183 related articles for article (PubMed ID: 31161595)
1. 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]
2. 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]
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. 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]
5. Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.
Jebamony J; Jacob D
Curr Med Imaging; 2020; 16(6):703-710. PubMed ID: 32723242
[TBL] [Abstract][Full Text] [Related]
6. [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]
7. Fuzzy-based classification of breast lesions using ultrasound echography and elastography.
Selvan S; Kavitha M; Devi SS; Suresh S
Ultrasound Q; 2012 Sep; 28(3):159-67. PubMed ID: 22902839
[TBL] [Abstract][Full Text] [Related]
8. Decision support system for breast cancer detection using mammograms.
Ganesan K; Acharya RU; Chua CK; Min LC; Mathew B; Thomas AK
Proc Inst Mech Eng H; 2013 Jul; 227(7):721-32. PubMed ID: 23636749
[TBL] [Abstract][Full Text] [Related]
9. Ultrasound Strain Elastography for Breast Lesions: Computer-Aided Evaluation With Quantifiable Elastographic Features.
Xiao Y; Zeng J; Zhang X; Niu LL; Qian M; Wang CZ; Zheng HR; Zheng RQ
J Ultrasound Med; 2017 Jun; 36(6):1089-1100. PubMed ID: 28295467
[TBL] [Abstract][Full Text] [Related]
10. Support vector machine based diagnostic system for breast cancer using swarm intelligence.
Chen HL; Yang B; Wang G; Wang SJ; Liu J; Liu DY
J Med Syst; 2012 Aug; 36(4):2505-19. PubMed ID: 21537848
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.
Dong M; Lu X; Ma Y; Guo Y; Ma Y; Wang K
J Digit Imaging; 2015 Oct; 28(5):613-25. PubMed ID: 25776767
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan™ systems.
Acharya UR; Vinitha Sree S; Krishnan MM; Molinari F; Garberoglio R; Suri JS
Ultrasonics; 2012 Apr; 52(4):508-20. PubMed ID: 22154208
[TBL] [Abstract][Full Text] [Related]
16. Breast Tumor Classification in Ultrasound Images by Fusion of Deep Convolutional Neural Network and Shallow LBP Feature.
Chen H; Ma M; Liu G; Wang Y; Jin Z; Liu C
J Digit Imaging; 2023 Jun; 36(3):932-946. PubMed ID: 36720840
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images.
El Houby EMF
J Med Syst; 2018 Jul; 42(8):157. PubMed ID: 29995204
[TBL] [Abstract][Full Text] [Related]
20. Breast ultrasound image enhancement using fuzzy logic.
Guo Y; Cheng HD; Huang J; Tian J; Zhao W; Sun L; Su Y
Ultrasound Med Biol; 2006 Feb; 32(2):237-47. PubMed ID: 16464669
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
