A Survey on Multi-Scale Medical images Fusion Techniques: Brain Diseases
A Survey on Multi-Scale Medical images Fusion Techniques: Brain Diseases
DOI:
https://doi.org/10.14738/jbemi.71.7415Keywords:
Image fusion, Brain Diseases Challenges, Multi-scale medical images fusion methods.Abstract
Brain diseases such as degenerative (alzheimer's disease), neoplastic disease (brain tumor like sarcoma, glioma) are considered an interesting topic areas in the medical image fusion diagnosis. Pixel-level image fusion techniques are designed to combine multiple/multi-scale input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Since they are difficult to be summarized ; survey paper are characterized by (1) medical image definition , brain diseases challenges , analysis a various techniques for multi-scale image fusion with its own modalities, fusion rule, fusion strategy and dis-advantage ,Whilst used a database of medical images for medical Harvard School (brain diseases) which contains various groups of co-registered multi-modal images including MRI/CT, MRI/PET and PET/SPECT and MRI (T1/T2) images.
References
(1) B.V. Dasarathy, Editorial: information fusion in the realm of medical applications – a bibliographic glimpse at its growing appeal, Inform. Fus. 13 (1) (2012) 1–9.
(2) Wu, R.-T., & Jahanshahi, M. R. (2018). Data fusion approaches for structural health monitoring and system identification: Past, present, and future. Structural Health Monitoring.
(3) C. Barillot, D. Lemoine, L.L. Briquer, F. Lachmann, B. Gibaud, Data fusion in medical imaging: merging multimodal and multipatient images, identification of structures and 3D display aspects, Euro. J. Radiol. 17 (1) (1993) 22–27.
(4) S. Marshall, G. Matsopoulos, Morphological data fusion in medical imaging, in: IEEE Winter Workshop on Nonlinear Digital Signal Processing, 1993, IEEE, 1993, pp. 6–1.
(5) G. Matsopoulos, S. Marshall, J. Brunt, Multiresolution morphological fusion of MR and CT images of the human brain, IEE Proc. Vis., Image Signal Process., vol. 141, IET, 1994, pp. 137–142.
(6) J. Julow, T. Major, M. Emri, I. Valalik, S. Sagi, L. Mangel, G. Nemeth, L. Tron, G. Varallyay, D. Solymosi, et al., The application of image fusion in stereotactic brachytherapy of brain tumours, Acta Neurochirurgica 142 (11) (2000) 1253– 1258.
(7) M.C. Dastjerdi, A. Karimian, H. Afarideh, A. Mohammadzadeh, FMDIB: a software tool for fusion of MRI and DHC-SPECT images of brain, in: World Congress on Medical Physics and Biomedical Engineering, September 7–12, 2009, Munich, Germany, Springer, 2009, pp. 741–744.
(8) Slichter, C.P., 2013. Principles of magnetic resonance, 1. Springer Science & Business Media.
(9) James, A.P., Dasarathy, B.V.: Medical image fusion: a survey of the state of the art. Inf. Fusion 19, 4–19 (2014).
(10) Talbar S.N., Chavan S.S., Pawar A. (2019) Non-subsampled Complex Wavelet Transform Based Medical Image Fusion. In: Arai K., Bhatia R., Kapoor S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham.
(11) Meher, B., Agrawal, S., Panda, R., & Abraham, A. (2018). A survey on region based image fusion methods . Information Fusion. doi:10.1016/j.inffus.2018.07.010.
(12) Rajiv Singh, "Medical image fusion: applications, approaches and evaluation", in Proceedings of International Conference on Medical Imaging and Diagnosis, Chicago, USA, October 20-21, 2016.
(13) B.V. Dasarathy, Editorial: information fusion in the realm of medical applications – a bibliographic glimpse at its growing appeal, Inform. Fus. 13 (1) (2012) 1–9.
