BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

346 related articles for article (PubMed ID: 19000952)

  • 1. Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications.
    Karahaliou AN; Boniatis IS; Skiadopoulos SG; Sakellaropoulos FN; Arikidis NS; Likaki EA; Panayiotakis GS; Costaridou LI
    IEEE Trans Inf Technol Biomed; 2008 Nov; 12(6):731-8. PubMed ID: 19000952
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis.
    Karahaliou A; Skiadopoulos S; Boniatis I; Sakellaropoulos P; Likaki E; Panayiotakis G; Costaridou L
    Br J Radiol; 2007 Aug; 80(956):648-56. PubMed ID: 17621604
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces.
    Chan HP; Sahiner B; Lam KL; Petrick N; Helvie MA; Goodsitt MM; Adler DD
    Med Phys; 1998 Oct; 25(10):2007-19. PubMed ID: 9800710
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network.
    Chan HP; Sahiner B; Petrick N; Helvie MA; Lam KL; Adler DD; Goodsitt MM
    Phys Med Biol; 1997 Mar; 42(3):549-67. PubMed ID: 9080535
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.
    Malar E; Kandaswamy A; Chakravarthy D; Giri Dharan A
    Comput Biol Med; 2012 Sep; 42(9):898-905. PubMed ID: 22871899
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Breast microcalcifications detection based on fusing features with DTCWT.
    Wang Z; Xin J; Zhang Q; Gao S; Ma C; Ren J; Zhang H; Qian W; Zhu W; Zhang X; Liu J
    J Xray Sci Technol; 2020; 28(2):197-218. PubMed ID: 31985483
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Usefulness of texture analysis for computerized classification of breast lesions on mammograms.
    Pereira RR; Azevedo Marques PM; Honda MO; Kinoshita SK; Engelmann R; Muramatsu C; Doi K
    J Digit Imaging; 2007 Sep; 20(3):248-55. PubMed ID: 17122993
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms.
    Zhang W; Doi K; Giger ML; Nishikawa RM; Schmidt RA
    Med Phys; 1996 Apr; 23(4):595-601. PubMed ID: 8860907
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A swarm optimized neural network system for classification of microcalcification in mammograms.
    Dheeba J; Selvi ST
    J Med Syst; 2012 Oct; 36(5):3051-61. PubMed ID: 21947904
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. A ranklet-based image representation for mass classification in digital mammograms.
    Masotti M
    Med Phys; 2006 Oct; 33(10):3951-61. PubMed ID: 17089857
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography.
    Wang J; Nishikawa RM; Yang Y
    Med Phys; 2017 Jul; 44(7):3726-3738. PubMed ID: 28477395
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis.
    Sahiner B; Chan HP; Petrick N; Helvie MA; Goodsitt MM
    Med Phys; 1998 Apr; 25(4):516-26. PubMed ID: 9571620
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Segmentation of suspicious clustered microcalcifications in mammograms.
    Gavrielides MA; Lo JY; Vargas-Voracek R; Floyd CE
    Med Phys; 2000 Jan; 27(1):13-22. PubMed ID: 10659733
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network.
    Chan HP; Lo SC; Sahiner B; Lam KL; Helvie MA
    Med Phys; 1995 Oct; 22(10):1555-67. PubMed ID: 8551980
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Using tissue texture surrounding calcification clusters to predict benign vs malignant outcomes.
    Thiele DL; Kimme-Smith C; Johnson TD; McCombs M; Bassett LW
    Med Phys; 1996 Apr; 23(4):549-55. PubMed ID: 9157269
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Malignant and benign clustered microcalcifications: automated feature analysis and classification.
    Jiang Y; Nishikawa RM; Wolverton DE; Metz CE; Giger ML; Schmidt RA; Vyborny CJ; Doi K
    Radiology; 1996 Mar; 198(3):671-8. PubMed ID: 8628853
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach.
    Dheeba J; Albert Singh N; Tamil Selvi S
    J Biomed Inform; 2014 Jun; 49():45-52. PubMed ID: 24509074
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.
    Zheng Y; Keller BM; Ray S; Wang Y; Conant EF; Gee JC; Kontos D
    Med Phys; 2015 Jul; 42(7):4149-60. PubMed ID: 26133615
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.