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Journal Abstract Search

166 related items for PubMed ID: 7652012

  • 1. Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space.
    Chan HP, Wei D, Helvie MA, Sahiner B, Adler DD, Goodsitt MM, Petrick N.
    Phys Med Biol; 1995 May; 40(5):857-76. PubMed ID: 7652012
    [Abstract] [Full Text] [Related]

  • 2. 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
    [Abstract] [Full Text] [Related]

  • 3. Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis.
    Petrosian A, Chan HP, Helvie MA, Goodsitt MM, Adler DD.
    Phys Med Biol; 1994 Dec; 39(12):2273-88. PubMed ID: 15551553
    [Abstract] [Full Text] [Related]

  • 4. False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis.
    Wei D, Chan HP, Petrick N, Sahiner B, Helvie MA, Adler DD, Goodsitt MM.
    Med Phys; 1997 Jun; 24(6):903-14. PubMed ID: 9198026
    [Abstract] [Full Text] [Related]

  • 5. Classification of mass and normal breast tissue on digital mammograms: multiresolution texture analysis.
    Wei D, Chan HP, Helvie MA, Sahiner B, Petrick N, Adler DD, Goodsitt MM.
    Med Phys; 1995 Sep; 22(9):1501-13. PubMed ID: 8531882
    [Abstract] [Full Text] [Related]

  • 6. 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
    [Abstract] [Full Text] [Related]

  • 7. 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
    [Abstract] [Full Text] [Related]

  • 8. Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices.
    Chan HP, Wu YT, Sahiner B, Wei J, Helvie MA, Zhang Y, Moore RH, Kopans DB, Hadjiiski L, Way T.
    Med Phys; 2010 Jul; 37(7):3576-86. PubMed ID: 20831065
    [Abstract] [Full Text] [Related]

  • 9. Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis.
    Sahiner B, Chan HP, Petrick N, Helvie MA, Goodsitt MM.
    Phys Med Biol; 1998 Oct; 43(10):2853-71. PubMed ID: 9814523
    [Abstract] [Full Text] [Related]

  • 10. 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
    [Abstract] [Full Text] [Related]

  • 11. Improvement of mammographic mass characterization using spiculation meausures and morphological features.
    Sahiner B, Chan HP, Petrick N, Helvie MA, Hadjiiski LM.
    Med Phys; 2001 Jul; 28(7):1455-65. PubMed ID: 11488579
    [Abstract] [Full Text] [Related]

  • 12. Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue.
    Sahiner B, Chan HP, Wei D, Petrick N, Helvie MA, Adler DD, Goodsitt MM.
    Med Phys; 1996 Oct; 23(10):1671-84. PubMed ID: 8946365
    [Abstract] [Full Text] [Related]

  • 13. 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
    [Abstract] [Full Text] [Related]

  • 14. 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
    [Abstract] [Full Text] [Related]

  • 15. Computer aided detection of masses in mammography using subregion Hotelling observers.
    Baydush AH, Catarious DM, Abbey CK, Floyd CE.
    Med Phys; 2003 Jul; 30(7):1781-7. PubMed ID: 12906196
    [Abstract] [Full Text] [Related]

  • 16. Effect of pixel resolution on texture features of breast masses in mammograms.
    Rangayyan RM, Nguyen TM, Ayres FJ, Nandi AK.
    J Digit Imaging; 2010 Oct; 23(5):547-53. PubMed ID: 19756865
    [Abstract] [Full Text] [Related]

  • 17. 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
    [Abstract] [Full Text] [Related]

  • 18. 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
    [Abstract] [Full Text] [Related]

  • 19. Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.
    Hadjiiski L, Sahiner B, Chan HP, Petrick N, Helvie MA, Gurcan M.
    Med Phys; 2001 Nov; 28(11):2309-17. PubMed ID: 11764038
    [Abstract] [Full Text] [Related]

  • 20. Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers.
    Mavroforakis ME, Georgiou HV, Dimitropoulos N, Cavouras D, Theodoridis S.
    Artif Intell Med; 2006 Jun; 37(2):145-62. PubMed ID: 16716579
    [Abstract] [Full Text] [Related]

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