(14) Pradeep K. Atrey, and M. Anwar Hossain, “Multimodal Fusion for Multimedia Analysis: A Survey”, Multimedia Systems, DOI: 10.1007/s00530-010-0182-0, Springer Verlag, 2010.
(15) S.L. Jany Shabu, Dr.C. Jayakumar, T. Surya,” Survey of Image Fusion Techniques for Brain Tumor Detection” , International Journal of Engineering and Advanced Technology (IJEAT),2013.
(16) 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.
(17) K.P.Indira, and R.Rani Hemamalini, “Analysis on Image Fusion Techniques for Medical Applications”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, Issue 9, 2014.
(18) Harmandeep Kaur, Er. Jyoti Rani, “Analytical Comparison of Various Image Fusion Techniques” International Journal of Advanced Research in Computer Science and software Engineering, Vol. 5, Issue 5, 2015.
(19) Jasmeetkaur, Er. Rajdavinder, “An Evaluation on Different Image Fusion Techniques”, IPASJ International Journal of Computer Science (IIJCS), vol. 2, Issue 4, 2014.
(20) Wu D., Yang A., Zhu L., Zhang C. (2014) Survey of Multi-sensor Image Fusion. In: Ma S., Jia L., Li X., Wang L.,Zhou H., Sun X. (eds) Life System Modeling and Simulation. ICSEE 2014, LSMS 2014. Communications in Computer and Information Science, vol 461. Springer, Berlin, Heidelberg.
(21) Fatma E. El-Gamal, and Mohammed Elmogy, “Current Trends in Medical Image Registration and Fusion”, Egyptian Informatics Journal, 2015.
(22) S.L. Jany Shabu, Dr.C. Jayakumar, T. Surya,” Survey of Image Fusion Techniques for Brain Tumor Detection” , International Journal of Engineering and Advanced Technology (IJEAT),2013.
(23) Bhavana. V, Krishnappa. H.K,” A Survey on Multi – Modality Medical Image Fusion”, peer-reviewed and accepted to be presented at the IEEE WiSPNET 2016 conference,IEEE,2016.
(24) Heba M. Elhoseny, El-Sayed Mahmoud El-Rabaie, Osama S. Farag Allah, Fathi E. Abd El-Samie.” Medical Image Fusion: A Literature Review Present Solutions and Future Directions”, Minufiya J. of Electronic Engineering Research (MJEER), Vol. 26, No. 2, July 2017.
(25) Bikash Meher , Sanjay Agrawal , Rutuparna Panda , Ajith Abraham , A survey on region based image fusion methods , Information Fusion (2018), doi: https://doi.org/10.1016/j.inffus.2018.07.010.
(26) http://www.med.harvard.edu/AANLIB/home.html.
(27) Y. Liu, S. Liu, Z. Wang, A general framework for image fusion based on multi-scale transform and sparse representation, Information Fusion 24 (1) (2015) 147-164.
(28) Z. Xu, Medical image fusion using multi-level local extrema, Inf. Fusion 19 (2014) 38–48.
(29) G.Vivone, L. Alparone, J. Chanussot, M. Dalla Mura, A. Garzelli, G. Licciardi, R. Restaino, Wald, A critical comparison among pansharpening algorithms, IEEE Transactions on Geoscience and Remote Sensing 53 (5) (2015) 2565–2586.
(30) Susmitha Vekkot, and Pancham Shukla, “A Novel Architecture for Wavelet based Image Fusion” Journal of World Academy of Science, Engineering and Technology, Vol. 57, pp. 32-3, 2009.
(31) Peng Geng, Xing Su, Tan Xu and Jianshu Liu, “Multi-modal Medical Image Fusion Based on the Multiwavelet and Nonsubsampled Direction Filter Bank”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.8, No.11, pp.75-84 2015.
(32) Maruturi Haribabu , CH.Hima Bindu , Dr.K.Satya Prasad, “A New Approach of Medical Image Fusion using Discrete Wavelet Transform”, ACEEE International Journal on Signal & Image Processing, Vol. 4, No. 2, May 2013.
