CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

Image Fusion of Brain Images using Redundant Discrete Wavelet Transform

by Umesh B. Mantale, Vishwajit B. Gaikwad
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 4
Year of Publication: 2013
Authors: Umesh B. Mantale, Vishwajit B. Gaikwad
10.5120/12871-8797

Umesh B. Mantale, Vishwajit B. Gaikwad . Image Fusion of Brain Images using Redundant Discrete Wavelet Transform. International Journal of Computer Applications. 74, 4 ( July 2013), 7-11. DOI=10.5120/12871-8797

@article{ 10.5120/12871-8797,
author = { Umesh B. Mantale, Vishwajit B. Gaikwad },
title = { Image Fusion of Brain Images using Redundant Discrete Wavelet Transform },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 4 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number4/12871-8797/ },
doi = { 10.5120/12871-8797 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:19.739487+05:30
%A Umesh B. Mantale
%A Vishwajit B. Gaikwad
%T Image Fusion of Brain Images using Redundant Discrete Wavelet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 4
%P 7-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image fusion has the very wider scope in medical sciences. Medical Images are obtained form different type of equipments and are of different modalities, each of them carries altogether different information. Especially study of brain images and its features is of greater interest for doctors since several centuries. Now because of radiology and evolution computers made this possible to look in to head online. This posed several challenges for software engineers to produce the good quality images or stream of images. Since medical images are from different modalities, which made it difficult to produce a single image from all these images. With the help of several image processing algorithms it is now possible to fuse the images. This gave rise to another challenge for producing efficient algorithm. This paper proposes the Redundant discrete wavelet transform (RDWT) based algorithm for image fusion, and compares with the other DWT based methods. These methods are assessed on the basis of statistical measures such as entropy, mean and standard deviation. According to the assessment made, it is found that the proposed method is giving better results. The Brain atlas based images are considered as input.

References
  1. Richa Singh, Mayank Vatsa, Afzel Noore "Multimodal Medical Image Fusion using Redundant DiscreteWavelet Transform", 2009 Seventh International Conference on Advances in Pattern Recognition.
  2. Jan Flusser, Filip ?Sroubek, and Barbara Zitov, Image Fusion: Principles, Methods, and Applications, Tutorial EUSIPCO 2007.
  3. Valdimir S. Petrovoc, Manchester School of Engineering, "Multisensor Pixel-level Image Fusion" , A Thesis of Phd program Feb-2001.
  4. Medha V. Wyawahare, Dr. Pradeep M. Patil, and Hemant K. Abhyankar, Image Registration Techniques: An overview, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 2, No. 3, September 2009
  5. Jiang Dong, Dafang Zhuang, Yaohuan Huang and Jingying Fu, "dvances in Multi-Sensor Data Fusion: Algorithms and Applications",Sensors 2009, 9, 7771-7784; doi: 0. 3390/ 91007771
  6. Manjusha Deshmukh and Udhav Bhosale, "Image Fusion and Image Quality Assessment of Fused Images" International Journal of Image Processing (IJIP), Volume (4).
  7. V. Ramachandra and Uttamkumar," Image fusion in GRDSS for Land use Mapping "www. mapindia. org/2005/papers/pdf/66. pdf.
  8. Uttam Kumar, Anindita Dasgupta, Chiranjit Mukhopadhyay, N. V. Joshi, and T. V. Ramachandra, "Comparison of 10 multi-sensor image fusion paradigms for ikonos images", http://wgbis. ces. iisc. ernet. in/energy/paper/ijrrcs_ikonos_fusion/image. htm.
  9. Shivsubramani Krishnamoorthy, Prof. K P Soman "Implementation and Comparative Study of Image Fusion Algorithms" , International Journal of Computer Applications (0975 – 8887) Volume 9– No. 2, November 2010.
  10. The Whole Brain Atlas- Harvard Medical School www. med. harvard. edu/aanlib/
  11. Eduardo Fernández Canga, "IMAGE FUSION" Project report for the degree of ME. in Electrical & Electronic Engineering
  12. N. Indhumadhi, G. Padmavathi, " Enhanced Image Fusion Algorithm Using Laplacian Pyramid and Spatial frequency Based Wavelet Algorithm", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-5, November 2011.
  13. Dennis L. HartmannATMS 552 Notes: Section 9: Wavelets Page 240-258
  14. Andrew P. Bradley Shift-invariance in the Discrete Wavelet Transform Proc. VIIth Digital Image Computing: Techniques and Applications, Sun C. , Talbot H. , Ourselin S. and Adriaansen T. (Eds. ), 10-12 Dec. 2003, Sydney
Index Terms

Computer Science
Information Sciences

Keywords

Multimodal Image Fusion DWT based image fusion Pixel level image fusion RDWT based image fusion method

Powered by PhDFocusTM