Home navigate_next Journals navigate_next ITIPPR navigate_next Vol. 4, No. 4navigate_next Comparative Analysis for Various Traditional and Hybrid Multimodal Medical Image Fusion Techniques for Clinical Treatment Analysis
ICSES Transactions on Image Processing and Pattern Recognition
Vol. 4, No. 4, Dec. 2018


Comparative Analysis for Various Traditional and Hybrid Multimodal Medical Image Fusion Techniques for Clinical Treatment Analysis

Book Chapter of Image Segmentation: A Guide to Image Mining
339
Visits
58
Downloads
Rajalingam B a,mail_outline, Priya R b, Bhavani R c
a Annamalai University, Cuddalore, India
b Annamalai University, Chidambaram, India
c Annamalai University, Chidambaram, India

 

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 was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors. This manuscript is published in Open-Access manner based on the copyright licence of Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0).

 

Cite 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-- --EndNote-- --Dublin--
star The old version of this page can be accessed via here, and is supported till 2020.
Purchase and Access

lock_open Open-Access

Bibliography

Manuscript ID: 217
Pages: 26-50
Submitted: 2018-11-05
Revised: 2018-11-18
Revised: 2018-12-30
Accepted: 2019-01-01
Published: 2018-12-30


Cited By (0)
Journal's Title
ITIPPR Cover Page

Journal

ICSES Transactions on Image Processing and Pattern Recognition
ISSN: 2645-8071

ISSN: 2645-8071
Frequency: Quarterly
Accessability: Online - Open Access (till 2020)
Founded in: Mar. 2015
Publisher: ICSES
DOI Suffix: 10.31424/icses.itippr