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 *

136 related articles for article (PubMed ID: 18003124)

  • 1. Digital mammogram spiculated mass detection and spicule segmentation using level sets.
    Ball JE; Bruce LM
    Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():4979-84. PubMed ID: 18003124
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Digital mammographic computer aided diagnosis (CAD) using adaptive level set segmentation.
    Ball JE; Bruce LM
    Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():4973-8. PubMed ID: 18003123
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Level set-based core segmentation of mammographic masses facilitating three stage (core, periphery, spiculation) analysis.
    Ball JE; Bruce LM
    Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():819-24. PubMed ID: 18002082
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.
    Sahiner B; Petrick N; Chan HP; Hadjiiski LM; Paramagul C; Helvie MA; Gurcan MN
    IEEE Trans Med Imaging; 2001 Dec; 20(12):1275-84. PubMed ID: 11811827
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Mammography segmentation with maximum likelihood active contours.
    Rahmati P; Adler A; Hamarneh G
    Med Image Anal; 2012 Aug; 16(6):1167-86. PubMed ID: 22831774
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automated detection of breast mass spiculation levels and evaluation of scheme performance.
    Jiang L; Song E; Xu X; Ma G; Zheng B
    Acad Radiol; 2008 Dec; 15(12):1534-44. PubMed ID: 19000870
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A completely automated CAD system for mass detection in a large mammographic database.
    Bellotti R; De Carlo F; Tangaro S; Gargano G; Maggipinto G; Castellano M; Massafra R; Cascio D; Fauci F; Magro R; Raso G; Lauria A; Forni G; Bagnasco S; Cerello P; Zanon E; Cheran SC; Lopez Torres E; Bottigli U; Masala GL; Oliva P; Retico A; Fantacci ME; Cataldo R; De Mitri I; De Nunzio G
    Med Phys; 2006 Aug; 33(8):3066-75. PubMed ID: 16964885
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Radiomics based detection and characterization of suspicious lesions on full field digital mammograms.
    Sapate SG; Mahajan A; Talbar SN; Sable N; Desai S; Thakur M
    Comput Methods Programs Biomed; 2018 Sep; 163():1-20. PubMed ID: 30119844
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Computer aided detection of clusters of microcalcifications on full field digital mammograms.
    Ge J; Sahiner B; Hadjiiski LM; Chan HP; Wei J; Helvie MA; Zhou C
    Med Phys; 2006 Aug; 33(8):2975-88. PubMed ID: 16964876
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system.
    Catarious DM; Baydush AH; Floyd CE
    Med Phys; 2004 Jun; 31(6):1512-20. PubMed ID: 15259655
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.
    Samala RK; Chan HP; Hadjiiski L; Helvie MA; Wei J; Cha K
    Med Phys; 2016 Dec; 43(12):6654. PubMed ID: 27908154
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A model-based framework for the detection of spiculated masses on mammography.
    Sampat MP; Bovik AC; Whitman GJ; Markey MK
    Med Phys; 2008 May; 35(5):2110-23. PubMed ID: 18561687
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Globally supported radial basis function based collocation method for evolution of level set in mass segmentation using mammograms.
    Kashyap KL; Bajpai MK; Khanna P
    Comput Biol Med; 2017 Aug; 87():22-37. PubMed ID: 28549292
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Computer-aided diagnosis of masses with full-field digital mammography.
    Li L; Clark RA; Thomas JA
    Acad Radiol; 2002 Jan; 9(1):4-12. PubMed ID: 11918357
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. 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]  

  • 18. Computer-aided detection systems for breast masses: comparison of performances on full-field digital mammograms and digitized screen-film mammograms.
    Wei J; Hadjiiski LM; Sahiner B; Chan HP; Ge J; Roubidoux MA; Helvie MA; Zhou C; Wu YT; Paramagul C; Zhang Y
    Acad Radiol; 2007 Jun; 14(6):659-69. PubMed ID: 17502255
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.
    Vikhe PS; Thool VR
    J Med Syst; 2016 Apr; 40(4):82. PubMed ID: 26811073
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 7.