(33) Q.P. Zhang, M. Liang, W.C. Sun, 2004, Medical diagnostic image fusion based on feature mapping wavelet neural networks, 2004 IEEE First Symposium on Multi-Agent Security and Survivability, pp. 51–54.
(34) V.D. Calhoun, T. Adali, Feature-based fusion of medical imaging data, IEEE Trans. Inf. Technol. Biomed. 13 (5) (2009) 711–720.
(35) Bengueddoudj, A., Messali, Z., Mosorov, V.: A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images.
(36) J. Innov. Opt. Health Sci. 10, 1750001 (2017) , Bengueddoudj, A., Messali, A.: An efficient algorithm for multimodal medical image fusion based on feature selection and PCA using DTCWT (FSPCA-DTCWT). Med. Technol. J. 2 (1), 179–192 (2018).
(37) Ganasala, P., Kumar, V.: CT and MR image fusion scheme in nonsubsampled contourlet transform domain. J. Digit. Imag. 27, 407–418 (2014).
(38) Q. Guihong, Z. Dali, and Y. Pingfan. Medical image fusion by wavelet transform modulus maxima. Optics Express, 9(4):184–190, 2001.
(39) Y. Kirankumar and S. Devi. Transform-based medical image fusion. International Journal of Biomedical Engineering and Technology, 1(1):101–110, 2007.
(40) L. Xu, S. Zheng, J. Jia, Unnatural L0 sparse representation for natural image deblurring, in: 2013 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2013), Portland, OR, USA, June 23-28, 2013, 2013, pp. 1107–1114.
(41) Udhaya Suriya TS, Rangarajan P “Brain tumour detection using discrete wavelet transform based medical image fusion” Biomedical Research, 28 (2), 2017, pp. 684-688.
(42) J. Yang, J. Wright, T. S. Huang, Y. Ma, Image super-resolution as sparse representation of raw image patches, in: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA, 2008.
(43) B. Yang, S. Li, Multifocus image fusion and restoration with sparse representation, IEEE T. Instrumentation and Measurement 59 (4) (2010) 884– 892.
(44) S. Akbar, R. Naqvi, I. Touqir, A. M. Siddiqui, Sparse representation of image and video using easy path wavelet transform, Signal Processing 103 (2014) 331–347.
(45) S.A. Kuhn, B. Romeike, J. Walter, R. Kal, R. Reichart, Multiplanar MRI–CT fusion neuronavigation-guided serial stereotactic biopsy of human brain tumors: proof of a strong correlation between tumor imaging and histopathology by a new technical approach, J. Can. Res. Clin. Oncol. 135 (9) (2009) 1293–1302.
(46) V. Barra, J.-Y. Boire, Automatic segmentation of subcortical brain structures in MR images using information fusion, IEEE Trans. Med. Imag. 20 (7) (2001) 549–558.
(47) D. Dey, D.G. Gobbi, P.J. Slomka, K.J. Surry, T.M. Peters, Automatic fusion of freehand endoscopic brain images to three-dimensional surfaces: creating stereoscopic panoramas, IEEE Trans. Med. Imag. 21 (1) (2002) 23–30.
(48) H. Lee, H. Hong, Hybrid surface-and voxel-based registration for MR-PET brain fusion, in: Image Analysis and Processing–ICIAP 2005, Springer, 2005, pp. 930–937.
(49) Y. Xia, S. Eberl, D. Feng, Dual-modality 3D brain PET-CT image segmentation based on probabilistic brain atlas and classification fusion, in: 17th IEEE International Conference on Image Processing (ICIP), 2010, IEEE, 2010, pp. 2557–2560.
(50) J.-D. Lee, B.-R. Huang, C.-H. Huang, A surface-projection MMI for the fusion of brain MR and SPECT images, in: IEEE Region 10 Conference, 2004, TENCON 2004, IEEE, 2004, pp. 179–182.
