Journal article
Knowledge-based interpretation of MR brain images
IEEE transactions on medical imaging, Vol.15(4), pp.443-452
1996
DOI: 10.1109/42.511748
PMID: 18215926
Abstract
he authors have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc. in magnetic resonance (MR) brain images. The authors' method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop. The authors' method was trained in 20 individual T1-weighted MR images. Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards. The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set. Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r=0.99, y=0,95x-2.1). Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5/spl plusmn/0.6 pixels. The authors' GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.
Details
- Title: Subtitle
- Knowledge-based interpretation of MR brain images
- Creators
- M SONKA - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, United StatesS. K TADIKONDA - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, United StatesS. M COLLINS - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, United States
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on medical imaging, Vol.15(4), pp.443-452
- DOI
- 10.1109/42.511748
- PMID
- 18215926
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Publisher
- Institute of Electrical and Electronics Engineers; New York, NY
- Language
- English
- Date published
- 1996
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984047864502771
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