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Arabian Journal for Science and Engineering
Erschienen in: Arabian Journal for Science and Engineering 8/2022

28.01.2022 | Research Article-Computer Engineering and Computer Science

An Adaptive MRI-PET Image Fusion Model Based on Deep Residual Learning and Self-Adaptive Total Variation

verfasst von: A. Lakshmi, M. Pallikonda Rajasekaran, S. Jeevitha, S. Selvendran

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 8/2022

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Abstract

Multi-modal medical image fusion facilitates construction of composite images capturing complementary features from discrete images of multiple modalities. Fusion of anatomical and functional images captures structural and functional details in the fused image, which enables an integral examination of both images for a through diagnosis. In this paper, we propose an adaptive MRI-PET fusion framework based on a deep learning framework and self-adaptive total variation called DRL-SATV. The proposed model tested on MR_Gad-PET, MR_T1-PET and MR_T2-PET modalities from a benchmark dataset achieves best mutual information values of 3.6964, 3.7170 and 3.5491, respectively, compared to state-of-the-art models. Further, we have established the superiority of the proposed model with other objective metrics, time and design complexity evaluations and behavioral analyses with respect to free parameters, advocating its usage in clinical settings.
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Literatur
1.
Hill, D.; Edwards, P.; Hawkes, D.; Hilton, M.L.; Jawertz, B.D.; Sengupta, A.: Fusing medical images. Image Process. 6(2), 22–24 (1994)
2.
Meher, B.; Agrawal, S.; Panda, R.; Abraham, A.: A survey on region based image fusion methods. Inform. Fusion 48, 119–132 (2019)CrossRef
3.
Jadvar, H.; Colletti, P.M.: Competitive advantage of PET/MRI. Eur. J. Radiol. 83(1), 84–94 (2014)CrossRef
4.
Virarkar, M.; Ganeshan, D.; Devine, C.; Bassett Jr., R.; Kuchana, V.; Bhosale, P.: Diagnostic value of PET/CT versus PET/MRI in gynecological malignancies of the pelvis: a meta-analysis. Clin. Imag. 60(1), 53–61 (2020)CrossRef
5.
Li, H.; Manjunath, B.S.; Mitra, S.K.: Multisensor image fusion using the wavelet transform. Graphical Models Image Process. 57(3), 235–245 (1995)CrossRef
6.
Behloul, F.; Janier, M.; Croisille, P.; Poirier, C.; Boudraa, A.; Unterreiner, R.; Mason, J.; Revel, D.: MRI-PET data fusion using soft computing for the automatic assessment of myocardial viability. In: Computers in Cardiology 1998, vol. 25 (Cat. No. 98CH36292), pp. 29–32. IEEE (1998)
7.
Xia, K.J.; Yin, H.S.; Wang, J.Q.: A novel improved deep convolutional neural network model for medical image fusion. Cluster Comput. 22(1), 1515–1527 (2019)MathSciNetCrossRef
8.
Hou, R.; Zhou, D.; Nie, R.; Liu, D.; Ruan, X.: Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model. Med. Biol. Eng. Comput. 57(4), 887–900 (2019)CrossRef
9.
Daneshvar, S.; Ghassemian, H.: MRI and PET images fusion based on human retina model. J. Zhejiang Univ. Sci. A 8(10), 1624–1632 (2007)CrossRef
10.
Du, J.; Li, W.; Lu, K.; Xiao, B.: An overview of multi-modal medical image fusion. Neurocomputing 215, 3–20 (2016)CrossRef
11.
Huang, S.H.; Chien, C.Y.; Lin, W.C.; Fang, F.M.; Wang, P.W.; Lui, C.C.; Huang, Y.C.; Hung, B.T.; Tu, M.C.; Chang, C.C.: A comparative study of fused FDG PET/MRI, PET/CT, MRI, and CT imaging for assessing surrounding tissue invasion of advanced buccal squamous cell carcinoma. Clin. Nucl. Med. 36(7), 518–525 (2011)CrossRef
12.
Donati, O.F.; Hany, T.F.; Reiner, C.S.; von Schulthess, G.K.; Marincek, B.; Seifert, B.; Weishaupt, D.: Value of retrospective fusion of PET and MR images in detection of hepatic metastases: comparison with 18F-FDG PET/CT and Gd-EOB-DTPA–enhanced MRI. J. Nucl. Med. 51(5), 692–699 (2010)CrossRef
13.
Amini, N.; Fatemizadeh, E.; Behnam, H.: MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules. J. Med. Eng. Technol. 38(4), 211–219 (2014)CrossRef
14.
Haddadpour, M.; Daneshvar, S.; Seyedarabi, H.: PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method. Biomed. J. 40(4), 219–225 (2017)CrossRef
15.
Du, J.; Li, W.; Xiao, B.: Anatomical-functional image fusion by information of interest in local Laplacian filtering domain. IEEE Trans. Image Process. 26(12), 5855–5866 (2017)MathSciNetCrossRef