(51) M.C. Dastjerdi, A. Karimian, H. Afarideh, A. Mohammadzadeh, FMDIB: a software tool for fusion of MRI and DHC-SPECT images of brain, in: World Congress on Medical Physics and Biomedical Engineering, September 7–12, 2009, Munich, Germany, Springer, 2009, pp. 741–744.
(52) Rajesh Kumar P., Arun Prasath T., Pallikonda Rajasekaran M., Vishnuvarthanan G. (2019) Brain Subject Segmentation in MR Image for Classifying Alzheimer’s Disease Using AdaBoost with Information Fuzzy Network Classifier. In: Nayak J., Abraham A., Krishna B., Chandra Sekhar G., Das A. (eds) Soft Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore.
(53) Blank R.H. (2019) Alzheimer’s Disease and Other Dementias: An Introduction. In: Social & Public Policy of Alzheimer's Disease in the United States. Palgrave Pivot, Singapore.
(54) Khanal B, Lorenzi M, Ayache N, Pennec X (2016) A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer’s disease. NeuroImage 134:35–52.
(55) Tokuchi R, Hishikawa N, Sato K, Hatanaka N, Fukui Y, TakemotoaM, Ohta Y, Yamashita T, Abe K (2016) Age-dependent cognitive and affective differences in Alzheimer’s and Parkinson’s diseases in relation to MRI findings. J Neurol Sci 365:3–8.
(56) Rajesh Kumar P., Arun Prasath T., Pallikonda Rajasekaran M., Vishnuvarthanan G. (2019) Decisive Tissue Segmentation in MR Images: Classification Analysis of Alzheimer’s Disease Using Patch Differential Clustering. In: Kulkarni A., Satapathy S., Kang T., Kashan A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore.
(57) Mirzaei G, Adeli A, Adeli H (2016) Imaging and machine learning techniques for diagnosis of Alzheimer’s disease. Rev Neurosci 27(8):857–870. https://doi.org/10.1515/revneuro-2016- 0029.
(58) 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.
(59) OBREJA, Cristian, Dragoș.(2018). Useful information analysis from medical images. Diagnosis improvement by hemodynamic modeling and image processing (Doctoral dissertation).Retrievedfrom https://www.arthra.ugal.ro/bitstream/handle/123456789/5044/Rezumat_Eng_Teza_doctorat_Cristian_Obreja_2018.pdf?sequence=2&isAllowed=y.
(60) C. He, Q. Liu, H. Li, et al., Multimodal medical image fusion based on IHS and PCA, Proc. Eng. 7 (2010) 280–285.
(61) S. Daneshvar, H. Ghassemian, MRI and PET image fusion by combining IHS and retina-inspired models, Inf. Fusion 11 (2) (2010) 114–123.
(62) Matsopoulos, G.K., Marshall, S., Brunt, J.N.H. 1994. Multiresolution morphological fusion of MR and CT images of the human brain, IEE Proceedings of Visual Image Signal Processing, vol. 141 (3), pp. 137–142.
(63) Patil, U., Mudengudi, U. 2011. Image fusion using hierarchical PCA, Proceedings of Internal Conference Image Information Processing, pp. 1–6.
(64) Q. Guihong, Z. Dali, Y. Pingfan, Medical image fusion by wavelet transform modulus maxima, Opt. Express 9 (4) (2001) 184–190.
(65) R. Singh, A. Khare, Fusion of multimodal medical images using Daubechies complex wavelet transform–A multiresolution approach, Inf. Fusion (2012).
(66) Y. Zheng, A.E. Essock, B.C. Hansen, et al., A new metric based on extended spatial frequency and its application to DWT based fusion algorithms, Inf. Fusion 8 (2) (2007) 177–192.
(67) Singh, R., Vatsa, M., Noore, A. 2009. Multimodal medical image fusion using redundant discrete wavelet transform, In: IEEE Seventh International Conference on Advances in Pattern Recognition, 2009, pp. 232–235.
