3D texture analysis of heterogeneous MRI data for diagnostic classification of childhood brain tumours

Studies in Health Technology and Informatics

Volumn 213, Issue , 2015, Pages 19-22

  Fetit, Ahmed E ^a,b   Novak, Jan ^b,c   Rodriguez, Daniel ^d   Auer, Dorothee P ^d,e   Clark, Chris A ^f   Grundy, Richard G ^d,e   Jaspan, Tim ^e   Peet, Andrew C ^b,c   Arvanitis, Theodoros N ^a,b  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b BIRMINGHAM CHILDREN S HOSPITAL   (United Kingdom)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^d UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^e NOTTINGHAM UNIVERSITY HOSPITALS NHS TRUST   (United Kingdom)

^f UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Machine Learning; MRI; Paediatric; SVM; Texture Analysis; Tumours

Indexed keywords

BRAIN; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; PEDIATRICS; STATISTICAL METHODS; SUPPORT VECTOR MACHINES; TUMORS;

DIFFERENT-MAGNETIC FIELDS; HIGHER ORDER STATISTICAL METHODS; LEAVE-ONE-OUT CROSS-VALIDATION (LOOCV); MAGNETIC RESONANCE IMAGING (MRI); PILOCYTIC ASTROCYTOMA; PIXEL DISTRIBUTION; QUALITATIVE ASSESSMENTS; TEXTURE ANALYSIS;

DIAGNOSIS;

ALGORITHM; BRAIN TUMOR; CHILD; CLINICAL TRIAL; COMPUTER ASSISTED DIAGNOSIS; FEMALE; HUMAN; MALE; MULTICENTER STUDY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; SUPPORT VECTOR MACHINE;

ALGORITHMS; BRAIN NEOPLASMS; CHILD; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; SUPPORT VECTOR MACHINE;

EID: 84952027684     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-61499-538-8-19     Document Type: Conference Paper

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