Non-invasive image-based approach for early detection of acute renal rejection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6361 LNCS, Issue PART 1, 2010, Pages 10-18

  Khalifa, Fahmi ^a   El Baz, Ayman ^a   Gimel'farb, Georgy ^b   El Ghar, Mohammed Abu ^c  

^a University of Louisville   (United States)

^b UNIVERSITY OF AUCKLAND   (New Zealand)

^c MANSOURA UNIVERSITY   (Egypt)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE REJECTION; AUTOMATIC CLASSIFICATION; CONTRAST AGENT; CURRENT TECHNOLOGY; DEFORMABLE MODELS; DESCRIPTORS; DYNAMIC CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING; EARLY DETECTION; IMAGE-BASED; ISO-CONTOURS; KIDNEY DYSFUNCTION; LAPLACE'S EQUATIONS; LEVEL SETS; LOCAL OBJECT; NON-INVASIVE; NUCLEAR IMAGING; PATIENT BREATHING; SPEED FUNCTION; STATISTICAL SHAPES; TARGET OBJECT; VISUAL APPEARANCE;

DEFORMATION; EQUATIONS OF MOTION; LAPLACE EQUATION; LAPLACE TRANSFORMS; MEDICAL COMPUTING; MEDICAL IMAGING; RESONANCE; ULTRASONIC IMAGING; ULTRASONOGRAPHY; VISUALIZATION;

MAGNETIC RESONANCE IMAGING;

CONTRAST MEDIUM;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; GRAFT REJECTION; HUMAN; IMAGE ENHANCEMENT; KIDNEY TRANSPLANTATION; MAGNETIC RESONANCE ANGIOGRAPHY; METHODOLOGY; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; CONTRAST MEDIA; GRAFT REJECTION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; KIDNEY TRANSPLANTATION; MAGNETIC RESONANCE ANGIOGRAPHY; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84879435756     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15705-9_2     Document Type: Conference Paper

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