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dc.contributor.advisorRoy E. Welsch, Rami S. Mangoubi and Mukund N. Desai.en_US
dc.contributor.authorJeffreys, Christopher G. (Christopher Grey), 1979-en_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2005-05-17T14:48:08Z
dc.date.available2005-05-17T14:48:08Z
dc.date.copyright2004en_US
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/16651
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004.en_US
dc.descriptionIncludes bibliographical references (p. 117-121).en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.description.abstractStem (cell research is one of the most promising and cutting-edge fields i the miedical sciences. It is believed that this innovative research will lead to life-saving treatments in the coming years. As part of their work, stem cell researchers must first determine which of their stem cell colonies are of sufficiently high quality to be suitable for experimental studies and therapeutic treatments. Since colony texture is a major discriminating feature in determining quality. we introduce a non-invasive, semi-automated texture-based stem cell colony classification methodology to aid researchers in colony quality control. We first consider the general problem of textural image segmentation. In a new approach to this problem. we characterize image texture by the subband energies of the image's wavelet decomposition, and we employ a non-parametric support vector machine to perform the classification that yields the segmentation. We also adapt a parametric wavelet-based classifier that utilizes the Kullback-Leibler distance. We apply both methods to a set of benchmark textural images, report low segmentation error rates and comment on the applicability of and tradeoffs between the non-parametric and parametric segmentation methods.en_US
dc.description.abstract(cont.) We then apply the two classifiers to the segmentation of stem cell colony images into regions of varying quality. This provides stem cell researchers with a rich set of descriptive graphical representations of their colonies to aid in quality control. From these graphical representatiolns, we extract colony-wise textural features to which we add colony-wise border features. Taken together, these features characterize overall colony quality. Using these features as inputs to a multiclass support vector machine, we successfully categorize full stem cell colonies into several quality categories. This methodology provides stem cell researchers with a novel, non-invasive quantitative quality control tool.en_US
dc.description.statementofresponsibilityby Christopher G. Jeffreys.en_US
dc.format.extent121 p.en_US
dc.format.extent2545793 bytes
dc.format.extent3318038 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectOperations Research Center.en_US
dc.titleSupport vector machine and parametric wavelet-based texture classification of stem cell imagesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center
dc.contributor.departmentSloan School of Management
dc.identifier.oclc56472941en_US


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