Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: Comparison with empiric and quantitative kinetic parameters

Academic Radiology

Volumn 11, Issue 12, 2004, Pages 1344-1354

  Szabó, Botond K ^a   Aspelin, Peter ^a   Wiberg, Maria Kristoffersen ^a  

^a KAROLINSKA INSTITUTE   (Sweden)

Author keywords

artificial neural networks; breast neoplasms; image segmentation; Magnetic resonance imaging

Indexed keywords

GADOLINIUM PENTETATE MEGLUMINE;

ADULT; AGED; ARTICLE; BODY WEIGHT; BREAST BIOPSY; BREAST LESION; BREAST TUMOR; CLINICAL ARTICLE; COMPARATIVE STUDY; CONFIDENCE INTERVAL; CORRELATION COEFFICIENT; EMPIRICISM; FEMALE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGING SYSTEM; KINETICS; LOGISTIC REGRESSION ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PARAMETER; PRIORITY JOURNAL;

ADULT; AGED; BREAST DISEASES; CONTRAST MEDIA; GADOLINIUM DTPA; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); RETROSPECTIVE STUDIES; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; USER-COMPUTER INTERFACE;

EID: 10444281666     PISSN: 10766332     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.acra.2004.09.006     Document Type: Article

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