Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine

Computerized Medical Imaging and Graphics

Volumn 34, Issue 5, 2010, Pages 404-413

  Yamamoto, Daisuke ^a,d   Arimura, Hidetaka ^b   Kakeda, Shingo ^c   Magome, Taiki ^a   Yamashita, Yasuo ^a   Toyofuku, Fukai ^b   Ohki, Masafumi ^b   Higashida, Yoshiharu ^b   Korogi, Yukunori ^c  

^a KYUSHU UNIVERSITY   (Japan)

^b KYUSHU UNIVERSITY   (Japan)

^c UNIVERSITY OF OCCUPATIONAL AND ENVIRONMENTAL HEALTH   (Japan)

^d SIEMENS AG   (Germany)

Author keywords

Computerized method; Detection of multiple sclerosis; False positive reduction scheme; Level set method; Rule based method; Support vector machine

Indexed keywords

COMPUTERIZED METHODS; FALSE-POSITIVE REDUCTION; LEVEL SET METHOD; MULTIPLE SCLEROSIS; RULE-BASED METHOD;

DROP BREAKUP; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; SUPPORT VECTOR MACHINES; VECTORS;

LEVEL MEASUREMENT;

ADULT; AGED; ARTICLE; CASE REPORT; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; FALSE POSITIVE RESULT; FEMALE; HUMAN; INTERMETHOD COMPARISON; MULTIPLE SCLEROSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROCESS DEVELOPMENT; SENSITIVITY ANALYSIS; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; IMAGE ENHANCEMENT; LABORATORY DIAGNOSIS; MALE; MIDDLE AGED; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ADULT; AGED; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; FALSE POSITIVE REACTIONS; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MULTIPLE SCLEROSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77953023533     PISSN: 08956111     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2010.02.001     Document Type: Article

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