Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

Magnetic Resonance in Medicine

Volumn 62, Issue 6, 2009, Pages 1609-1618

  Zacharaki, Evangelia I ^a,b   Wang, Sumei ^a   Chawla, Sanjeev ^a   Yoo, Dong Soo ^a,c   Wolf, Ronald ^a   Melhem, Elias R ^a   Davatzikos, Christos ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PATRAS   (Greece)

^c Dankook University   (South Korea)

Author keywords

Brain tumor; Classification; Feature selection; MRI; SVM; Texture; Tumor grade

Indexed keywords

BRAIN; COMPUTER AIDED DIAGNOSIS; FEATURE EXTRACTION; GRADING; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; PATHOLOGY; STATISTICAL METHODS; SUPPORT VECTOR MACHINES; TEXTURES; TUMORS; TURING MACHINES;

BINARY SUPPORT VECTOR MACHINES; BRAIN TUMORS; COMPUTER ASSISTED CLASSIFICATIONS; LEAVE-ONE-OUT CROSS VALIDATIONS; MULTI-CLASS CLASSIFICATION; RECURSIVE FEATURE ELIMINATION; TUMOR GRADES; WORLD HEALTH ORGANIZATION;

CLASSIFICATION (OF INFORMATION);

GADODIAMIDE;

ADOLESCENT; ADULT; AGED; ARTICLE; BOLUS INJECTION; BRAIN METASTASIS; BRAIN TUMOR; CANCER CLASSIFICATION; CANCER DIAGNOSIS; CANCER GRADING; CANCER MORPHOLOGY; CANCER SURGERY; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; DIFFERENTIAL DIAGNOSIS; FEMALE; GLIOBLASTOMA; GLIOMA; HUMAN; LOADING DRUG DOSE; MAJOR CLINICAL STUDY; MALE; MENINGIOMA; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PERFUSION WEIGHTED IMAGING; RELIABILITY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TUMOR BIOPSY; VALIDATION PROCESS;

EID: 73149086838     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.22147     Document Type: Article

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