Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/15080
Title: Computer aided diagnosis system for automatic detection of brain tumor through magnetic resonance image
Researcher: Nandhagopal,N
Guide(s): Karnan,M
Keywords: brain tumor,
Computer aided diagnosis system,
computerized brain image
magnetic resonance image
radiologist s attention
Upload Date: 15-Jan-2014
University: Manonmaniam Sundaranar University
Completed Date: April 2012
Abstract: Computer Aided Diagnosis system has been developed for Automatic Detection of newlineBrain Tumor through MRI.Improving the ability to identify early-stage tumors is an newlineimportant goal for physicians, because early detection of lung cancer is a key factor in newlineproducing successful treatments. Computer-aided diagnosis (CAD) involves the use of newlinecomputers to bring suspicious areas on a medical image to a radiologist s attention. CAD for newlinecancer detection in medical images starts with a digital image. The computer scans and newlinemarks suspicious are looking areas in the image. Radiologists can then focus on those areas newlineand decide if a biopsy or further evaluation is needed. newlineComputer-Aided Diagnosis (CAD) and automated prescreening by computer are two newlineways to potentially counter many of the problems that would result from screening a large newlinenumber of human for brain tumor using MRI. Even if there is no large-scale screening newlineprogram, computerized brain image analysis could perhaps be used to improve the quality of newlineconventional brain tumor analysis. In a CAD scenario, computerized image analysis is used newlineto suggest possible affected tumor regions in the image so that the radiologist can then newlineexamine these regions more carefully. Additionally, image-processing operations such as newlinecontrast enhancement and edge detection can be applied to the image at the request of the newlinehuman reader before a decision is made. The automated prompting and the additional newlineinformation provided by computerized image analysis should also result in grater newlinerepeatability and uniformity in the standard care. It should also result in some increase in newlinesensitivity for a given level of specificity; i.e., more tumors detected the same biopsy rate. newline
Pagination: xvi, 217p.
URI: http://hdl.handle.net/10603/15080
Appears in Departments:Centre for Information Technology and Engineering

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01_titles.pdfAttached File127.14 kBAdobe PDFView/Open
02_certificate.pdf79 kBAdobe PDFView/Open
03_declaration.pdf65.25 kBAdobe PDFView/Open
04_acknowledgement.pdf61.48 kBAdobe PDFView/Open
05_contents.pdf80.96 kBAdobe PDFView/Open
06_list of tables.pdf71.71 kBAdobe PDFView/Open
07_abbreviation.pdf93.81 kBAdobe PDFView/Open
08_chapter 1.pdf646.83 kBAdobe PDFView/Open
09_chapter 2.pdf202.77 kBAdobe PDFView/Open
10_chapter 3.pdf1.88 MBAdobe PDFView/Open
11_chapter 4.pdf4.54 MBAdobe PDFView/Open
12_chapter 5.pdf1.89 MBAdobe PDFView/Open
13_chapter 6.pdf163.66 kBAdobe PDFView/Open
14_chapter 7.pdf420.42 kBAdobe PDFView/Open
15_chapter 8.pdf92.71 kBAdobe PDFView/Open
16_references.pdf232.01 kBAdobe PDFView/Open
17_list of publication.pdf93.97 kBAdobe PDFView/Open


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