Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/9851
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dc.coverage.spatialInformation and Communicationen_US
dc.date.accessioned2013-07-11T10:34:16Z-
dc.date.available2013-07-11T10:34:16Z-
dc.date.issued2013-07-11-
dc.identifier.urihttp://hdl.handle.net/10603/9851-
dc.description.abstractThe main objective of this work is to develop intermediate steps for automatic Karyotyping, aimed at improving the efficiency of the process. In Karyotyping, the input is an image of metaphase Chromosomes and the output is an ordered arrangement of Chromosomes, referred to as a Karyotype, which is used to ascertain genetic factors associated with diseases. The steps in automatic Karyotyping are: preprocessing, segmentation, feature extraction and classification. The factors that limit the efficiency of automatic Karyotyping systems are (1) The presence of unwanted components like Interphase cells that interfere with the segmentation of Chromosomes. (2) The presence of occlusion which hinders the extraction of features from the partially hidden Chromosomes.(3) The closely spaced features of intra-group Chromosomes in the feature space make it difficult to accurately classify them. This work proposes methods to address these problems. To efficiently remove the Interphase cells which exhibit similar characteristic as that of the Chromosomes, a segmentation technique termed as pixel ranking through multidirectional parameter evaluation has been proposed. To resolve occlusion, a stochastic search algorithm based on Evolutionary Computation and Particle Swarm Optimization has been proposed in this work. In this technique, a set of initial random solutions are created from the occluding Chromosomes and using the operators borrowed from Genetics and swarm behavior, the initial solutions are transformed into probable solutions for the problem over certain number of generations. Some of the intra-group Chromosome classes have overlapping and closely spaced features that limits the classification accuracy. The performance of this technique has been compared with other techniques. An improvement in the efficiency of automatic Karyotyping using the proposed techniques, by comparing with the performance of an existing automatic Karyotyping package, has been established.en_US
dc.format.extentxvi, 177p.en_US
dc.languageEnglishen_US
dc.relationNo. of references 114en_US
dc.rightsuniversityen_US
dc.titleDevelopment and performance evaluation of processing techniques for automatic Karyotypingen_US
dc.creator.researcherGanesh Vaidyanathan Sen_US
dc.subject.keywordKaryotypingen_US
dc.subject.keywordChromosomes-
dc.subject.keywordSegmentation technique-
dc.subject.keywordEvolutionary Computation-
dc.subject.keywordParticle Swarm Optimization-
dc.description.noteAppendices p. 116-164, References p. 165-174, List of publications p. 175-177en_US
dc.contributor.guideKumaravel Nen_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Information and Communication Engineeringen_US
dc.date.registered03/09/2010en_US
dc.date.completed06/07/2011en_US
dc.date.awarded2011en_US
dc.format.dimensions--en_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf951.19 kBAdobe PDFView/Open
03_abstract.pdf16.09 kBAdobe PDFView/Open
04_acknowledgement.pdf15.63 kBAdobe PDFView/Open
05_contents.pdf56.79 kBAdobe PDFView/Open
06_chapter 1.pdf170.45 kBAdobe PDFView/Open
07_chapter 2.pdf274.51 kBAdobe PDFView/Open
08_chapter 3.pdf384.87 kBAdobe PDFView/Open
09_chapter 4.pdf280.75 kBAdobe PDFView/Open
10_chapter 5.pdf442.61 kBAdobe PDFView/Open
11_chapter 6.pdf43.96 kBAdobe PDFView/Open
12_appendices 1 to 3.pdf2.14 MBAdobe PDFView/Open
13_references.pdf58.8 kBAdobe PDFView/Open
14_publications.pdf19.08 kBAdobe PDFView/Open
15_vitae.pdf12.6 kBAdobe PDFView/Open


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