Estimating CT Image from MRI Data Using Structured Random Forest and Auto-Context Model

IEEE Transactions on Medical Imaging

Volumn 35, Issue 1, 2016, Pages 174-183

  Huynh, Tri ^a   Gao, Yaozong ^a,b   Kang, Jiayin ^a   Wang, Li ^a   Zhang, Pei ^a   Lian, Jun ^a   Shen, Dinggang ^a,c  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b Department of Computer Science ^*  (United States)

^c Korea University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; DIAGNOSIS; FORECASTING; ITERATIVE METHODS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY;

ATTENUATION CORRECTION; CLINICAL DIAGNOSIS; DEFORMABLE REGISTRATION; LEARNING-BASED METHODS; POSITRON EMISSION TOMOGRAPHY ATTENUATIONS; RADIATION EXPOSURE; RADIOTHERAPY TREATMENT PLANNING; SPATIAL INFORMATIONS;

COMPUTERIZED TOMOGRAPHY;

ATTENUATION; BRAIN; COMPUTER ASSISTED TOMOGRAPHY; EXPERIMENTAL MODEL; HUMAN; LEARNING; MODEL; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; PREDICTION; PROSTATE; RANDOM FOREST; REGISTRATION; ALGORITHM; BRAIN TUMOR; DECISION TREE; IMAGE PROCESSING; MALE; PATHOLOGY; PROCEDURES; PROSTATE TUMOR; STATISTICAL MODEL; X-RAY COMPUTED TOMOGRAPHY;

ALGORITHMS; BRAIN NEOPLASMS; DECISION TREES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MODELS, STATISTICAL; PROSTATIC NEOPLASMS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84959335586     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2461533     Document Type: Article

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