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Reseach Article

A Novel Multimodal Medical Image Fusion Approach based on Phase Congruency and Directive Contrast in NSCT Domain

by S. Anitha, T. Subhashini, M. Kamaraju
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 129 - Number 10
Year of Publication: 2015
Authors: S. Anitha, T. Subhashini, M. Kamaraju
10.5120/ijca2015907014

S. Anitha, T. Subhashini, M. Kamaraju . A Novel Multimodal Medical Image Fusion Approach based on Phase Congruency and Directive Contrast in NSCT Domain. International Journal of Computer Applications. 129, 10 ( November 2015), 30-35. DOI=10.5120/ijca2015907014

@article{ 10.5120/ijca2015907014,
author = { S. Anitha, T. Subhashini, M. Kamaraju },
title = { A Novel Multimodal Medical Image Fusion Approach based on Phase Congruency and Directive Contrast in NSCT Domain },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 129 },
number = { 10 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 30-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume129/number10/23111-2015907014/ },
doi = { 10.5120/ijca2015907014 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:23:04.017769+05:30
%A S. Anitha
%A T. Subhashini
%A M. Kamaraju
%T A Novel Multimodal Medical Image Fusion Approach based on Phase Congruency and Directive Contrast in NSCT Domain
%J International Journal of Computer Applications
%@ 0975-8887
%V 129
%N 10
%P 30-35
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, Non-subsampled medical image fusion is a unique tool, which develops many imaging techniques in medical field. The main work is to capture the information from different image sources and convert them into single output. Two different fusion rules like phase congruency and directive contrast are introduced. The work was discussed based on the images in medical field which gives accurate results with less distortion is based upon transformation and parameters. The main drawback of previous methods are they cannot produce a color image for better clarity and accurate analysis of medical image. In this paper, the parameters such as Mutual Information(MI), Edge Based Similarity Measure(QAB/F), Structural Information Metric(Qe), Degree Of Distortion(Qo) and Normalization Of Image(Qw) are introduced to increase the visual perception of an image. The parameters and lab-transform is used for better quality of the image.

References
  1. HarvardUniversitysite(http://www.med.harvard.edu/AALIB/home.html).
  2. G. Bhatnagar and B. Raman, “A new image fusion technique based on directive contrast,” Electron. Lett. Comput. Vision Image Anal., vol. 8, no. 2, pp. 18–38, 2009.
  3. Q. Zhang and B. L. Guo, “Multifocus image fusion using the nonsubsampled contourlet transform,” Signal Process. vol. 89, no. 7, pp. 1334–1346, 2009.
  4. Y. Chai, H. Li, and X. Zhang, Multi focus image fusion based on features contrast of multiscale products in nonsubsampled Contourlet transform domain,” Optik, vol. 123, pp. 569–581, 2012.
  5. Q.Guihong, Z.Dali, and Y.Pingfan, “Medical image fusion by Wavelet transform modulus maxima, ‘opt. Express, vol. 9,pp. 184-190, 2001.
  6. L. Yang, B. L. Guo, and W. Ni, “Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform,” Neurocomputing, vol.72, pp. 203–211, 2008.
  7. Performance comparison of different multi-resolution transforms for image fusion Shutao Li *, Bin Yang, Jianwen Hu 12(2011)74–84.
  8. C. Yang, J. Zhang, X. Wang, and X. Liu, “A novel similarity based quality metric for image fusion,” Inf. Fusion, vol. 9, pp. 156–160,2008.
  9. L.D. Cunha, J.P. Zhou, The nonsubsampled contourlet transform: theory, design, and applications, IEEE Transactions on Image Processing 15 (10) (2006) 3089–3101.
  10. Q. Zhang, B.L. Guo, Multi-focus image fusion using the nonsubsampled contourlet transform, Signal Processing 89 (7) (2009) 1334–1346.
  11. S. Zheng, W.Z. Shi, J. Liu, G.X. Zhu, J.W. Tian, Multisource image fusion method using support value transform, IEEE Transactions on Image Processing 16 (7)(2007) 1831–1839.
  12. Gaurav Bhatnagar, Q. M. Jonathan Wu, and Zheng Liu, “Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain,” vol. 15 , no. 5, august 2013.
  13. Dr.S.S.Bedi1, Mrs.Jyoti Agarwal2, Pankaj Agarwal, “Image Fusion Techniques and Quality Assessment Parameters for Clinical Diagnosis: A Review,” vol. 2,issue 2, february 2013.
Index Terms

Computer Science
Information Sciences

Keywords

NSCT domain MRI image CT image phase congruency and directive contrast.