(68) Liu, Y., Yang, J., Sun, J. 2010. PET/CT medical image fusion algorithm based on multiwavelet transform, In: IEEE 2nd International Conference on Advanced Computer Control, 2010, pp. 264–268.
(69) Xue-jun, W., Ying, M. 2010. A medical image fusion algorithm based on lifting wavelet transform, In: IEEE International Conference on Artificial Intelligence and Computational Intelligence, 2010 (3), pp. 474–476.
(70) L. Yang, B.L. Guo, W. Ni, Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform, Neurocomputing 72 (1) (2008) 203–211.
(71) G. Bhatnagar, Q.M. Wu, Z. Liu, Directive contrast based multimodal medical image fusion in NSCT domain, IEEE Trans. Multimed. 15 (5) (2013) 1014–1024.
(72) Q. Miao, C. Shi, P. Xu, et al., A novel algorithm of image fusion using shearlets, Opt. Commun. 284 (6) (2011) 1540–1547.
(73) L. Wang, B. Li, L. Tian, Multi-modal medical volumetric data fusion using 3D discrete shearlet transform and global-to-local rule, IEEE Trans. Biomed. Eng. (2014).
(74) H. Yin, S. Li, L. Fang, Simultaneous image fusion and super-resolution using sparse representation, Inf. Fusion 14 (3) (2013) 229–240.
(75) B. Yang, S. Li, Pixel-level image fusion with simultaneous orthogonal matching pursuit, Inf. Fusion 13 (1) (2012) 10–19.
(76) H. Yin, S. Li, Multimodal image fusion with joint sparsity model, Opt. Eng. 50 (6) (2011) 7–10.
(77) N. Yu, T. Qiu, F. Bi, et al., Image features extraction and fusion based on joint sparse representation, IEEE J. Sel. Top. Signal Process. 5 (5) (2011) 1074–1082.
(78) S. Li, H. Yin, L. Fang, Group-sparse representation with dictionary learning for medical image denoising and fusion, IEEE Trans. Biomed. Eng. 59 (12) (2012) 3450–3459.
(79) R.Redondo,F.Sroubek,S.Fischer, and G.Grstobal,” Multifoucs image fusion using the log-Gabor transform and a multisize windows technique”Inf .Fusion, Vol .10.no.2,pp.163-171,2009.
(80) S. Li, X. Kang X, J. Hu, Image fusion with guided filtering, IEEE Trans. Image Process. 22 (7) (2013) 2864–2875.
(81) Z. Xu, Medical image fusion using multi-level local extrema, Inf. Fusion 19 (2014) 38–48.
(82) R. Shen, I. Cheng, A. Basu, Cross-scale coefficient selection for volumetric medical image fusion, IEEE Trans. Biomedical Engineering 60 (4) (2013) 1069-1079
(83) Y. Liu, S. Liu, Z. Wang, A general framework for image fusion based on multi-scale transform and sparse representation, Information Fusion 24 (1) (2015) 147-164.
(84) [ Aggarwal, Jakek,ed.” Multi sensor fusion for computer vision”. Vol.99. Springer sciences & Business media, 2013.
(85) , C. Pohl, J Van Genderen, “ Multisensor Image Fusion of remotely sensed data : concepts, methods and applications”, International Journal Remote Sensing vol19(5) march 2009.
(86) S. S. Hana, b,*, H.T. Lia, H.Y.Gua,b,”” The study on image fusion for high spatial resolution remote sensing images”, The international Archives of the photogrammetry, Remote sensing and spatial information sciences vol XXXVII. Part B7. Beijing, April 2008.
(87) Jagalingam P, ArkalVittal Hedge. “Pixel Level Image Fusion- A Review on various Techniques.
(88) Z. Xu, Medical image fusion using multi-level local extrema, Inf. Fusion 19 (2014) 38–48.
