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.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

200 related items for PubMed ID: 17621604

  • 21.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 22.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 23.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 24.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 25.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 26.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 27.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 28.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 29. 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]

  • 30.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 31.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 32. Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.
    Tan M, Pu J, Cheng S, Liu H, Zheng B.
    Ann Biomed Eng; 2015 Oct; 43(10):2416-28. PubMed ID: 25851469
    [Abstract] [Full Text] [Related]

  • 33. Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology.
    Betal D, Roberts N, Whitehouse GH.
    Br J Radiol; 1997 Sep; 70(837):903-17. PubMed ID: 9486066
    [Abstract] [Full Text] [Related]

  • 34. Use of border information in the classification of mammographic masses.
    Varela C, Timp S, Karssemeijer N.
    Phys Med Biol; 2006 Jan 21; 51(2):425-41. PubMed ID: 16394348
    [Abstract] [Full Text] [Related]

  • 35. 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 21; 40(5):857-76. PubMed ID: 7652012
    [Abstract] [Full Text] [Related]

  • 36. Digital mammography: observer performance study of the effects of pixel size on the characterization of malignant and benign microcalcifications.
    Chan HP, Helvie MA, Petrick N, Sahiner B, Adler DD, Paramagul C, Roubidoux MA, Blane CE, Joynt LK, Wilson TE, Hadjiiski LM, Goodsitt MM.
    Acad Radiol; 2001 Jun 21; 8(6):454-66. PubMed ID: 11394537
    [Abstract] [Full Text] [Related]

  • 37.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 38. Analysis of mammographic microcalcifications using gray-level image structure features.
    Dhawan AP, Chitre Y, Kaiser-Bonasso C.
    IEEE Trans Med Imaging; 1996 Jun 21; 15(3):246-59. PubMed ID: 18215906
    [Abstract] [Full Text] [Related]

  • 39. 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 21; 198(3):671-8. PubMed ID: 8628853
    [Abstract] [Full Text] [Related]

  • 40. Classification of microcalcifications in radiographs of pathologic specimens for the diagnosis of breast cancer.
    Wu YC, Freedman MT, Hasegawa A, Zuurbier RA, Lo SC, Mun SK.
    Acad Radiol; 1995 Mar 21; 2(3):199-204. PubMed ID: 9419548
    [Abstract] [Full Text] [Related]

    Page: [Previous] [Next] [New Search]
    of 10.