Fusion of CT and MR Liver Images by SURF-Based Registration

Keywords: Image Fusion, SURF, Wavelet Transform

Abstract

Medical imaging plays an important role in the diagnosis and treatment of different diseases. Images with more details are obtained by image fusion for more accurate analysis of medical images. In this study, Computed Tomography (CT) and Magnetic Resonance (MR) images of the liver from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) are fused using different combinations of different wavelet types such as daubechies, coiflet and symlet. To accomplish this task, first the preprocessing steps are completed, and then registration is performed using Speed up Robust Features (SURF). As a result, to measure the quality of the obtained fusion image Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measurement (SSIM), Mean Structural Similarity (MSSIM) and Feature Similarity Index (FSIM) metrics are used.

Downloads

Download data is not yet available.

References

J. Ma, Y. Ma, and C. Li, "Infrared and visible image fusion methods and applications: A survey," Information Fusion, vol. 45, pp. 153-178, 2019.

G. Piella, "A general framework for multiresolution image fusion: from pixels to regions," Information fusion, vol. 4, no. 4, pp. 259-280, 2003.

J. Wang, J. Peng, X. Feng, G. He, and J. Fan, "Fusion method for infrared and visible images by using non-negative sparse representation," Infrared Physics & Technology, vol. 67, pp. 477-489, 2014.

Y. Liu, X. Chen, Z. Wang, Z. J. Wang, R. K. Ward, and X. Wang, "Deep learning for pixel-level image fusion: Recent advances and future prospects," Information Fusion, vol. 42, pp. 158-173, 2018.

A. P. James and B. V. Dasarathy, "Medical image fusion: A survey of the state of the art," Information Fusion, vol. 19, pp. 4-19, 2014.

L. Shuaiqi, Z. Jie, and S. Mingzhu, "Medical image fusion based on rolling guidance filter and spiking cortical model," Computational and Mathematical Methods in Medicine, vol. 2015, 2015.

S. Li, H. Yin, and L. Fang, "Group-sparse representation with dictionary learning for medical image denoising and fusion," IEEE Transactions on biomedical engineering, vol. 59, no. 12, pp. 3450-3459, 2012.

S. Singh, D. Gupta, R. Anand, and V. Kumar, "Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network," Biomedical Signal Processing and Control, vol. 18, pp. 91-101, 2015.

A. Galande and R. Patil, "The art of medical image fusion: A survey," in 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013, pp. 400-405: IEEE.

R. Gharbia, A. T. Azar, A. E. Baz, and A. E. Hassanien, "Image fusion techniques in remote sensing," arXiv preprint arXiv:1403.5473, 2014.

P. Ganasala and V. Kumar, "CT and MR image fusion scheme in nonsubsampled contourlet transform domain," Journal of digital imaging, vol. 27, no. 3, pp. 407-418, 2014.

A. S. Sekhar and M. N. G. Prasad, "A novel approach of image fusion on MR and CT images using wavelet transforms," in 2011 3rd International Conference on Electronics Computer Technology, 2011, vol. 4, pp. 172-176.

S. Rajkumar, P. Bardhan, S. K. Akkireddy, and C. Munshi, "CT and MRI image fusion based on Wavelet Transform and Neuro-Fuzzy concepts with quantitative analysis," in 2014 International Conference on Electronics and Communication Systems (ICECS), 2014, pp. 1-6.

F. Ali, I. El-Dokany, A. Saad, and F. E.-S. Abd El-Samie, "Curvelet fusion of MR and CT images," Progress in Electromagnetics Research, vol. 3, pp. 215-224, 2008.

G. Pajares and J. M. De La Cruz, "A wavelet-based image fusion tutorial," Pattern recognition, vol. 37, no. 9, pp. 1855-1872, 2004.

L. Chiorean and M.-F. Vaida, "Medical image fusion based on discrete wavelet transform using Java technology," in Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces, 2009, pp. 55-60: IEEE.

M. Ceylan and A. E. Canbilen, "Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoising," International Journal of Intelligent Systems and Applications in Engineering, vol. 5, no. 4, pp. 222-231, 2017.

Y. Lu, K. Gao, T. Zhang, and T. Xu, "A novel image registration approach via combining local features and geometric invariants," PloS one, vol. 13, no. 1, p. e0190383, 2018.

H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," in European conference on computer vision, 2006, pp. 404-417: Springer.

B. Erickson et al., "Radiology Data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma [TCGA-LIHC] collectionThe," Cancer Imaging Archive, 2016.

K. Clark et al., "The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository," Journal of digital imaging, vol. 26, no. 6, pp. 1045-1057, 2013.

TCIA. Cancer Imaging Archive. Available: https://www.cancerimagingarchive.net/

Published
2019-12-12
How to Cite
[1]
M. F. ASLAN, A. Durdu, and K. Sabanci, “Fusion of CT and MR Liver Images by SURF-Based Registration”, IJISAE, vol. 7, no. 4, pp. 216-221, Dec. 2019.
Section
Research Article

Most read articles by the same author(s)