144 related articles for article (PubMed ID: 29792411)
1. [Experiments on the Feature Selection and Classiifcation of Ultrasound Elastography Images for the Diagnosis of Breast Cancers].
Zhang Y; Zhang Y; Xiao Y
Zhongguo Yi Liao Qi Xie Za Zhi; 2016 Nov; 40(6):397-402. PubMed ID: 29792411
[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. Robust phase-based texture descriptor for classification of breast ultrasound images.
Cai L; Wang X; Wang Y; Guo Y; Yu J; Wang Y
Biomed Eng Online; 2015 Mar; 14():26. PubMed ID: 25889570
[TBL] [Abstract][Full Text] [Related]
5. Feature selection algorithm based on binary BAT algorithm and optimum path forest classifier for breast cancer detection using both echographic and elastographic mode ultrasound images.
Sasikala S; Ezhilarasi M; Arunkumar S
J Cancer Res Ther; 2023; 19(2):191-197. PubMed ID: 37006057
[TBL] [Abstract][Full Text] [Related]
6. Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound.
Gomez W; Pereira WC; Infantosi AF
IEEE Trans Med Imaging; 2012 Oct; 31(10):1889-99. PubMed ID: 22759441
[TBL] [Abstract][Full Text] [Related]
7. Particle swarm optimization based fusion of ultrasound echographic and elastographic texture features for improved breast cancer detection.
Sasikala S; Bharathi M; Ezhilarasi M; Senthil S; Reddy MR
Australas Phys Eng Sci Med; 2019 Sep; 42(3):677-688. PubMed ID: 31161595
[TBL] [Abstract][Full Text] [Related]
8. Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images.
Yu Y; Xiao Y; Cheng J; Chiu B
Comput Biol Med; 2018 Feb; 93():31-46. PubMed ID: 29275098
[TBL] [Abstract][Full Text] [Related]
9. Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.
Milenković J; Hertl K; Košir A; Zibert J; Tasič JF
Artif Intell Med; 2013 Jun; 58(2):101-14. PubMed ID: 23548472
[TBL] [Abstract][Full Text] [Related]
10. Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions.
Osapoetra LO; Chan W; Tran W; Kolios MC; Czarnota GJ
PLoS One; 2020; 15(12):e0244965. PubMed ID: 33382837
[TBL] [Abstract][Full Text] [Related]
11. Computer-aided analysis of ultrasound elasticity images for classification of benign and malignant breast masses.
Moon WK; Choi JW; Cho N; Park SH; Chang JM; Jang M; Kim KG
AJR Am J Roentgenol; 2010 Dec; 195(6):1460-5. PubMed ID: 21098210
[TBL] [Abstract][Full Text] [Related]
12. Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound.
Xu Z; Wang Y; Chen M; Zhang Q
Comput Biol Med; 2022 Oct; 149():105920. PubMed ID: 35986969
[TBL] [Abstract][Full Text] [Related]
13. A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images.
Wei M; Du Y; Wu X; Su Q; Zhu J; Zheng L; Lv G; Zhuang J
Comput Math Methods Med; 2020; 2020():5894010. PubMed ID: 33062038
[TBL] [Abstract][Full Text] [Related]
14. Comparison of shear-wave and strain ultrasound elastography in the differentiation of benign and malignant breast lesions.
Chang JM; Won JK; Lee KB; Park IA; Yi A; Moon WK
AJR Am J Roentgenol; 2013 Aug; 201(2):W347-56. PubMed ID: 23883252
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images.
Wu WJ; Lin SW; Moon WK
Comput Med Imaging Graph; 2012 Dec; 36(8):627-33. PubMed ID: 22939834
[TBL] [Abstract][Full Text] [Related]
17. Breast tumor classification using different features of quantitative ultrasound parametric images.
Hsu SM; Kuo WH; Kuo FC; Liao YY
Int J Comput Assist Radiol Surg; 2019 Apr; 14(4):623-633. PubMed ID: 30617720
[TBL] [Abstract][Full Text] [Related]
18. Diagnostic performance of quantitative shear wave elastography in the evaluation of solid breast masses: determination of the most discriminatory parameter.
Au FW; Ghai S; Moshonov H; Kahn H; Brennan C; Dua H; Crystal P
AJR Am J Roentgenol; 2014 Sep; 203(3):W328-36. PubMed ID: 25148191
[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. Comparison of 3D and 2D shear-wave elastography for differentiating benign and malignant breast masses: focus on the diagnostic performance.
Choi HY; Sohn YM; Seo M
Clin Radiol; 2017 Oct; 72(10):878-886. PubMed ID: 28526455
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]