Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/22092
Title: Development of medical image enhancement algorithms using edge information based methods
Researcher: Anand, S
Guide(s): Shantha, Selva Kumari R
Keywords: Adaptive histogram equalization
Computer Tomography
Hyperbolic Secant Square
Information and communication engineering
Medical image
Upload Date: 5-Aug-2014
University: Anna University
Completed Date: 01/10/2013
Abstract: Medical image enhancement improves the quality and facilitatesdiagnosis This thesis investigates seven methods of medical image enhancement by exploiting useful edge information Since edges have higher perceptual importance the edge information based enhancement process is interesting However determination of edge information is not an easy job In newlineaddition enhancement methods have limitations such as i Less effective for medical images containing wide range of anisotropic and directional features ii Noise influence Two dimensional 2D high pass HP filters wavelet transforms WT directionlet transform DT and non sub sampled contourlet transform NSCT are used to obtain the edge information Their multiple scale representation helps to suppress the noise In this thesis methods are developed to enhance Computer Tomography CT Chest X ray Retinal Mammogram Ultrasound and Blood smear images Sharpening is a simple enhancement process that emphasize high frequency component of the original image Sensitivity to noise and limited newlinedirections are the major drawbacks of conventional high pass filters used in image sharpening enhancement To combat with these difficulties 2D isotropic Hyperbolic Secant Square HBSS high pass filter is developed to enhance the CT images and retinal images by sharpening This 2D nonseparable HBSS filters provides directional selectivity and less noise sensitivity The improved HP filter responses are used to sharp the CT and retinal images The improved performance of this method compared with common unsharp masking USM in various levels of noise conditions Structural similarity measure SSIM qualitatively evaluates their newlineperformances newline newline
Pagination: xxiii, 183p.
URI: http://hdl.handle.net/10603/22092
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File32.9 kBAdobe PDFView/Open
02_certificate.pdf510.03 kBAdobe PDFView/Open
03_abstract.pdf13.41 kBAdobe PDFView/Open
04_acknowledgement.pdf6.35 kBAdobe PDFView/Open
05_contents.pdf97.07 kBAdobe PDFView/Open
06_chapter 1.pdf58.71 kBAdobe PDFView/Open
07_chapter 2.pdf258.73 kBAdobe PDFView/Open
08_chapter 3.pdf7.64 MBAdobe PDFView/Open
09_chapter 4.pdf8.02 MBAdobe PDFView/Open
10_chapter 5.pdf7.23 MBAdobe PDFView/Open
11_chapter 6.pdf7.67 MBAdobe PDFView/Open
12_chapter 7.pdf1.83 MBAdobe PDFView/Open
13_chapter 8.pdf14.2 MBAdobe PDFView/Open
14_chapter 9.pdf16.53 kBAdobe PDFView/Open
15_references.pdf50.19 kBAdobe PDFView/Open
16_publications.pdf6.57 kBAdobe PDFView/Open
17_vitae.pdf5.71 kBAdobe PDFView/Open


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