Statistical methods for polyhedral shape classification with incomplete data - application to cryo-electron tomographic images

Simple item page
dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
dc.check.opt-outNoen
dc.check.reasonNo embargo requireden
dc.check.typeNo Embargo Required
dc.contributor.advisorRoy Choudhury, Kingshuken
dc.contributor.advisorO'Sullivan, Finbarren
dc.contributor.authorBag, Sukantadev
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2016-07-11T11:43:47Z
dc.date.available2016-07-11T11:43:47Z
dc.date.issued2015
dc.date.submitted2015
dc.description.abstractA certain type of bacterial inclusion, known as a bacterial microcompartment, was recently identified and imaged through cryo-electron tomography. A reconstructed 3D object from single-axis limited angle tilt-series cryo-electron tomography contains missing regions and this problem is known as the missing wedge problem. Due to missing regions on the reconstructed images, analyzing their 3D structures is a challenging problem. The existing methods overcome this problem by aligning and averaging several similar shaped objects. These schemes work well if the objects are symmetric and several objects with almost similar shapes and sizes are available. Since the bacterial inclusions studied here are not symmetric, are deformed, and show a wide range of shapes and sizes, the existing approaches are not appropriate. This research develops new statistical methods for analyzing geometric properties, such as volume, symmetry, aspect ratio, polyhedral structures etc., of these bacterial inclusions in presence of missing data. These methods work with deformed and non-symmetric varied shaped objects and do not necessitate multiple objects for handling the missing wedge problem. The developed methods and contributions include: (a) an improved method for manual image segmentation, (b) a new approach to 'complete' the segmented and reconstructed incomplete 3D images, (c) a polyhedral structural distance model to predict the polyhedral shapes of these microstructures, (d) a new shape descriptor for polyhedral shapes, named as polyhedron profile statistic, and (e) the Bayes classifier, linear discriminant analysis and support vector machine based classifiers for supervised incomplete polyhedral shape classification. Finally, the predicted 3D shapes for these bacterial microstructures belong to the Johnson solids family, and these shapes along with their other geometric properties are important for better understanding of their chemical and biological characteristics.en
dc.description.sponsorshipScience Foundation Ireland (SFI Basic Science Research 07/REF/MA7F543); Science Foundation Ireland (SFI Math Initiative Grant); Science Foundation Ireland (SFI-PI Grant 11/1027)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBag, S. 2015. Statistical methods for polyhedral shape classification with incomplete data - application to cryo-electron tomographic images. PhD Thesis, University College Cork.en
dc.identifier.endpage193en
dc.identifier.urihttps://hdl.handle.net/10468/2854
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2015, Sukantadev Bag.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectStatisticsen
dc.subjectClassificationen
dc.subjectPolyhedronen
dc.subjectCryo-EMen
dc.subjectMetabolosomeen
dc.subjectTomographyen
dc.subjectShape classificationen
dc.subjectSupport vector machinesen
dc.subjectBayes classifieren
dc.subjectImage segmentationen
dc.thesis.opt-outfalse
dc.titleStatistical methods for polyhedral shape classification with incomplete data - application to cryo-electron tomographic imagesen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD (Science)en
ucc.workflow.supervisorkingshuk@ucc.ie
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Abstract_BagS_PhD2015.pdf
Size:
4.92 KB
Format:
Adobe Portable Document Format
Description:
Abstract
Thumbnail Image
Name:
BagS_PhD2015.pdf
Size:
5.22 MB
Format:
Adobe Portable Document Format
Description:
Full Text E-thesis
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
5.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Doctoral Theses
College of Science, Engineering and Food Science - Doctoral Theses
Mathematical Sciences - Doctoral Theses