16.
Tang, L.; Qian, J.; Li, L.; Hu, J.; Wu, X.: Multimodal medical image fusion based on discrete Tchebichef moments and pulse coupled neural network. Int. J. Imag. Syst. Technol. 27(1), 57–65 (2017)CrossRef
17.
Shahdoosti, H.R.; Tabatabaei, Z.: MRI and PET/SPECT image fusion at feature level using ant colony based segmentation. Biomed. Signal Process. Control 47, 63–74 (2019)CrossRef
18.
Padmavathi, K.; Asha, C.S.; Maya, V.K.: A novel medical image fusion by combining TV-L1 decomposed textures based on adaptive weighting scheme. Eng. Sci. Technol. Int. J. 23(1), 225–239 (2020)
19.
Liu, Y.; Chen, X.; Wang, Z.; Wang, Z.J.; Ward, R.K.; Wang, X.: Deep learning for pixel-level image fusion: recent advances and future prospects. Inform. Fusion 42, 158–173 (2018)CrossRef
20.
Liu, Y.; Chen, X.; Peng, H.; Wang, Z.: Multi-focus image fusion with a deep convolutional neural network. Inform. Fusion 36, 191–207 (2017)CrossRef
21.
Yang, B.; Zhong, J.; Li, Y.; Chen, Z.: Multi-focus image fusion and super-resolution with convolutional neural network. Int. J. Wavel. Multiresolut. Inf. Process. 15(04), 1750037 (2017)MathSciNetCrossRef
22.
Piao, J.; Chen, Y.; Shin, H.: A new deep learning based multi-spectral image fusion method. Entropy 21(6), 570 (2019)CrossRef
23.
Liu, Y.; Chen, X.; Cheng, J.; Peng, H.: A medical image fusion method based on convolutional neural networks. In: 2017 20th International Conference on Information Fusion (Fusion), pp. 1–7. IEEE (2017)
24.
Xu, X.; Shan, D.; Wang, G.; Jiang, X.: Multimodal medical image fusion using PCNN optimized by the QPSO algorithm. Appl. Soft Comput. 46, 588–595 (2016)CrossRef
25.
Hermessi, H.; Mourali, O.; Zagrouba, E.: Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain. Neural Comput. Appl. 30, 2029–2045 (2018)CrossRef
26.
Yang, Z.; Chen, Y.; Le, Z.; Fan, F.; Pan, E.: Multi-source medical image fusion based on Wasserstein generative adversarial networks. IEEE Access 7, 175947–175958 (2019)CrossRef
27.
Kumar, N.; Hoffmann, N.; Oelschlägel, M.; Koch, E.; Kirsch, M.; Gumhold, S.: Structural similarity based anatomical and functional brain imaging fusion. In: Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, pp. 121–129. Springer, Cham (2019)
28.
29.
He, K.; Zhang, X.; Ren, S.; Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
30.
Kim, J.; Kwon Lee, J.; Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
31.
Le Guen, V.: Cartoon + texture image decomposition by the tv-l1 model. Image Process. Line 4, 204–219 (2014)CrossRef
32.
Rudin, L.I.; Osher, S.J.; Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)MathSciNetCrossRef
33.
Lee, Y.J.; Lee, S.; Yoon, J.: A framework for moving least squares method with total variation minimizing regularization. J. Math. Imag. Vis. 48(3), 566–582 (2014)MathSciNetCrossRef
34.
He, C.; Hu, C.; Zhang, W.; Shi, B.; Hu, X.: Fast total-variation image deconvolution with adaptive parameter estimation via split Bregman method. Math. Probl. Eng. (2014)
35.
Duran, J.; Coll, B.; Sbert, C.: Chambolle’s projection algorithm for total variation denoising. Image Process. Line 2013, 311–331 (2013)CrossRef
36.
Song, J.; Cho, H.; Yoon, J.; Yoon, S.M.: Structure adaptive total variation minimization-based image decomposition. IEEE Trans. Circuits Syst. Video Technol. 28(9), 2164–2176 (2017)CrossRef
37.
Zhang, J.; Wei, Z.H.; Xiao, L.: A relaxed split Bregman iteration for total variation regularized image denoising. In: International Conference on Intelligent Computing, pp. 189–197. Springer, Berlin, Heidelberg (2012)
38.
Boyd, S.; Parikh, N.; Chu, E.; Peleato, B.; Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)CrossRef
39.
Wang, B.; Mezlini, A.M.; Demir, F.; Fiume, M.; Tu, Z.; Brudno, M.; Haibe-Kains, B.; Goldenberg, A.: Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11(3), 333 (2014)CrossRef
Metadaten
Titel
An Adaptive MRI-PET Image Fusion Model Based on Deep Residual Learning and Self-Adaptive Total Variation
verfasst von
A. Lakshmi
M. Pallikonda Rajasekaran
S. Jeevitha
S. Selvendran
Publikationsdatum
28.01.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Arabian Journal for Science and Engineering / Ausgabe 8/2022
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-020-05201-2

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