Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/49416
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dc.coverage.spatialCertain improvements in support Vector machine kernel using hybrid Evolutionary algorithms for Medical image classificationen_US
dc.date.accessioned2015-09-11T05:14:07Z-
dc.date.available2015-09-11T05:14:07Z-
dc.date.issued2015-09-11-
dc.identifier.urihttp://hdl.handle.net/10603/49416-
dc.description.abstractWith the technological advances in digital imaging large newlinecollections of medical images are generated and stored in the medical newlinedatabases X ray CT MRI PET ultrasound images are a major source of newlineanatomical and functional information needed for diagnosis research and newlineteaching With the amount of digital medical images generated the medical newlinedatabase is huge and multi varied The need to search data collections newlineefficiently requires good image retrieval systems Content Based Image newlineRetrieval CBIR helps to retrieve images from the database similar to the newlinequery image are now widely used for medical image retrieval CBIR is newlinebasically a two step process such as Feature Extraction and Feature Matching newlineFeature Extraction is the process to extract image features to a distinguishable newlineExtend Information extracted from images such as colour texture and shape newlineare known as feature vectors This process is done on both query image and newlineimages in databases Feature Matching involves comparing the features of newlineboth the images in the database newlineIncorporating adaptable techniques to process the images of varied newlinecharacteristics and classes is the biggest issue in CBIR systems In this newlineresearch the problem of retrieving medical images from a multi varied newlinedatabase is addressed The underlying objective of this research work is to newlineinvestigate the efficacy of the various classification algorithms used in content newlinebased image retrieval and proposes a novel classification algorithm newline newlineen_US
dc.format.extentxx, 131p.en_US
dc.languageEnglishen_US
dc.relationp119-130.en_US
dc.rightsuniversityen_US
dc.titleCertain improvements in support Vector machine kernel using hybrid Evolutionary algorithms for Medical image classificationen_US
dc.title.alternativeen_US
dc.creator.researcherRenukadevi N Ten_US
dc.subject.keywordContent Based Image Retrievalen_US
dc.subject.keywordMedical databases X rayen_US
dc.description.notereference p119-130.en_US
dc.contributor.guideThangaraj Pen_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Science and Humanitiesen_US
dc.date.registeredn.d,en_US
dc.date.completed01/08/2014en_US
dc.date.awarded30/08/2014en_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 Science and Humanities

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01_title.pdfAttached File25.18 kBAdobe PDFView/Open
02_certificate.pdf61.56 kBAdobe PDFView/Open
03_abstract.pdf15.81 kBAdobe PDFView/Open
04_acknowledgement.pdf6.24 kBAdobe PDFView/Open
05_content.pdf39.96 kBAdobe PDFView/Open
06_chapter1.pdf160.25 kBAdobe PDFView/Open
07_chapter2.pdf86.43 kBAdobe PDFView/Open
08_chapter3.pdf2.54 MBAdobe PDFView/Open
09_chapter4.pdf1.73 MBAdobe PDFView/Open
10_chapter5.pdf1.45 MBAdobe PDFView/Open
11_chapter6.pdf786.2 kBAdobe PDFView/Open
12_chapter7.pdf345.24 kBAdobe PDFView/Open
13_reference.pdf62.15 kBAdobe PDFView/Open
14_publication.pdf21.55 kBAdobe PDFView/Open


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