Red Blood Cell Image Classification Using Model Observers

dc.contributor.advisorGifford, Howard C.
dc.contributor.committeeMemberShevkoplyas, Sergey S.
dc.contributor.committeeMemberInce, Nuri F.
dc.creatorLin, Hongwei
dc.date.accessioned2018-11-30T18:27:34Z
dc.date.available2018-11-30T18:27:34Z
dc.date.createdMay 2018
dc.date.issued2018-05
dc.date.submittedMay 2018
dc.date.updated2018-11-30T18:27:34Z
dc.description.abstractHealthy red blood cells (RBCs) undergo a gradual morphological transformation during storage. From original healthy discocytes, RBCs gradually lose membrane surface area and cell volume, eventually turning into ghost (lysed cell). The degree of deterioration varies from cell to cell. These cells can be classified into 7 classes: discocyte, stomatocyte, echinocyte 1, echinocyte 2, echinocyte 3, sphero-echinocyte and spherocyte. Currently, researchers categorize a blood cell into different class by visual inspection. This work is laborious and inefficient. The limitation on the sample size is a problem for the evaluation of the blood unit quality and other research. Our objective was to test linear discriminants for classification work. The images we used were provided by Nate and his colleagues who fabricated a microfluidic device that can take image of RBCs. We extract features based on cell shape and surface texture. The overall accuracy of our system is 69.4%.
dc.description.departmentBiomedical Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/3513
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectImage classification
dc.subjectModel observer
dc.titleRed Blood Cell Image Classification Using Model Observers
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2020-05-01
local.embargo.terms2020-05-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentBiomedical Engineering, Department of
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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