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Title: Image processing techniques in regurgitation analysis
Researcher: Pinjari, Abdul Khayum
Guide(s): Sridevi P V
Giriprasad M N
Keywords: Image processing techniques
Regurgitation analysis
Valvular Regurgitation
Upload Date: 25-Jul-2013
University: Jawaharlal Nehru Technological University, Anantapuram
Completed Date: 13/06/2012
Abstract: Valvular Regurgitation (VR) is acknowledged as the fundamental reason for morbidity and mortality among cardiac patients. Although mere physical examination is enough for a clinician to find out the existence of regurgitation, diagnostic methods are necessary to estimate the seriousness of VR and the changes in cardiac chambers as a consequence of the volume overload. Recently echocardiography with Doppler proved to be the most useful to have the non-invasive recognition and assessment of severity besides etiology of the regurgitation of the valves. The measurements of the regurgitation help in evaluating the exact advancement of the disease which is crucial in deciding the opportune time for surgical treatment or any specific treatment. Doppler echocardiography plays the vital role in giving valuable information on the severity of VR. Today in clinical cardiology a very high quantification precision is needed for medical application which is provided by the Color Doppler Echocardiographic images. Comprehensive methods are presented herein to estimate and quantify Mitral Regurgitation (MR) and Aortic Regurgitation (AR) through the two dimensional color Doppler echocardiographic images which is the outcome of Proximal Flow Convergence (PFC) method. Experiments show better MR and AR quantification accuracy compared to the American Heart Association (AHA) values due to its flexibility in the selection of features and parameters.The process commences with the quantification of MR, using an image processing and Proximal Flow Convergence method in the first part of the thesis. In the preprocessing stage the color Doppler echocardiographic MR images which are in RGB color space are converted into Y Cb Cr color space. Followed by preprocessing, in the second stage, the converted image is segmented using non-linear anisotropic diffusion method for flow field measurements. The percentage of backward flow of blood is calculated from the segmented image.
Pagination: 128p.
Appears in Departments:Department of Electronics and Communication

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05_abstract.pdfAttached File139.63 kBAdobe PDFView/Open
04_acknowledgement.pdf83.38 kBAdobe PDFView/Open
08_chapter 2.pdf180.19 kBAdobe PDFView/Open
09_chapter 3.pdf620.21 kBAdobe PDFView/Open
10_chapter 4.pdf352.11 kBAdobe PDFView/Open
11_chapter 5.pdf465.01 kBAdobe PDFView/Open
12_chapter 6.pdf745.88 kBAdobe PDFView/Open
13_chapter 7.pdf139.92 kBAdobe PDFView/Open
15_publications.pdf139.55 kBAdobe PDFView/Open
07_chapter 1.pdf639.46 kBAdobe PDFView/Open
06_contents.pdf134.09 kBAdobe PDFView/Open
02_declaration & certificate.pdf531.32 kBAdobe PDFView/Open
03_list of tables &figures.pdf136.09 kBAdobe PDFView/Open
14_references.pdf180.43 kBAdobe PDFView/Open
01_title.pdf151.56 kBAdobe PDFView/Open

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