110 related articles for article (PubMed ID: 16481695)
1. 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]
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. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information.
Tourassi GD; Vargas-Voracek R; Catarious DM; Floyd CE
Med Phys; 2003 Aug; 30(8):2123-30. PubMed ID: 12945977
[TBL] [Abstract][Full Text] [Related]
10. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment.
Li H; Giger ML; Olopade OI; Lan L
Acad Radiol; 2007 May; 14(5):513-21. PubMed ID: 17434064
[TBL] [Abstract][Full Text] [Related]
11. Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms.
Banik S; Rangayyan RM; Desautels JE
Int J Comput Assist Radiol Surg; 2013 Jan; 8(1):121-34. PubMed ID: 22460365
[TBL] [Abstract][Full Text] [Related]
12. Application of fractal analysis to mammography.
Raguso G; Ancona A; Chieppa L; L'abbate S; Pepe ML; Mangieri F; De Palo M; Rangayyan RM
Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():3182-5. PubMed ID: 21096599
[TBL] [Abstract][Full Text] [Related]
13. Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location.
Li H; Giger ML; Huo Z; Olopade OI; Lan L; Weber BL; Bonta I
Med Phys; 2004 Mar; 31(3):549-55. PubMed ID: 15070253
[TBL] [Abstract][Full Text] [Related]
14. Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis.
Huo Z; Giger ML; Vyborny CJ
IEEE Trans Med Imaging; 2001 Dec; 20(12):1285-92. PubMed ID: 11811828
[TBL] [Abstract][Full Text] [Related]
15. Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status.
Malkov S; Shepherd JA; Scott CG; Tamimi RM; Ma L; Bertrand KA; Couch F; Jensen MR; Mahmoudzadeh AP; Fan B; Norman A; Brandt KR; Pankratz VS; Vachon CM; Kerlikowske K
Breast Cancer Res; 2016 Dec; 18(1):122. PubMed ID: 27923387
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. 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]
19. Fractal analysis of visual search activity for mass detection during mammographic screening.
Alamudun F; Yoon HJ; Hudson KB; Morin-Ducote G; Hammond T; Tourassi GD
Med Phys; 2017 Mar; 44(3):832-846. PubMed ID: 28079249
[TBL] [Abstract][Full Text] [Related]
20. Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: application for mammography.
Bliznakova K; Suryanarayanan S; Karellas A; Pallikarakis N
Med Phys; 2010 Nov; 37(11):5604-17. PubMed ID: 21158272
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
