288 related articles for article (PubMed ID: 28233619)
1. Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography.
Zhang Q; Xiao Y; Suo J; Shi J; Yu J; Guo Y; Wang Y; Zheng H
Ultrasound Med Biol; 2017 May; 43(5):1058-1069. PubMed ID: 28233619
[TBL] [Abstract][Full Text] [Related]
2. Quantification of elastic heterogeneity using contourlet-based texture analysis in shear-wave elastography for breast tumor classification.
Zhang Q; Xiao Y; Chen S; Wang C; Zheng H
Ultrasound Med Biol; 2015 Feb; 41(2):588-600. PubMed ID: 25444693
[TBL] [Abstract][Full Text] [Related]
3. Computer-aided diagnosis based on quantitative elastographic features with supersonic shear wave imaging.
Xiao Y; Zeng J; Niu L; Zeng Q; Wu T; Wang C; Zheng R; Zheng H
Ultrasound Med Biol; 2014 Feb; 40(2):275-86. PubMed ID: 24268454
[TBL] [Abstract][Full Text] [Related]
4. Deep learning based classification of breast tumors with shear-wave elastography.
Zhang Q; Xiao Y; Dai W; Suo J; Wang C; Shi J; Zheng H
Ultrasonics; 2016 Dec; 72():150-7. PubMed ID: 27529139
[TBL] [Abstract][Full Text] [Related]
5. A Radiomics Approach for the Classification of Fibroepithelial Lesions on Breast Ultrasonography.
Sim Y; Lee SE; Kim EK; Kim S
Ultrasound Med Biol; 2020 May; 46(5):1133-1141. PubMed ID: 32102739
[TBL] [Abstract][Full Text] [Related]
6. Analysis of elastographic and B-mode features at sonoelastography for breast tumor classification.
Moon WK; Huang CS; Shen WC; Takada E; Chang RF; Joe J; Nakajima M; Kobayashi M
Ultrasound Med Biol; 2009 Nov; 35(11):1794-802. PubMed ID: 19767139
[TBL] [Abstract][Full Text] [Related]
7. Dual-mode artificially-intelligent diagnosis of breast tumours in shear-wave elastography and B-mode ultrasound using deep polynomial networks.
Zhang Q; Song S; Xiao Y; Chen S; Shi J; Zheng H
Med Eng Phys; 2019 Feb; 64():1-6. PubMed ID: 30578163
[TBL] [Abstract][Full Text] [Related]
8. Classification of breast tumors using sonographic texture analysis.
Ardakani AA; Gharbali A; Mohammadi A
J Ultrasound Med; 2015 Feb; 34(2):225-31. PubMed ID: 25614395
[TBL] [Abstract][Full Text] [Related]
9. A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses.
Daoud MI; Bdair TM; Al-Najar M; Alazrai R
Comput Math Methods Med; 2016; 2016():6740956. PubMed ID: 28127383
[TBL] [Abstract][Full Text] [Related]
10. EMD-DWT based transform domain feature reduction approach for quantitative multi-class classification of breast lesions.
Ara SR; Bashar SK; Alam F; Hasan MK
Ultrasonics; 2017 Sep; 80():22-33. PubMed ID: 28499122
[TBL] [Abstract][Full Text] [Related]
11. Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images.
Moon WK; Shen YW; Huang CS; Chiang LR; Chang RF
Ultrasound Med Biol; 2011 Apr; 37(4):539-48. PubMed ID: 21420580
[TBL] [Abstract][Full Text] [Related]
12. Computer-assisted assessment of ultrasound real-time elastography: initial experience in 145 breast lesions.
Zhang X; Xiao Y; Zeng J; Qiu W; Qian M; Wang C; Zheng R; Zheng H
Eur J Radiol; 2014 Jan; 83(1):e1-7. PubMed ID: 24148563
[TBL] [Abstract][Full Text] [Related]
13. Computer-aided diagnosis with textural features for breast lesions in sonograms.
Chen DR; Huang YL; Lin SH
Comput Med Imaging Graph; 2011 Apr; 35(3):220-6. PubMed ID: 21131178
[TBL] [Abstract][Full Text] [Related]
14. Radiomics With Attribute Bagging for Breast Tumor Classification Using Multimodal Ultrasound Images.
Li Y; Liu Y; Zhang M; Zhang G; Wang Z; Luo J
J Ultrasound Med; 2020 Feb; 39(2):361-371. PubMed ID: 31432552
[TBL] [Abstract][Full Text] [Related]
15. Significant differentiation of focal breast lesions: calculation of strain ratio in breast sonoelastography.
Thomas A; Degenhardt F; Farrokh A; Wojcinski S; Slowinski T; Fischer T
Acad Radiol; 2010 May; 17(5):558-63. PubMed ID: 20171905
[TBL] [Abstract][Full Text] [Related]
16. Texture feature analysis for breast ultrasound image enhancement.
Liao YY; Wu JC; Li CH; Yeh CK
Ultrason Imaging; 2011 Oct; 33(4):264-78. PubMed ID: 22518956
[TBL] [Abstract][Full Text] [Related]
17. Classification of benign and malignant breast tumors by 2-d analysis based on contour description and scatterer characterization.
Tsui PH; Liao YY; Chang CC; Kuo WH; Chang KJ; Yeh CK
IEEE Trans Med Imaging; 2010 Feb; 29(2):513-22. PubMed ID: 20129851
[TBL] [Abstract][Full Text] [Related]
18. Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy.
Wan S; Lee HC; Huang X; Xu T; Xu T; Zeng X; Zhang Z; Sheikine Y; Connolly JL; Fujimoto JG; Zhou C
Med Image Anal; 2017 May; 38():104-116. PubMed ID: 28327449
[TBL] [Abstract][Full Text] [Related]
19. Differentiation of benign from malignant nonpalpable breast masses: a comparison of computer-assisted quantification and visual assessment of lesion stiffness with the use of sonographic elastography.
Chung SY; Moon WK; Choi JW; Cho N; Jang M; Kim KG
Acta Radiol; 2010 Feb; 51(1):9-14. PubMed ID: 19929254
[TBL] [Abstract][Full Text] [Related]
20. Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features.
Joo S; Yang YS; Moon WK; Kim HC
IEEE Trans Med Imaging; 2004 Oct; 23(10):1292-300. PubMed ID: 15493696
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