(89) A. A. Goshtasby, S. Nikolov, Image fusion: Advances in the state of the art, Information Fusion 8 (2) (2007) 114–118.
(90) R.Redondo,F.Sroubek,S.Fischer, and G.Grstobal,” Multifoucs image fusion using the log-Gabor transform and a multisize windows technique”Inf .Fusion, Vol .10.no.2,pp.163-171,2009.
(91) Z. Zhang, R. S. Blum, A categorization of multiscale-decomposition-based image fusion schemes 645 with a performance study for a digital camera application, Proceedings of the IEEE 87 (8) (1999) 1315–1326.
(92) G. Pajares, J. M. de la Cruz, A wavelet-based image fusion tutorial, Pattern Recognition 37 (9) 660 (2004) 1855–1872
(93) J. J. Lewis, R. J. O. Callaghan, S. G. Nikolov, D. R. Bull, N. Canagarajah, Pixel- and regionbased image fusion with complex wavelets, Information Fusion 8 (2) (2007) 119–130.
(94) E. J. Cands, D. L. Donoho, Curvelets and curvilinear integrals, Journal of Approximation Theory 113 (1) (2001) 59–90.
(95) F. Nencini, A. Garzelli, S. Baronti, L. Alparone, Remote sensing image fusion using the curvelet transform, Information Fusion 8 (2) (2007) 143–156, special Issue on Image Fusion: Advances in the State of the Art.
(96) B. Yang, S. Li, Multifocus image fusion and restoration with sparse representation, IEEE Trans730 actions on Instrumentation and Measurement 59 (4) (2010) 884-892.
(97) Y. C. Pati, R. Rezaiifar, P. S. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, in: Proceedings of Asilomar Conference on Signals, Systems and Computers, Vol. 1, 1993, pp. 40–44.
(98) S. Li, H. Yin, L. Fang, Group-sparse representation with dictionary learning for medical image 735 denoising and fusion, IEEE Transactions on Biomedical Engineering 59 (12) (2012) 3450–3459.
(99) C. Chen, Y. Li, W. Liu, J. Huang, Image fusion with local spectral consistency and dynamic gradient sparsity, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2760–2765.
(100) B. Yang, S. Li, Pixel-level image fusion with simultaneous orthogonal matching pursuit, Infor740 mation Fusion 13 (1) (2012) 10–19.
(101) H. Yin, S. Li, Multimodal image fusion with joint sparsity model, Optical Engineering 50 (6) (2011) 067007.1–067007.10.
(102) N. Yu, T. Qiu, F. Bi, A. Wang, Image features extraction and fusion based on joint sparse representation, IEEE Journal of Selected Topics in Signal Processing 5 (5) (2011) 1074–1082.
(103) B.yang.J. Luo, S. Li, Color image fusion with extend joint sparse model, in: Proceedings of International Conference on Pattern Recognition, 2012, pp. 376–379.
(104) J. Luo, S. Li, Color image fusion with extend joint sparse model, in: Proceedings of International Conference on Pattern Recognition, 2012, pp. 376–379.
(105) H. Yin, S. Li, L. Fang, Simultaneous image fusion and super-resolution using sparse representa750 tion, Information Fusion 14 (3) (2013) 229–240 .
(106) S. Li, H. Yin, L. Fang, Remote sensing image fusion via sparse representations over learned dictionaries, IEEE Transactions on Geoscience and Remote Sensing 51 (9) (2013) 4779–4789.
(107) M. Kim, D. K. Han, H. Ko, Joint patch clustering-based dictionary learning for multimodal image fusion, Information Fusion 27 (1) (2016) 198–214.
(108) W. Wang, L. Jiao, S. Yang, Fusion of multispectral and panchromatic images via sparse representation and local autoregressive model, Information Fusion 20(1) (2014) 73-87.
