Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/49841
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dc.contributor.authorYang, Jing
dc.date.accessioned2012-05-25T01:45:32Z
dc.date.available2012-05-25T01:45:32Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10356/49841
dc.description.abstractMedical image segmentation has many applications in health care industry. This project aims at applying the newly developed learning algorithm - Extreme Learning Machine (ELM) to 3D-medical images to segment liver image based on texture features. Further to liver segmentation, a comparison is made between direct ELM segmentation and ELM segmentation with adaboosting on liver tumor segmentation. A study of Active Shape Model (ASM) is carried out to regulate the shape obtained in liver segmentation. Multiple texture features for the medical image are extracted from 3D CT images before being trained and tested using ELM. A variety of methods are applied to measure the performance. Several pre-processing and post-processing methods such as alignment, morphological operations are used to improve the accuracy in classification. Active Shape Model allows the shape of testing data which is represented by a series of landmarks to change within constrain of the training model. The contour generated from ELM segmentation is used to obtain the initial landmarks for ASM.en_US
dc.format.extent103 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control engineeringen_US
dc.titleApplication of extreme learning machine with 3D image description for medical objects segmentationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorJiang Xudongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
dc.contributor.supervisor2Huang Weiminen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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