Spine detection in CT and MR using iterated marginal space learning

Medical Image Analysis

Volumn 17, Issue 8, 2013, Pages 1283-1292

  Michael Kelm, B ^a   Wels, Michael ^a   Kevin Zhou, S ^b   Seifert, Sascha ^a   Suehling, Michael ^a   Zheng, Yefeng ^b   Comaniciu, Dorin ^b  

^a SIEMENS AG   (Germany)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

Generative discriminative detection; Marginal space learning; Spinal disk segmentation; Spine detection; Vertebra segmentation

Indexed keywords

COMPUTER AIDED DIAGNOSIS(CAD); DIMENSIONAL TRANSFORMATION; FALSE POSITIVE DETECTION; HYPOTHESIS RESOLUTIONS; MARGINAL SPACE LEARNING; ROBUST APPROACHES; SPINE DETECTIONS; THREE-DIMENSIONAL POSITIONING;

COMPUTER AIDED DIAGNOSIS; ITERATIVE METHODS; MAGNETIC RESONANCE; MUSCULOSKELETAL SYSTEM;

COMPUTERIZED TOMOGRAPHY;

ACCURACY; ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; ERROR; FALSE POSITIVE RESULT; INTERVERTEBRAL DISK; LUMBAR SPINE; MARGINAL SPACE LEARNING; NUCLEAR MAGNETIC RESONANCE; ORIENTATION; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SPATIAL LEARNING; SPINE; VERTEBRA; VERTEBRA BODY;

GENERATIVE-DISCRIMINATIVE DETECTION; MARGINAL SPACE LEARNING; SPINAL DISK SEGMENTATION; SPINE DETECTION; VERTEBRA SEGMENTATION;

ALGORITHMS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPINAL NEOPLASMS; SPINE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84884968241     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2012.09.007     Document Type: Article

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