(109) H. R. Shahdoosti, H. Ghassemian, Combining the spectral PCA and spatial PCA fusion methods by an optimal filter, Information Fusion 27 (1) (2016) 150–160.
(110) T. M. Tu, S. C. Su, H. C. Shyu, P. S. Huang, A new look at IHS-like image fusion methods, Information Fusion 2 (3) (2001) 177-186.
(111) T. M. Tu, S. C. Su, H. C. Shyu, P. S. Huang, A new look at IHS-like image fusion methods, Information Fusion 2 (3) (2001) 177-186.
(112) T. M. Tu, P. S. Huang, C. L. Hung, C. P. Chang, A fast intensity-hue-saturation fusion technique 800 with spectral adjustment for IKONOS imagery, IEEE Geoscience and Remote Sensing Letters 1 (4) (2004) 309-312.
(113) S. Rahmani, M. Strait, D. Merkurjev, M. Moeller, T. Wittman, An adaptive IHS pan-sharpening method, IEEE Geoscience and Remote Sensing Letters 7 (4) (2010) 746–750.
(114) Anjali Malviya, S. G. Bhirud, “Image Fusion of Digital Images”, International Journal of Recent Trends in Engineering, Vol. 2, No. 3, November 2009.
(115) J. J. Lewis, J. Robert, O. Callaghan, S. G. Nikolov, D. R. Bull and N. Canagaraja, “Pixel- and region-based image fusion with complex wavelets,” Information Fusion, Elsevier, vol. 8, pp. 119-130, 2007.
(116) N. Mitianoudis and T. Stathaki, “Pixel-based and Region-based Image Fusion schemes using ICA bases,” Elsevier Information Fusion, vol. 8, pp. 131-142, 2007.
(117) N. Cvejic, Bull, DR and N. Canagarajah,“Regionbased multimodal image fusion using ICA bases,” IEEE Sensors Journal, vol. 7, no. 5, pp. 743- 751, 2007.
(118) Susmitha Vekkot, and Pancham Shukla, “A Novel Architecture for Wavelet based Image Fusion” Journal of World Academy of Science, Engineering and Technology, Vol. 57, pp. 32-3, 2009
(119) School of Computer Science, Fudan University Shanghai, china “Information Fusion” www.elsevier.com/locate/inffus.
(120) Mamta Sharma, Geetanjali institute of technical studies, Udaipur, India “A Review : Image Fusion Techniques and Applications Vol. 7(3), 2016 1082- 1085
(121) Zheng, M. M., Krishnan, S. M., & Doraiswami, R. (n.d.). Data fusion application for computer-assisted clinical endoscopic image analysis. IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373). doi:10.1109/ccece.2002.1013103.
(122) G. Piella, A general framework for multiresolution image fusion: from pixels to regions, Information Fusion 4 (4) (2003) 259–280.
(123) [ S. Li, X. Kang, J. Hu, Image fusion with guided filtering, IEEE Trans. Image Process. 22 (7) (2013) 2864–2875.
(124) A. Toet, Image fusion by a ratio of low-pass pyramid, Pattern Recognit. Lett. 9 (4) (1989) 245–253.
(125) P.J. Burt, E.H. Adelson, The Laplacian pyramid as a compact image code, IEEE Trans. Commun. 31 (4)(1983) 532–540.
(126) Z. Wang, A.C. Bovik, H.R. Sheikh, et al., Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process. 13 (4) (2004) 600–612.
(127) Q. Miao, C. Shi, P. Xu, et al., A novel algorithm of image fusion using shearlets, Opt. Commun. 284 (6) (2011) 1540–1547, M. Hossny, S. Nahavandi, D. Creighton, Comments on ‘Information measure for performance of image fusion’, Electron. Lett. 44 (18) (2008) 1066–1067.
