Abstract
Empirical Wavelet Transform (EWT) is an adaptive signal decomposition technique in which the wavelet basis is constructed based on the information contained in the signal instead of a fixed basis as in standard Wavelet Transform (WT). Its adaptive nature enables EWT in many image processing applications like image denoising, image compression, etc. In this paper, a new adaptive image fusion algorithm is proposed for combining CT and PET images using EWT. EWT first decomposes both the images into approximate and detailed components using the adaptive filters that are constructed according to the content of an image by estimating the frequency boundaries. Then, the corresponding approximate and detailed components of CT and PET images are combined by using appropriate fusion rules. An adaptive EWT image fusion, a newly proposed method, is compared with standard WT fusion using the image quality metrics, image fusion metrics and error metrics. The quantitative analysis proved that the newly proposed method results in better quality than the standard WT method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Constantinos S. Pattichis, Marios S. Pattichis, Evangelia Micheli-Tzanakou: Medical imaging fusion applications: An overview, Thirty-Fifth Asilomar Conf. on Signals, Systems and Computers, 2001; 2: 1263–1267.
C. Pohl, J. L. Van Genderen: Review article Multisensor image fusion in remote sensing: Concepts, methods and applications, International Journal of Remote Sensing, 1998; 19(5): 823–854.
H.B. Mitchell: Image Fusion Theories, Techniques and Applications, Springer, 2010.
Hui Li, B. S. Manjunath. Sanjit K. Mitra, Multi-Sensor Image Fusion using the Wavelet Transform, Graphical Models and Image Processing, 1995; 57(3): 235–245.
N.E. Huang and Z. Shen and S.R. Long and M.C. Wu and H.H. Shih and Q. Zheng and N-C. Yen and C.C. Tung and H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Proc. Royal Society London A Mathematical, Physical and Engineering Sciences., 1998; 454(1971): 903–995.
Jerome Gilles, Empirical Wavelet Transform, IEEE Transactions on Image Processing, 2013; 61(16): 3999–4010.
Maheshwari, Shishir and Pachori, Ram Bilas and Acharya, U Rajendra: Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images, IEEE Journal of Biomedical and Health Informatics, 2017, 21(3): 803–813.
Bhattacharyya, Abhijit and Pachori, Ram Bilas: A Multivariate Approach for Patient Specific EEG Seizure Detection using Empirical Wavelet Transform, IEEE Transactions on Biomedical Engineering, 2017.
Varghees, V Nivitha and Ramachandran, KI: Effective Heart Sound Segmentation and Murmur Classification Using Empirical Wavelet Transform and Instantaneous Phase for Electronic Stethoscope, IEEE Sensors Journal, 2017, 17(12): 3861–3872.
I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, CBMS-NSF Regional Conference Series in Applied Mathematics, 1992.
Jerome Gilles and Giang Tran, Stanley Osher, 2D Empirical Transforms: Wavelets, Ridgelets and Curvelets revisited, SIAM Journal on Imaging Sciences, 2014; 7(1): 157–186.
Scott Shaobing Chen, David L. Donoho, Michael A. Saunders: Atomic Decomposition by Basis Pursuit, SIAM Review, Society for Industrial and Applied Mathematics, 2001; 43(1): 129–159.
Jagalingam Pa, Arkal Vittal Hegdeb: A Review of Quality Metrics for Fused Image, Aquatic Procedia, 2015; 4: 133–142.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Barani, R., Sumathi, M. (2018). Adaptive PET/CT Fusion Using Empirical Wavelet Transform. In: Bhateja, V., Tavares, J., Rani, B., Prasad, V., Raju, K. (eds) Proceedings of the Second International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-8228-3_40
Download citation
DOI: https://doi.org/10.1007/978-981-10-8228-3_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8227-6
Online ISBN: 978-981-10-8228-3
eBook Packages: EngineeringEngineering (R0)