These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

56 related articles for article (PubMed ID: 19406614)

  • 1. A textural approach for mass false positive reduction in mammography.
    Lladó X; Oliver A; Freixenet J; Martí R; Martí J
    Comput Med Imaging Graph; 2009 Sep; 33(6):415-22. PubMed ID: 19406614
    [TBL] [Abstract][Full Text] [Related]  

  • 2. False positive reduction in mammographic mass detection using local binary patterns.
    Oliver A; Lladó X; Freixenet J; Martí J
    Med Image Comput Comput Assist Interv; 2007; 10(Pt 1):286-93. PubMed ID: 18051070
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification.
    Dhahbi S; Barhoumi W; Kurek J; Swiderski B; Kruk M; Zagrouba E
    Comput Methods Programs Biomed; 2018 Jul; 160():75-83. PubMed ID: 29728249
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms.
    Choi JY; Ro YM
    Phys Med Biol; 2012 Nov; 57(21):7029-52. PubMed ID: 23053352
    [TBL] [Abstract][Full Text] [Related]  

  • 6. False-positive reduction in mammography using multiscale spatial Weber law descriptor and support vector machines.
    Hussain M
    Neural Comput Appl; 2014; 25(1):83-93. PubMed ID: 24954976
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Detection of breast masses in mammograms by density slicing and texture flow-field analysis.
    Mudigonda NR; Rangayyan RM; Desautels JE
    IEEE Trans Med Imaging; 2001 Dec; 20(12):1215-27. PubMed ID: 11811822
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification.
    Reyad YA; Berbar MA; Hussain M
    J Med Syst; 2014 Sep; 38(9):100. PubMed ID: 25037713
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Development of an automated method for detecting mammographic masses with a partial loss of region.
    Hatanaka Y; Hara T; Fujita H; Kasai S; Endo T; Iwase T
    IEEE Trans Med Imaging; 2001 Dec; 20(12):1209-14. PubMed ID: 11811821
    [TBL] [Abstract][Full Text] [Related]  

  • 10. On combining morphological component analysis and concentric morphology model for mammographic mass detection.
    Gao X; Wang Y; Li X; Tao D
    IEEE Trans Inf Technol Biomed; 2010 Mar; 14(2):266-73. PubMed ID: 19906595
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection.
    Rojas Domínguez A; Nandi AK
    Comput Med Imaging Graph; 2008 Jun; 32(4):304-15. PubMed ID: 18358699
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A concentric morphology model for the detection of masses in mammography.
    Eltonsy NH; Tourassi GD; Elmaghraby AS
    IEEE Trans Med Imaging; 2007 Jun; 26(6):880-9. PubMed ID: 17679338
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Modeling false positive error making patterns in radiology trainees for improved mammography education.
    Zhang J; Silber JI; Mazurowski MA
    J Biomed Inform; 2015 Apr; 54():50-7. PubMed ID: 25640462
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Development of tolerant features for characterization of masses in mammograms.
    Rojas-Domínguez A; Nandi AK
    Comput Biol Med; 2009 Aug; 39(8):678-88. PubMed ID: 19524221
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Computer-aided detection schemes: the effect of limiting the number of cued regions in each case.
    Zheng B; Leader JK; Abrams G; Shindel B; Catullo V; Good WF; Gur D
    AJR Am J Roentgenol; 2004 Mar; 182(3):579-83. PubMed ID: 14975949
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Computer aided detection of masses in mammograms as decision support.
    Karssemeijer N; Otten JD; Rijken H; Holland R
    Br J Radiol; 2006 Dec; 79 Spec No 2():S123-6. PubMed ID: 17209117
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A support vector machine approach for detection of microcalcifications.
    El-Naqa I; Yang Y; Wernick MN; Galatsanos NP; Nishikawa RM
    IEEE Trans Med Imaging; 2002 Dec; 21(12):1552-63. PubMed ID: 12588039
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computer-aided detection performance in mammographic examination of masses: assessment.
    Gur D; Stalder JS; Hardesty LA; Zheng B; Sumkin JH; Chough DM; Shindel BE; Rockette HE
    Radiology; 2004 Nov; 233(2):418-23. PubMed ID: 15358846
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Use of border information in the classification of mammographic masses.
    Varela C; Timp S; Karssemeijer N
    Phys Med Biol; 2006 Jan; 51(2):425-41. PubMed ID: 16394348
    [TBL] [Abstract][Full Text] [Related]  

  • 20. ATMTN: a telemammography network architecture.
    Sheybani EO; Sankar R
    IEEE Trans Biomed Eng; 2002 Dec; 49(12):1438-43. PubMed ID: 12542239
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 3.