A Novel Adaptive PET/CT Image Fusion Algorithm

Author(s): Kai-jian Xia*, Jian-qiang Wang, Jian Cai

Journal Name: Current Bioinformatics

Volume 14 , Issue 7 , 2019

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities.

Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework.

Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components.

Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective.

Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

Keywords: Lung cancer, PET/CT fusion, Piella framework, membership functions, DTCWT, activity measure, decision factor.

Wang HH. A New Multiwavelet-Based Approach to Image Fusion. J Math Imaging Vis 2004; 21: 177-92.
Den Y, Wu Y, Zhou L. Blind Image Forensics Based on Dual-Tree Complex Wavelet Transform Statistical Features. J Syst Simulation 2011; 8: 1660-3.
El-Hariri MA, Gouhar GK, Refat AM. Integrated PET/CT in the preoperative staging of lung cancer: A prospective comparison of CT, PET and integrated PET/CT. Egyptian J Radiol Nuclear Med 2012; 43: 613-21.
Zhang Z, Blum RS. A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proc IEEE 1999; 87: 1315-26.
Piella G. A general framework for multiresolution image fusion: from pixel to regions. Inf Fusion 2003; 4: 259-80.
Li G, Xu S. Extension of Piella pixel-level multiresolution image fusion framework and its algorithm. Optics Precision Eng 2012; 20: 123-32.
Lu Y, Guo L, Li H. SAR and MS image fusion based on curvelet transform and activity measure. App Res Computers 2012; 29: 11-6.
Zhong Q, Xia L. Image fusion method based on dual-tree complex wavelet transform. Comput Eng App 2008; 44: 89-95.
Sakurai K, Hara M, Ozawa Y, Nakagawa M, Shibamoto Y. Thoracic hemangiomas: imaging via CT, MR, and PET along with pathologic correlation. J Thorac Imaging 2008; 23: 114-20.
Nunez J, Otazu X, Fors O, et al. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans Geosci Remote Sens 1999; 37: 1204-11.
Wong STC, Knowlton RC, Hawkins RA, et al. Multimodal Image Fusion for Noninvasive Epilepsy Surgery Planning. IEEE Comput Graph Appl 1996; 16: 30-8.
Vajdic SM, Katz HE, Downing AR, et al. Al-based relational matching and multimodal medical image fusion: Generalized 3D approaches. Intl Soc Opt Eng 1994; 2: 1565-73.
Pluim JPW, Maintz JBA, Viergever MA. Mutual information matching in multiresolution contexts. Image Vis Comput 2001; 19: 45-52.
Kingsbury NG. The Dual-tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional Filters. Proceedings of 8th IEEE Digital Signal Processing Workshop, Bryce Canyon. Utah, USA. IEEE: IEEE . 1998 ; 86-9.
Yang F, Wei H. Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules. Infrared Phys Technol 2013; 60: 235-43.
Sánchez SR, Rodríguez FA, Gómez RM, et al. [Utility of PET/CT for mediastinal staging of non-small cell lung cancer in stage III (N2)]. Rev Esp Med Nucl 2011; 30: 211-6.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Page: [658 - 666]
Pages: 9
DOI: 10.2174/1574893613666180704153946
Price: $65

Article Metrics

PDF: 39
PRC: 1