Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/34136
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dc.coverage.spatialCertain investigations on application Of multiresolution analysis and Advanced neural networks on Mammograms for early detection of Breast canceren_US
dc.date.accessioned2015-02-10T06:38:34Z-
dc.date.available2015-02-10T06:38:34Z-
dc.date.issued2015-02-10-
dc.identifier.urihttp://hdl.handle.net/10603/34136-
dc.description.abstractBreast cancer which continues to be a significant public health problem newlinearound the world is the most prevalent cancer among women Breast cancer is most newlineeffectively treated when detected at an early stage and the survival probability of newlinethe patient is dependent on the stage at which it is diagnosed Digital X ray newlinemammographic method is a specialized radiographic imaging technique for newlinediagnosis of breast diseases It identifies the morphological differences that indicate newlinethe presence of breast cancer such as masses microcalcifications, and architectural newlinedistortions Detection of breast cancer at an early stage requires mammographic newlineimages which have high sensitivity and specificity with a relatively low radiation newlinedose This imposes challenging requirements for interactive and intelligent medical newlineimage analysis Computerized medical image analysis method can provide effective newlinetools to help differential diagnosis intervention and treatment monitoring newlineIn the literature various Computer Aided Diagnostic CAD systems newlineare described to detect the presence of breast cancer and to classify them as newlinebenign or malignant A detailed review of existing methods is presented in order to newlineprovide an insight about the state of the art The objective of this research is to newlinedesign advanced image processing techniques and algorithms that can aid breast newlinecancer detection at an early stage newlineMammography remains the most effective diagnostic technique for newlineearly breast cancer detection however not all types of breast cancer can be newlinedetected by mammograms newline newlineen_US
dc.format.extentxix, 140p.en_US
dc.languageEnglishen_US
dc.relationp128-138.en_US
dc.rightsuniversityen_US
dc.titleCertain investigations on application Of multiresolution analysis and Advanced neural networks on Mammograms for early detection of Breast canceren_US
dc.title.alternativeen_US
dc.creator.researcherMalar Een_US
dc.subject.keywordBreast canceren_US
dc.subject.keywordComputer Aided Diagnosticen_US
dc.description.noteappendix p118-127, reference p128-138.en_US
dc.contributor.guideKandaswamy Aen_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Electrical and Electronics Engineeringen_US
dc.date.registeredn.d,en_US
dc.date.completed01/10/2014en_US
dc.date.awarded30/10/2014en_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 Electrical and Electronics Engineering

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01_title.pdfAttached File44.77 kBAdobe PDFView/Open
02_certificate.pdf1.15 MBAdobe PDFView/Open
03_abstract.pdf29.87 kBAdobe PDFView/Open
04_acknowledgement.pdf41.71 kBAdobe PDFView/Open
05_content.pdf84.48 kBAdobe PDFView/Open
06_chapter1.pdf416.39 kBAdobe PDFView/Open
07_chapter2.pdf389 kBAdobe PDFView/Open
08_chapter3.pdf706.33 kBAdobe PDFView/Open
09_chapter4.pdf510.12 kBAdobe PDFView/Open
10_chapter5.pdf862.06 kBAdobe PDFView/Open
11_chapter6.pdf486.05 kBAdobe PDFView/Open
12_chapter7.pdf38.23 kBAdobe PDFView/Open
13_appendix.pdf99.51 kBAdobe PDFView/Open
14_reference.pdf505.18 kBAdobe PDFView/Open
15_publication.pdf33.39 kBAdobe PDFView/Open


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