Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/22402
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dc.coverage.spatialInformation and Communication Engineeringen_US
dc.date.accessioned2014-08-07T07:09:34Z-
dc.date.available2014-08-07T07:09:34Z-
dc.date.issued2014-08-07-
dc.identifier.urihttp://hdl.handle.net/10603/22402-
dc.description.abstractThe last few decades have witnessed a remarkable advancement in newlinethe field of image compression Image compression plays a key role in newlinemedical image analysis Compression methods are essential in many medical newlineapplications to ensure fast searching of images future storage and analysis of newlinemeasured data Image compression can be either lossy or lossless In lossless newlinecompression the reconstructed image exactly resembles the original image In newlinelossy compression as reconstruction is not possible it leads to information loss newlinein the reconstructed image newlineRecently many methods proposed for fractal image compression newlinehave gained attention for achieving high compression ratios However it newlineresults in information loss and consumes more encoding time In order to newlineovercome the above stated issues a quasi lossless technique is required that newlinewill compress the medical image preserving image quality providing high newlinecompression ratio newline newlineen_US
dc.format.extentxix, 133p.en_US
dc.languageEnglishen_US
dc.relation-en_US
dc.rightsuniversityen_US
dc.titleQuasi lossless fractal image compression of medical images based on self organising neural networken_US
dc.title.alternative-en_US
dc.creator.researcherBhavani, Sen_US
dc.subject.keywordImage compressionen_US
dc.subject.keywordInformation and communication engineeringen_US
dc.subject.keywordMagnetic Resonance Imagesen_US
dc.description.noteAppendix p.80-123, References p.124-130.en_US
dc.contributor.guideThanushkodi, Ken_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Information and Communication Engineeringen_US
dc.date.registeredn.d.en_US
dc.date.completed01/07/2012en_US
dc.date.awarded30/07/2012en_US
dc.format.dimensions23cm.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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01_title.pdfAttached File50.02 kBAdobe PDFView/Open
02_certificate.pdf642.53 kBAdobe PDFView/Open
03_abstract.pdf22.37 kBAdobe PDFView/Open
04_acknowledgement.pdf19.85 kBAdobe PDFView/Open
05_contents.pdf54.36 kBAdobe PDFView/Open
06_chapter 1.pdf74.34 kBAdobe PDFView/Open
07_chapter 2.pdf77.13 kBAdobe PDFView/Open
08_chapter 3.pdf145.91 kBAdobe PDFView/Open
09_chapter 4.pdf1.52 MBAdobe PDFView/Open
10_chapter 5.pdf1.92 MBAdobe PDFView/Open
11_chapter 6.pdf30.81 kBAdobe PDFView/Open
12_appendix.pdf495.79 kBAdobe PDFView/Open
13_references.pdf48.27 kBAdobe PDFView/Open
14_publications.pdf32.56 kBAdobe PDFView/Open
15_vitae.pdf17.52 kBAdobe PDFView/Open


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