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ICSES Transactions on Image Processing and Pattern Recognition
Vol. 4, No. 4, Dec. 2018
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Editors' Choice |
a Annamalai University, Cuddalore, India
b Annamalai University, Chidambaram, India
c Annamalai University, Chidambaram, India
Mr. Rajalingam B
 orcid.org/0000-0002-8082-133X
 Corresponding Author
 Affiliation: Annamalai University, Cuddalore, India
 Tel: 9952088955
 E-mail: rajalingam35@gmail.com
 Biography: B.Rajalingam, Graduated B.Tech (IT) in Alpha College of Engineering Affiliated by Anna University, Chennai, Tamilnadu in 2010. He got ME (CSE) degree from Annamalai University, Chidambaram, Tamilnadu in 2012. He has worked as assistant professor at Aksheyaa college of Engineering, Maduranthakam, Tamilnadu from 2012 to 2016. Currently he is pursuing his Ph.D. in Department of Computer Science and Engineering at Annamalai University. He has published 15 papers in international journals and presented 12 papers in the national and international conferences. His research interest includes Medical Image Processing, Pattern Recognition and Artificial Intelligence.
Mr. Rajalingam B's publications in ICSES
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This article has been retracted by International Computer Science and Engineering Society (ICSES) because of ethical misconduct, scientific distortion, or administrative error, and cannot be downloaded and used for any purpose based on the violation in ICSES Ethics in Publicationcall_made |
Retraction Note by the Editor-in-Chief
Highlights and Novelties
1- A comparison on various traditional and hybrid medical image fusion algorithms is proposed.
2- It preserves all relevant and important information contained in input images.
3- Fused medical image facilitates accurate diagnosis and better clinical treatment analysis.
Manuscript Abstract
The extensive use of medical imaging has become a regular practice in modern medical health care centers. It is used almost in every stage of patient management system. However, it is intuition or expertise of physician to choose a modality or alternative modality wisely for managing the patient as single multimodal medical image has limitations. Therefore, single multimodal medical image is necessarily ruled out in diagnosis and treatment processes. Multimodal medical image fusion plays a significant role in the diagnosis, treatment planning, delivery of treatment, and review of patients response to the treatment. In this chapter proposed new fused image created from two multimodal medical images for the better visualization and interpretation of abnormalities in context with the purpose of accurate diagnosis, to prepare precise treatment plan, to classify the stages of diseases, and to review the effectiveness of the treatment. The proposed research work presents the feature based fusion algorithms in transforms domain to combine the relevant and complementary spectral features of two modalities namely computed tomography(CT) and magnetic resonance imaging (MRI), Positron Emission Tomography (PET) and Single photon Computed Tomography(SPECT). The traditional fusion algorithms compared with hybrid fusion algorithm. The hybrid multimodality medical image fusion is a powerful technique for analysis of lesions. In this chapter experimental results discovered that the proposed techniques provide better visualization of fused image and gives the superior results compared to various existing traditional algorithms
Keywords
Multimodal medical image fusion Additive wavelet transform Non subsampled shearlet transform Non subsampled contourlet transform Guided image filtering Curvelet transform
Copyright and Licence
Copyright © International Computer Science and Engineering Society (ICSES). This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0) license, supported by creativecommons.orgcall_madeCite this manuscript as
Rajalingam B, Priya R, Bhavani R, "Comparative Analysis for Various Traditional and Hybrid Multimodal Medical Image Fusion Techniques for Clinical Treatment Analysis," in Image Segmentation: A Guide to Image Mining, 1st ed., ITIPPR: ICSES, 2018, pp. 26-50.
For External Scientific Databeses
--BibTex--
@article{al._217
title="Comparative Analysis for Various Traditional and Hybrid Multimodal Medical Image Fusion Techniques for Clinical Treatment Analysis",
author="Rajalingam B",
author="Priya R",
author="Bhavani R",
journal="ICSES Transactions on Image Processing and Pattern Recognition (ITIPPR)",
volume="4",
number="4",
pages="26-50",
year="2018",
month="12",
day="30",
publisher= "International Computer Science and Engineering Society (ICSES)",
doi="",
url="http://www.i-cses.com/files/download.php?pID=217"}
--EndNote--
%0 Journal Article
%T Comparative Analysis for Various Traditional and Hybrid Multimodal Medical Image Fusion Techniques for Clinical Treatment Analysis
%A Rajalingam B
%A Priya R
%A Bhavani R
%J ICSES Transactions on Image Processing and Pattern Recognition (ITIPPR)
%V 4
%N 4
%P 26-2018
%D 2018-12-30
%I International Computer Science and Engineering Society (ICSES)
%U http://www.i-cses.com/files/download.php?pID=217
%8 2018-12-30
%R
%@ 2645-8071
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