199 related articles for article (PubMed ID: 28071843)
1. Automated measurement of pressure injury through image processing.
Li D; Mathews C
J Clin Nurs; 2017 Nov; 26(21-22):3564-3575. PubMed ID: 28071843
[TBL] [Abstract][Full Text] [Related]
2. The characteristics of pressure injury photographs from the electronic health record in clinical settings.
Li D; Mathews C; Zhang F
J Clin Nurs; 2018 Feb; 27(3-4):819-828. PubMed ID: 29076271
[TBL] [Abstract][Full Text] [Related]
3. Wound tissue segmentation by computerised image analysis of clinical pressure injury photographs: a pilot study.
Li D; Mathews C; Zamarripa C; Zhang F; Xiao Q
J Wound Care; 2022 Aug; 31(8):710-719. PubMed ID: 36001699
[TBL] [Abstract][Full Text] [Related]
4. Skin colour assessment of replanted fingers in digital images and its reliability for the incorporation of images in nursing progress notes.
Terashima T; Yoshimura S
J Clin Nurs; 2018 Mar; 27(5-6):e1225-e1232. PubMed ID: 29266698
[TBL] [Abstract][Full Text] [Related]
5. Computerized segmentation and measurement of chronic wound images.
Ahmad Fauzi MF; Khansa I; Catignani K; Gordillo G; Sen CK; Gurcan MN
Comput Biol Med; 2015 May; 60():74-85. PubMed ID: 25756704
[TBL] [Abstract][Full Text] [Related]
6. Concurrent validation and reliability of digital image analysis of granulation tissue color for clinical pressure ulcers.
Iizaka S; Sugama J; Nakagami G; Kaitani T; Naito A; Koyanagi H; Matsuo J; Kadono T; Konya C; Sanada H
Wound Repair Regen; 2011; 19(4):455-63. PubMed ID: 21518090
[TBL] [Abstract][Full Text] [Related]
7. Automated framework for accurate segmentation of pressure ulcer images.
Garcia-Zapirain B; Shalaby A; El-Baz A; Elmaghraby A
Comput Biol Med; 2017 Nov; 90():137-145. PubMed ID: 28987989
[TBL] [Abstract][Full Text] [Related]
8. The challenge of objective scar colour assessment in a clinical setting: using digital photography.
Anderson JC; Hallam MJ; Nduka C; Osorio D
J Wound Care; 2015 Aug; 24(8):379-87. PubMed ID: 26562381
[TBL] [Abstract][Full Text] [Related]
9. Photographing Injuries in the Acute Care Setting: Development and Evaluation of a Standardized Protocol for Research, Forensics, and Clinical Practice.
Bloemen EM; Rosen T; Cline Schiroo JA; Clark S; Mulcare MR; Stern ME; Mysliwiec R; Flomenbaum NE; Lachs MS; Hargarten S
Acad Emerg Med; 2016 May; 23(5):653-9. PubMed ID: 26932497
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images.
Fortin M; Omidyeganeh M; Battié MC; Ahmad O; Rivaz H
Biomed Eng Online; 2017 May; 16(1):61. PubMed ID: 28532491
[TBL] [Abstract][Full Text] [Related]
11. Binary tissue classification on wound images with neural networks and bayesian classifiers.
Veredas F; Mesa H; Morente L
IEEE Trans Med Imaging; 2010 Feb; 29(2):410-27. PubMed ID: 19825516
[TBL] [Abstract][Full Text] [Related]
12. Computer-assisted, interactive fundus image processing for macular drusen quantitation.
Shin DS; Javornik NB; Berger JW
Ophthalmology; 1999 Jun; 106(6):1119-25. PubMed ID: 10366080
[TBL] [Abstract][Full Text] [Related]
13. Efficient use of mobile devices for quantification of pressure injury images.
Garcia-Zapirain B; Sierra-Sosa D; Ortiz D; Isaza-Monsalve M; Elmaghraby A
Technol Health Care; 2018; 26(S1):269-280. PubMed ID: 29710755
[TBL] [Abstract][Full Text] [Related]
14. Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry.
David OP; Sierra-Sosa D; Zapirain BG
Biomed Eng Online; 2017 Jan; 16(1):4. PubMed ID: 28086892
[TBL] [Abstract][Full Text] [Related]
15. Lower extremity ulcer image segmentation of visual and near-infrared imagery.
Bochko V; Välisuo P; Harju T; Alander J
Skin Res Technol; 2010 May; 16(2):190-7. PubMed ID: 20456099
[TBL] [Abstract][Full Text] [Related]
16. Predictive validity of granulation tissue color measured by digital image analysis for deep pressure ulcer healing: a multicenter prospective cohort study.
Iizaka S; Kaitani T; Sugama J; Nakagami G; Naito A; Koyanagi H; Konya C; Sanada H
Wound Repair Regen; 2013; 21(1):25-34. PubMed ID: 23110386
[TBL] [Abstract][Full Text] [Related]
17. Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images.
Feeny AK; Tadarati M; Freund DE; Bressler NM; Burlina P
Comput Biol Med; 2015 Oct; 65():124-36. PubMed ID: 26318113
[TBL] [Abstract][Full Text] [Related]
18. Class separation improvements in pixel classification using colour injection.
Blanco E; Mazo M; Bergasa L; Palazuelos S; Rodríguez J; Losada C; Martín J
Sensors (Basel); 2010; 10(8):7803-42. PubMed ID: 22163628
[TBL] [Abstract][Full Text] [Related]
19. Clinical validation of an algorithm for rapid and accurate automated segmentation of intracoronary optical coherence tomography images.
Chatzizisis YS; Koutkias VG; Toutouzas K; Giannopoulos A; Chouvarda I; Riga M; Antoniadis AP; Cheimariotis G; Doulaverakis C; Tsampoulatidis I; Bouki K; Kompatsiaris I; Stefanadis C; Maglaveras N; Giannoglou GD
Int J Cardiol; 2014 Apr; 172(3):568-80. PubMed ID: 24529948
[TBL] [Abstract][Full Text] [Related]
20. Automatic evaluation of pressure sore status by combining information obtained from high-frequency ultrasound and digital photography.
Moghimi S; Baygi MH; Torkaman G
Comput Biol Med; 2011 Jul; 41(7):427-34. PubMed ID: 21600572
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
