Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/24151
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dc.coverage.spatialMRI brain tumor segmentation using combined approachesen_US
dc.date.accessioned2014-08-27T06:51:39Z-
dc.date.available2014-08-27T06:51:39Z-
dc.date.issued2014-08-27-
dc.identifier.urihttp://hdl.handle.net/10603/24151-
dc.description.abstractImage segmentation is the process by which the original image is subdivided into its constituent regions or objects The level to which the subdivision is achieved depends on the problem being solved Simply the inputs of the image segmentation are images but the outputs are attributes extracted from images Image segmentation is an important initial task for higher level image processing such as object recognition or object tracking In medical image segmentation structures or objects of interest for segmentation include organs parts of pathology abnormalities such as brain tumors and brain tissues This research work proposed the methods to segment tumors on newlinebrain MRI images accurately by combining both region and boundary based segmentation methods The region based methods have drawbacks at the boundary of tumors and boundary based methods suffer from contour initialization problem In the combined approach the research work used the benefit of boundary based and region based method to overcome the problem of each other The region based method is used to initialize the boundary based method and the precise segmentation properties of the boundary based method used to overcome the problem of region based method at border of tumors newlineen_US
dc.format.extentxxiii, 198p.en_US
dc.languageEnglishen_US
dc.relationp.180-195.en_US
dc.rightsuniversityen_US
dc.titleCertain investigations on MRI brain tumor segmentation using combined approachesen_US
dc.title.alternativeen_US
dc.creator.researcherRajendran Aen_US
dc.subject.keywordBrain tissuesen_US
dc.subject.keywordBrain tumorsen_US
dc.subject.keywordInformation and communication engineeringen_US
dc.subject.keywordMRI imagesen_US
dc.description.noteReferences p.180-195,en_US
dc.contributor.guideDhanasekaran Ren_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.completed01/11/2013en_US
dc.date.awarded30/11/2013en_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 File36.24 kBAdobe PDFView/Open
02_certificate.pdf141.84 kBAdobe PDFView/Open
03_abstract.pdf23.51 kBAdobe PDFView/Open
04_acknowledgement.pdf20.85 kBAdobe PDFView/Open
05_contents.pdf57.03 kBAdobe PDFView/Open
06_chapter1.pdf49 kBAdobe PDFView/Open
07_chapter2.pdf886.21 kBAdobe PDFView/Open
08_chapter3.pdf472.47 kBAdobe PDFView/Open
09_chapter4.pdf906.9 kBAdobe PDFView/Open
10_chapter5.pdf318.36 kBAdobe PDFView/Open
11_chapter6.pdf291.8 kBAdobe PDFView/Open
12_chapter7.pdf311.93 kBAdobe PDFView/Open
13_chapter8.pdf553.04 kBAdobe PDFView/Open
14_chapter9.pdf18.28 kBAdobe PDFView/Open
15_references.pdf92.97 kBAdobe PDFView/Open
16_publications.pdf37.93 kBAdobe PDFView/Open
17_vitae.pdf20.23 kBAdobe PDFView/Open


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