BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

316 related articles for article (PubMed ID: 29531575)

  • 1. Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector.
    Hao R; Qiang Y; Yan X
    Comput Math Methods Med; 2018; 2018():2183847. PubMed ID: 29531575
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Segmentation Framework of Pulmonary Nodules in Lung CT Images.
    Mukhopadhyay S
    J Digit Imaging; 2016 Feb; 29(1):86-103. PubMed ID: 26055544
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model.
    Li B; Chen K; Tian L; Yeboah Y; Ou S
    Comput Math Methods Med; 2013; 2013():515386. PubMed ID: 23690876
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Fuzzy speed function based active contour model for segmentation of pulmonary nodules.
    Chen K; Li B; Tian LF; Zhu WB; Bao YH
    Biomed Mater Eng; 2014; 24(1):539-47. PubMed ID: 24211937
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel lung nodules detection scheme based on vessel segmentation on CT images.
    Jia T; Zhang H; Meng H
    Biomed Mater Eng; 2014; 24(6):3179-86. PubMed ID: 25227026
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.
    Way TW; Hadjiiski LM; Sahiner B; Chan HP; Cascade PN; Kazerooni EA; Bogot N; Zhou C
    Med Phys; 2006 Jul; 33(7):2323-37. PubMed ID: 16898434
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme.
    Han H; Li L; Han F; Song B; Moore W; Liang Z
    IEEE J Biomed Health Inform; 2015 Mar; 19(2):648-59. PubMed ID: 25486657
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.
    Teramoto A; Fujita H; Yamamuro O; Tamaki T
    Med Phys; 2016 Jun; 43(6):2821-2827. PubMed ID: 27277030
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.
    Wang Z; Xin J; Sun P; Lin Z; Yao Y; Gao X
    Comput Methods Programs Biomed; 2018 Aug; 162():197-209. PubMed ID: 29903487
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Segmentation of pulmonary nodules in CT images based on 3D-UNET combined with three-dimensional conditional random field optimization.
    Wu W; Gao L; Duan H; Huang G; Ye X; Nie S
    Med Phys; 2020 Sep; 47(9):4054-4063. PubMed ID: 32428969
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Shape-based computer-aided detection of lung nodules in thoracic CT images.
    Ye X; Lin X; Dehmeshki J; Slabaugh G; Beddoe G
    IEEE Trans Biomed Eng; 2009 Jul; 56(7):1810-20. PubMed ID: 19527950
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Juxta-vascular nodule segmentation based on flow entropy and geodesic distance.
    Sun S; Guo Y; Guan Y; Ren H; Fan L; Kang Y
    IEEE J Biomed Health Inform; 2014 Jul; 18(4):1355-62. PubMed ID: 24733031
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation.
    Singadkar G; Mahajan A; Thakur M; Talbar S
    J Digit Imaging; 2020 Jun; 33(3):678-684. PubMed ID: 32026218
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets.
    Zhang W; Wang X; Li X; Chen J
    Comput Biol Med; 2018 Jan; 92():64-72. PubMed ID: 29154123
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
    Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
    Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images.
    Chen B; Kitasaka T; Honma H; Takabatake H; Mori M; Natori H; Mori K
    Int J Comput Assist Radiol Surg; 2012 May; 7(3):465-82. PubMed ID: 21739111
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Potential lung nodules identification for characterization by variable multistep threshold and shape indices from CT images.
    Iqbal S; Iqbal K; Arif F; Shaukat A; Khanum A
    Comput Math Methods Med; 2014; 2014():241647. PubMed ID: 25506388
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering.
    Li B; Chen Q; Peng G; Guo Y; Chen K; Tian L; Ou S; Wang L
    Biomed Eng Online; 2016 May; 15(1):49. PubMed ID: 27150553
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification.
    Liu H; Zhang CM; Su ZY; Wang K; Deng K
    Comput Math Methods Med; 2015; 2015():185726. PubMed ID: 25945120
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics.
    Kaya A; Can AB
    J Biomed Inform; 2015 Aug; 56():69-79. PubMed ID: 26008877
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.