wu YC; Doi K; Giger ML; Metz CE; Zhang W
J Digit Imaging; 1994 Nov; 7(4):196-207. PubMed ID: 7858017
[TBL] [Abstract][Full Text] [Related]
31. Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT.
Guo W; Li Q
Med Phys; 2014 Sep; 41(9):091906. PubMed ID: 25186393
[TBL] [Abstract][Full Text] [Related]
32. 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]
33. Estimation of malignancy of pulmonary nodules at CT scans: Effect of computer-aided diagnosis on diagnostic performance of radiologists.
Liu J; Zhao L; Han X; Ji H; Liu L; He W
Asia Pac J Clin Oncol; 2021 Jun; 17(3):216-221. PubMed ID: 32757455
[TBL] [Abstract][Full Text] [Related]
34. In vivo repeatability of automated volume calculations of small pulmonary nodules with CT.
Rampinelli C; De Fiori E; Raimondi S; Veronesi G; Bellomi M
AJR Am J Roentgenol; 2009 Jun; 192(6):1657-61. PubMed ID: 19457831
[TBL] [Abstract][Full Text] [Related]
35. Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis.
Gierada DS; Politte DG; Zheng J; Schechtman KB; Whiting BR; Smith KE; Crabtree T; Kreisel D; Krupnick AS; Patterson GA; Puri V; Meyers BF
J Comput Assist Tomogr; 2016; 40(4):589-95. PubMed ID: 27096403
[TBL] [Abstract][Full Text] [Related]
36. 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]
37. Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs ROC analysis of radiologists' performance.
Shiraishi J; Abe H; Li F; Engelmann R; MacMahon H; Doi K
Acad Radiol; 2006 Aug; 13(8):995-1003. PubMed ID: 16843852
[TBL] [Abstract][Full Text] [Related]
38. A computerized scheme for lung nodule detection in multiprojection chest radiography.
Guo W; Li Q; Boyce SJ; McAdams HP; Shiraishi J; Doi K; Samei E
Med Phys; 2012 Apr; 39(4):2001-12. PubMed ID: 22482621
[TBL] [Abstract][Full Text] [Related]
39. Modeling of the lung nodules for detection in LDCT scans.
Farag A; Elhabian S; Graham J; Farag A; Elshazly S; Falk R; Mahdi H; Abdelmunim H; Al-Ghaafary S
Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():3618-21. PubMed ID: 21096845
[TBL] [Abstract][Full Text] [Related]
40. JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function.
Lo SB; Freedman MT; Gillis LB; White CS; Mun SK
AJR Am J Roentgenol; 2018 Mar; 210(3):480-488. PubMed ID: 29336601
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]