Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/54361
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dc.contributor.authorSong, Menglu.
dc.date.accessioned2013-06-19T06:47:23Z
dc.date.available2013-06-19T06:47:23Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10356/54361
dc.description.abstractThis report presents a semi-automatic method for abdominal wall segmentation from 3D Computed Tomography (CT) images using Active Shape Model (ASM) as well as Extreme Learning Machine (ELM). As a top-down approach, ASM builds a shape model from a set of sample images where the abdominal wall has been annotated manually. By iteratively adjusting the pose and shape parameters of the model, a best fit to a new image can be found such that the model can describe abdominal wall as accurately as possible. On the other hand, ELM as a bottom-up approach is adopted to distinguish wall voxels with other non-wall voxels. Partial wall can be extracted to deform the model in the fitting process of ASM. The experimental result shows that the proposed ASM and ELM method is capable of delivering reasonably good performance for abdominal wall segmentation. To the author’s best knowledge, this is the first work that applies ASM and ELM for abdominal wall segmentation.en_US
dc.format.extent53 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Bioengineeringen_US
dc.titleDeformable abdominal wall segmentationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLin Zhipingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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