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Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures

by S.asif Hussain, M.n.giri Prasad,, D. Satyanarayana
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 5
Year of Publication: 2012
Authors: S.asif Hussain, M.n.giri Prasad,, D. Satyanarayana
10.5120/7185-9918

S.asif Hussain, M.n.giri Prasad,, D. Satyanarayana . Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures. International Journal of Computer Applications. 47, 5 ( June 2012), 22-29. DOI=10.5120/7185-9918

@article{ 10.5120/7185-9918,
author = { S.asif Hussain, M.n.giri Prasad,, D. Satyanarayana },
title = { Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 5 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 22-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number5/7185-9918/ },
doi = { 10.5120/7185-9918 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:41:06.716407+05:30
%A S.asif Hussain
%A M.n.giri Prasad,
%A D. Satyanarayana
%T Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 5
%P 22-29
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation algorithms based on ROI typically rely on the homogeneity of image intensities. CT scanner is dedicated as research Scanner which has been developed in view of imaging applications. A key Feature of the work is to use Empirical system to achieve resolution recovery with novel region based method. This method identifies local intensity cluster with local clustering criterion function with respect to neighborhood center. Reconstruction quality is analyzed quantitatively in terms bias field correction for intensity inhomogenity correction. This method is valid on synthetic images of various imaging modalities. A significant improvement in reconstruction quality can be realized by faster and more accurate visual quality quantitative measures where Reconstruction quality is analyzed quantitatively in terms of bias-variance measures (bar phantom) and mean square error (lesion phantom). However, with the inclusion of the empirical kernel, the iterative algorithms provide superior reconstructions compared to FBP, both in terms of visual quality and quantitative measures. Simulated results show improved tumor bias and variance characteristics with the proposed algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Intensity Inhomogeneities Empirical System Kernel Bias-variance Iterative Algorithms

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