(128) A. Mittal, R. Soundararajan, A.C. Bovik, Making a “completely blind” image quality analyzer, IEEE Trans. Signal Process. Lett. 20 (3) (2013) 209–212., J.H. Jang, Y. Bae, J.B. Ra, Contrast-enhanced fusion of multisensor images using subband-decomposed multiscale retinex, IEEE Trans. Image Process. 21 (8) (2012) 3479–3490.
(129) J.H. Jang, Y. Bae, J.B. Ra, Contrast-enhanced fusion of multisensor images using subband-decomposed multiscale retinex, IEEE Trans. Image Process. 21 (8) (2012) 3479–3490.
(130) A. Mittal, R. Soundararajan, A.C. Bovik, Making a “completely blind” image quality analyzer, IEEE Trans. Signal Process. Lett. 20 (3) (2013) 209–212.
(131) Z. Xu, Medical image fusion using multi-level local extrema, Inf. Fusion 19 (2014) 38–48.
(132) Q. Miao, C. Shi, P. Xu, et al., A novel algorithm of image fusion using shearlets, Opt. Commun. 284 (6) (2011) 1540–1547.
(133) H.R. Sheikh, A.C. Bovik, Image information and visual quality, IEEE Trans. Image Process. 15 (2) (2006) 430–444.
(134) J.Choi,K.Yu, Y. Kim, A new adaptive component-substitution-based satellite image fusion by 805 using partial replacement, IEEE Transactions on Geoscience and Remote Sensing 49 (1) (2011) 295-309.
(135) Bhatnagar, G., Wu, Q. M. J., & Liu, Z. (2013). Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain. IEEE Transactions on Multimedia, 15(5), 1014–1024. doi:10.1109/tmm.2013.2244870.
(136) Yang, G., Li, M., Chen, L., & Yu, J. (2015). The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density. Computational and Mathematical Methods in Medicine, 2015, 1–13. doi:10.1155/2015/262819.
(137) Du, J., Li, W., Xiao, B., & Nawaz, Q. (2016). Union Laplacian pyramid with multiple features for medical image fusion. Neurocomputing, 194, 326–339. doi:10.1016/j.neucom.2016.02.047.
(138) Zhu, Z., Chai, Y., Yin, H., Li, Y., & Liu, Z. (2016). A novel dictionary learning approach for multi-modality medical image fusion. Neurocomputing, 214, 471–482. doi:10.1016/j.neucom.2016.06.036.
(139) Chen, C.-I. (2017). Fusion of PET and MR Brain Images Based on IHS and Log-Gabor Transforms. IEEE Sensors Journal, 17(21), 6995–7010. doi:10.1109/jsen.2017.2747220.
(140) Du, J., Li, W., & Xiao, B. (2017). Anatomical-Functional Image Fusion by Information of Interest in Local Laplacian Filtering Domain. IEEE Transactions on Image Processing, 26(12), 5855–5866. doi:10.1109/tip.2017.2745202.
(141) Daniel, E. (2018). Optimum Wavelet-Based Homomorphic Medical Image Fusion Using Hybrid Genetic–Grey Wolf Optimization Algorithm. IEEE Sensors Journal, 18(16), 6804–6811. doi:10.1109/jsen.2018.2822712.
(142) Rajalingam B, Priya R,2018, Multimodal Medical Image Fusion Using Various Hybrid Fusion Techniques For clinical Treatment Analysis, Open Journal Systems, Smart Construction Research, Volume 2 Issue 4 | 2018.
(143) Du, J., Li, W., & Xiao, B. (2018). Fusion of anatomical and functional images using parallel saliency features. Information Sciences, 430-431, 567–576. doi:10.1016/j.ins.2017.12.008.
(144) Abdulkareem, M. B. (2018). Design and Development of Multimodal Medical Image Fusion using Discrete Wavelet Transform. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). doi:10.1109/icicct.2018.8472997.
(145) M.M. Fraz, P. Remagnino, A. Hoppe, S. Barman, Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier, in: International Conference on Computer Medical Applications (ICCMA), 2013, DOI:10.1109/ICCMA.2013.6506180
