A fuzzy logic approach to identifying brain structures in MRI using expert anatomic knowledge

Gilbert R. Hillman; Chih Wei Chang; Hao Ying Ying; John Yen; Leena Ketonen; Thomas A. Kent

doi:10.1006/cbmr.1999.1516

A fuzzy logic approach to identifying brain structures in MRI using expert anatomic knowledge

Gilbert R. Hillman, Chih Wei Chang, Hao Ying Ying, John Yen, Leena Ketonen, Thomas A. Kent

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

We report a novel computer method for automatic labeling of structures in 3D MRI data sets using expert anatomical knowledge that is coded in fuzzy sets and fuzzy rules. The method first identifies major structures and then uses spatial relationships to these landmarks to recognize other structures. This labeling process simulates the iterative process that we ourselves use to locate structures in images. We demonstrate its application in three data sets, labeling brain MRI by locating the longitudinal and lateral fissures and the central sulci and then determining boundaries for the frontal lobes. Our method is adaptable to the identification of other anatomical structures.

Original language	English (US)
Pages (from-to)	503-516
Number of pages	14
Journal	Computers and Biomedical Research
Volume	32
Issue number	6
DOIs	https://doi.org/10.1006/cbmr.1999.1516
State	Published - Dec 1999

Keywords

Brain atlas
Functional MRI
Fuzzy logic
Image segmentation
Magnetic resonance images

ASJC Scopus subject areas

Medicine (miscellaneous)

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10.1006/cbmr.1999.1516

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title = "A fuzzy logic approach to identifying brain structures in MRI using expert anatomic knowledge",

abstract = "We report a novel computer method for automatic labeling of structures in 3D MRI data sets using expert anatomical knowledge that is coded in fuzzy sets and fuzzy rules. The method first identifies major structures and then uses spatial relationships to these landmarks to recognize other structures. This labeling process simulates the iterative process that we ourselves use to locate structures in images. We demonstrate its application in three data sets, labeling brain MRI by locating the longitudinal and lateral fissures and the central sulci and then determining boundaries for the frontal lobes. Our method is adaptable to the identification of other anatomical structures.",

keywords = "Brain atlas, Functional MRI, Fuzzy logic, Image segmentation, Magnetic resonance images",

author = "Hillman, {Gilbert R.} and Chang, {Chih Wei} and Ying, {Hao Ying} and John Yen and Leena Ketonen and Kent, {Thomas A.}",

note = "Funding Information: The authors thank Professor Louis C. Sheppard for generous assistance. Some images were supplied by Dr. Harvey S. Levin, University of Maryland. Phillip Berryhill and Dr. Gerald Campbell provided additional anatomical expertise for identification of the frontal lobes. Image acquisition was supported by NIH NS-21889. Program development and research was supported in part by NIH R03 MH49552 and NSF IRI-9257293 and by the UTMB Center for Biomedical Engineering. This work was presented in part at ASNR 1996, at CFSA/IFIS/SOFT {\textquoteright}95 on Fuzzy Theory and Applications Taipei, and at FUZZ-IEEE/IFES 1996",

year = "1999",

month = dec,

doi = "10.1006/cbmr.1999.1516",

language = "English (US)",

volume = "32",

pages = "503--516",

journal = "Computers and Biomedical Research",

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publisher = "Academic Press",

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T1 - A fuzzy logic approach to identifying brain structures in MRI using expert anatomic knowledge

AU - Hillman, Gilbert R.

AU - Chang, Chih Wei

AU - Ying, Hao Ying

AU - Yen, John

AU - Ketonen, Leena

AU - Kent, Thomas A.

N1 - Funding Information: The authors thank Professor Louis C. Sheppard for generous assistance. Some images were supplied by Dr. Harvey S. Levin, University of Maryland. Phillip Berryhill and Dr. Gerald Campbell provided additional anatomical expertise for identification of the frontal lobes. Image acquisition was supported by NIH NS-21889. Program development and research was supported in part by NIH R03 MH49552 and NSF IRI-9257293 and by the UTMB Center for Biomedical Engineering. This work was presented in part at ASNR 1996, at CFSA/IFIS/SOFT ’95 on Fuzzy Theory and Applications Taipei, and at FUZZ-IEEE/IFES 1996

PY - 1999/12

Y1 - 1999/12

N2 - We report a novel computer method for automatic labeling of structures in 3D MRI data sets using expert anatomical knowledge that is coded in fuzzy sets and fuzzy rules. The method first identifies major structures and then uses spatial relationships to these landmarks to recognize other structures. This labeling process simulates the iterative process that we ourselves use to locate structures in images. We demonstrate its application in three data sets, labeling brain MRI by locating the longitudinal and lateral fissures and the central sulci and then determining boundaries for the frontal lobes. Our method is adaptable to the identification of other anatomical structures.

AB - We report a novel computer method for automatic labeling of structures in 3D MRI data sets using expert anatomical knowledge that is coded in fuzzy sets and fuzzy rules. The method first identifies major structures and then uses spatial relationships to these landmarks to recognize other structures. This labeling process simulates the iterative process that we ourselves use to locate structures in images. We demonstrate its application in three data sets, labeling brain MRI by locating the longitudinal and lateral fissures and the central sulci and then determining boundaries for the frontal lobes. Our method is adaptable to the identification of other anatomical structures.

KW - Brain atlas

KW - Functional MRI

KW - Fuzzy logic

KW - Image segmentation

KW - Magnetic resonance images

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JO - Computers and Biomedical Research

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A fuzzy logic approach to identifying brain structures in MRI using expert anatomic knowledge

Abstract

Keywords

ASJC Scopus subject areas

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