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dc.contributor.authorBelous, G
dc.contributor.authorBusch, A
dc.contributor.authorRowlands, D
dc.contributor.editorDavid Al-Dabass, Ismail Saad, Khairul Anuar Mohamad, Mohd Hanafi Ahmad Hijazi
dc.date.accessioned2018-02-13T03:49:04Z
dc.date.available2018-02-13T03:49:04Z
dc.date.issued2014
dc.identifier.isbn9781479932511
dc.identifier.urihttp://hdl.handle.net/10072/112836
dc.description.abstractThis paper presents a model-based learning segmentation algorithm to detect the left ventricle (LV) boundary of the heart from ultrasound (US) images by combining a random forest classifier with an active shape model (ASM). Our method applies an ASM for initial detection of the LV landmarks. Each landmark is subsequently directed radially inward or outward as a result of the random forest classifier identifying the landmark as outside or inside the LV boundary, respectively. This is done while preserving the shape characteristics obtained from the ASM. Our objective is to evaluate the combined application of a random forest classifier with an ASM for detecting the LV boundary with US images. Accuracy of this method is evaluated by comparing both our method and ASM to LV contours traced by an expert. A dataset of 85 randomly selected patient studies was chosen. The method exhibits improved accuracy compared to the ASM, producing a global overlap coefficient of 90.09% compared to 83.8% obtained with an active shape model.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.publisher.urihttp://ieeexplore.ieee.org/abstract/document/6959936/
dc.relation.ispartofconferencenameAIMS 2013
dc.relation.ispartofconferencetitleProceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013
dc.relation.ispartofdatefrom2013-12-03
dc.relation.ispartofdateto2013-12-05
dc.relation.ispartoflocationSabah, Malaysia
dc.relation.ispartofpagefrom315
dc.relation.ispartofpageto319
dc.subject.fieldofresearchComputer vision
dc.subject.fieldofresearchImage processing
dc.subject.fieldofresearchcode460304
dc.subject.fieldofresearchcode460306
dc.titleSegmentation of the Left Ventricle From Ultrasound Using Random Forest with Active Shape Model
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.hasfulltextNo Full Text
gro.griffith.authorRowlands, David D.
gro.griffith.authorBusch, Andrew W.


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    Contains papers delivered by Griffith authors at national and international conferences.

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