188 related articles for article (PubMed ID: 23206609)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
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. 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]
9. 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]
10. 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]
11. Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE.
Sui Y; Wei Y; Zhao D
Comput Math Methods Med; 2015; 2015():368674. PubMed ID: 25977704
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Semi-automatic volumetric measurement of lung cancer using multi-detector CT effects of nodule characteristics.
Iwano S; Okada T; Koike W; Matsuo K; Toya R; Yamazaki M; Ito S; Ito J; Naganwa S
Acad Radiol; 2009 Oct; 16(10):1179-86. PubMed ID: 19524456
[TBL] [Abstract][Full Text] [Related]
14. Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.
Madero Orozco H; Vergara Villegas OO; Cruz Sánchez VG; Ochoa Domínguez Hde J; Nandayapa Alfaro Mde J
Biomed Eng Online; 2015 Feb; 14():9. PubMed ID: 25888834
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. High performance lung nodule detection schemes in CT using local and global information.
Guo W; Li Q
Med Phys; 2012 Aug; 39(8):5157-68. PubMed ID: 22894441
[TBL] [Abstract][Full Text] [Related]
17. Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size.
Sahiner B; Chan HP; Hadjiiski LM; Cascade PN; Kazerooni EA; Chughtai AR; Poopat C; Song T; Frank L; Stojanovska J; Attili A
Acad Radiol; 2009 Dec; 16(12):1518-30. PubMed ID: 19896069
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. 3D shape analysis to reduce false positives for lung nodule detection systems.
Filho AOC; Silva AC; de Paiva AC; Nunes RA; Gattass M
Med Biol Eng Comput; 2017 Aug; 55(8):1199-1213. PubMed ID: 27752930
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
