106 related articles for article (PubMed ID: 28269153)
1. A new approach for the detection of architectural distortions using textural analysis of surrounding tissue.
Zyout I; Togneri R
Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():3965-3968. PubMed ID: 28269153
[TBL] [Abstract][Full Text] [Related]
2. A computer-aided detection of the architectural distortion in digital mammograms using the fractal dimension measurements of BEMD.
Zyout I; Togneri R
Comput Med Imaging Graph; 2018 Dec; 70():173-184. PubMed ID: 29691123
[TBL] [Abstract][Full Text] [Related]
3. Empirical mode decomposition of digital mammograms for the statistical based characterization of architectural distortion.
Zyout I; Togneri R
Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():109-12. PubMed ID: 26736212
[TBL] [Abstract][Full Text] [Related]
4. A study on the computerized fractal analysis of architectural distortion in screening mammograms.
Tourassi GD; Delong DM; Floyd CE
Phys Med Biol; 2006 Mar; 51(5):1299-312. PubMed ID: 16481695
[TBL] [Abstract][Full Text] [Related]
5. Mass segmentation in mammograms by using Bidimensional Emperical Mode Decomposition BEMD.
Jai-Andaloussi S; Sekkaki A; Quellec G; Lamard M; Cazuguel G; Roux C
Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():5441-4. PubMed ID: 24110967
[TBL] [Abstract][Full Text] [Related]
6. Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms.
Guo Q; Shao J; Ruiz VF
Int J Comput Assist Radiol Surg; 2009 Jan; 4(1):11-25. PubMed ID: 20033598
[TBL] [Abstract][Full Text] [Related]
7. Characterizing Architectural Distortion in Mammograms by Linear Saliency.
Narváez F; Alvarez J; Garcia-Arteaga JD; Tarquino J; Romero E
J Med Syst; 2017 Feb; 41(2):26. PubMed ID: 28005248
[TBL] [Abstract][Full Text] [Related]
8. Computer-aided detection of architectural distortion in prior mammograms of interval cancer.
Rangayyan RM; Banik S; Desautels JE
J Digit Imaging; 2010 Oct; 23(5):611-31. PubMed ID: 20127270
[TBL] [Abstract][Full Text] [Related]
9. Significance of MPEG-7 textural features for improved mass detection in mammography.
Eltonsy NH; Tourassi GD; Fadeev A; Elmaghraby AS
Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():4779-82. PubMed ID: 17946650
[TBL] [Abstract][Full Text] [Related]
10. Detection of architectural distortion in prior mammograms of interval-cancer cases with neural networks.
Banik S; Rangayyan RM; Desautels JE
Annu Int Conf IEEE Eng Med Biol Soc; 2009; 2009():6667-70. PubMed ID: 19964909
[TBL] [Abstract][Full Text] [Related]
11. Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis.
Karahaliou A; Skiadopoulos S; Boniatis I; Sakellaropoulos P; Likaki E; Panayiotakis G; Costaridou L
Br J Radiol; 2007 Aug; 80(956):648-56. PubMed ID: 17621604
[TBL] [Abstract][Full Text] [Related]
12. Detection of architectural distortion in prior mammograms.
Banik S; Rangayyan RM; Desautels JE
IEEE Trans Med Imaging; 2011 Feb; 30(2):279-94. PubMed ID: 20851789
[TBL] [Abstract][Full Text] [Related]
13. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.
Zyout I; Czajkowska J; Grzegorzek M
Comput Med Imaging Graph; 2015 Dec; 46 Pt 2():95-107. PubMed ID: 25795630
[TBL] [Abstract][Full Text] [Related]
14. Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers.
Mavroforakis ME; Georgiou HV; Dimitropoulos N; Cavouras D; Theodoridis S
Artif Intell Med; 2006 Jun; 37(2):145-62. PubMed ID: 16716579
[TBL] [Abstract][Full Text] [Related]
15. Detection of architectural distortion in prior mammograms via analysis of oriented patterns.
Rangayyan RM; Banik S; Desautels JE
J Vis Exp; 2013 Aug; (78):. PubMed ID: 24022326
[TBL] [Abstract][Full Text] [Related]
16. Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation.
Kamra A; Jain VK; Singh S; Mittal S
J Digit Imaging; 2016 Feb; 29(1):104-14. PubMed ID: 26138756
[TBL] [Abstract][Full Text] [Related]
17. Location of mammograms ROI's and reduction of false-positive.
Salazar-Licea LA; Pedraza-Ortega JC; Pastrana-Palma A; Aceves-Fernandez MA
Comput Methods Programs Biomed; 2017 May; 143():97-111. PubMed ID: 28391823
[TBL] [Abstract][Full Text] [Related]
18. False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis.
Wei D; Chan HP; Petrick N; Sahiner B; Helvie MA; Adler DD; Goodsitt MM
Med Phys; 1997 Jun; 24(6):903-14. PubMed ID: 9198026
[TBL] [Abstract][Full Text] [Related]
19. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.
Bandeira Diniz JO; Bandeira Diniz PH; Azevedo Valente TL; Corrêa Silva A; de Paiva AC; Gattass M
Comput Methods Programs Biomed; 2018 Mar; 156():191-207. PubMed ID: 29428071
[TBL] [Abstract][Full Text] [Related]
20. Comparison of digital mammography and digital breast tomosynthesis in the detection of architectural distortion.
Dibble EH; Lourenco AP; Baird GL; Ward RC; Maynard AS; Mainiero MB
Eur Radiol; 2018 Jan; 28(1):3-10. PubMed ID: 28710582
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]