dc.contributor.authorMahshid Farzinfar
dc.date.accessioned2012-09-24T05:42:51Z
dc.date.accessioned2017-07-23T08:34:33Z
dc.date.available2012-09-24T05:42:51Z
dc.date.available2017-07-23T08:34:33Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationMahshid, F. (2012). Novel level set based statistical frameworks for segmentation of MR images. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/50726
dc.description.abstractIn summary, two automated frameworks for segmentation of medical images are proposed. They are the joint curve evolution and the EM-joint shape based algorithms. In addition, the generalization of our frameworks to 3D models of multiple struc- tures. The resulting implementation is tested on a variety of studies to parceling of anatomical structures. The proposed algorithms can be used for surgery navigation and 3D visualization. They could also be applied to neuroscience studies in finding new diseases related to anatomical characteristics and the increase in reliability in diagnosing of illnesses such as schizophrenia.en_US
dc.format.extent202 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleNovel level set based statistical frameworks for segmentation of MR imagesen_US
dc.typeThesis
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.supervisorTeoh Eam Khwang (EEE)en_US
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en_US


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