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Article: MRI brain image segmentation by multi-resolution edge detection and region selection

TitleMRI brain image segmentation by multi-resolution edge detection and region selection
Authors
Issue Date2000
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/compmedimag
Citation
Computerized Medical Imaging And Graphics, 2000, v. 24 n. 6, p. 349-357 How to Cite?
AbstractCombining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multi-resolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures. Copyright (C) 2000 Elsevier Science Ltd. | Combining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multiresolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures.
Persistent Identifierhttp://hdl.handle.net/10722/155136
ISSN
2015 Impact Factor: 1.385
2015 SCImago Journal Rankings: 0.630
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorTang, Hen_US
dc.contributor.authorWu, EXen_US
dc.contributor.authorMa, QYen_US
dc.contributor.authorGallagher, Den_US
dc.contributor.authorPerera, GMen_US
dc.contributor.authorZhuang, Ten_US
dc.date.accessioned2012-08-08T08:32:01Z-
dc.date.available2012-08-08T08:32:01Z-
dc.date.issued2000en_US
dc.identifier.citationComputerized Medical Imaging And Graphics, 2000, v. 24 n. 6, p. 349-357en_US
dc.identifier.issn0895-6111en_US
dc.identifier.urihttp://hdl.handle.net/10722/155136-
dc.description.abstractCombining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multi-resolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures. Copyright (C) 2000 Elsevier Science Ltd. | Combining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multiresolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures.en_US
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/compmedimagen_US
dc.relation.ispartofComputerized Medical Imaging and Graphicsen_US
dc.subject.meshBrain - Anatomy & Histologyen_US
dc.subject.meshHumansen_US
dc.subject.meshImage Processing, Computer-Assisteden_US
dc.subject.meshMagnetic Resonance Imaging - Methodsen_US
dc.subject.meshSubtraction Techniqueen_US
dc.titleMRI brain image segmentation by multi-resolution edge detection and region selectionen_US
dc.typeArticleen_US
dc.identifier.emailWu, EX:ewu1@hkucc.hku.hken_US
dc.identifier.authorityWu, EX=rp00193en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/S0895-6111(00)00037-9en_US
dc.identifier.pmid11008183-
dc.identifier.scopuseid_2-s2.0-0034333476en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034333476&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume24en_US
dc.identifier.issue6en_US
dc.identifier.spage349en_US
dc.identifier.epage357en_US
dc.identifier.isiWOS:000089804100002-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridTang, H=36827331000en_US
dc.identifier.scopusauthoridWu, EX=7202128034en_US
dc.identifier.scopusauthoridMa, QY=7402815617en_US
dc.identifier.scopusauthoridGallagher, D=7201610333en_US
dc.identifier.scopusauthoridPerera, GM=35596592800en_US
dc.identifier.scopusauthoridZhuang, T=7006739100en_US

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