Abstract
In last years, various medical image fusion algorithms have been proposed to fuse medical image. But, most of them focus on fusing grayscale images. This paper proposes a qualified algorithm for the fusion of multimodal color medical images. The technique of F-transforms has mainly been employed as a fusion technique for images obtained from equal or different modalities. The restriction of fused color mixing RGB, substitution method is resolved by incorporating F-transform and color mixing RGB. The proposed method significantly outperforms the traditional methods in terms of both visual quality and objective evaluation, with improved contrast and overall intensity. The proposed method provides better visual information than the gray ones and more adaptable to human vision. Additional, PCA is functional on the two-level decomposition to maximize the spatial resolution. Experimental evaluation demonstrates that the proposed algorithm qualitatively outperforms many existing state-of-the-art multimodal image fusion algorithms.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
S. F. Nemec, M. A. Donat, S. Mehrain, K. Friedrich, C. Krestan, C. Matula, H. Imhof, and C. Czerny, “CT–MR image data fusion for computer assisted navigated neurosurgery of temporal bone tumors,” Europ. J. Radiol. 62, 192–198 (2007).
Z. Chao and A. A. Sufi, “Color enhancement in image fusion,” in Proc. IEEE Workshop on Applications of Computer Vision WACV 2008 (Copper Mountain, CO, 2008), pp. 1–6.
C. He, Q. Liu, H. Li, and H. Wang, “Multimodal medical image fusion based on HIS and PCA,” Proc. Eng. 7, 280–285 (2010).
S. Daneshvar and H. Ghassemian, “MRI and PET image fusion by combining IHS and retina-inspired models,” Inf. Fusion 11, 114–123 (2010).
S.-H. Lai and M. Fang, “A hierarchical neural network algorithm for robust and automatic windowing of MR images,” Artif. Intellig. Med. 19, 97–119 (2000).
H. Ghassemian, “A retina based multi-resolution image-fusion,” in Proc. IEEE Int. Geoscience and Remote Sensing Symp. IGARSS’01 (NSW, Sydney, 2001), pp. 709–711.
N. Al-Azzawi, H. A. M. Sakim, W. A. K. W. Abdullah, and H. Ibrahim, “Medical image fusion scheme using complex contourlet transform based on PCA,” in Proc. Annu. IEEE Int. Conf. on Engineering in Medicine and Biology Society, EMBC 2009 (Hilton Minneapolis, MI, 2009), pp. 5813–5816.
N. Al-Azzawi and W. A. K. W. Abdullah, “Medical image fusion schemes using contourlet transform and PCA bases,” in Image Fusion and Its Applications, Ed. by Y. Zheng (IntechOpen, 2011), pp. 93–110.
K. Baum, M. Helguera, and A. Krol, “Fusion viewer: A new tool for fusion and visualization of multimodal medical data sets,” J. Digital Imag. 21, 59–68 (2008).
Y. Zheng, E. A. Essock, B. C. Hansen, and A. M. Haun, “A new metric based on extended spatial frequency and its application to DWT based fusion algorithms,” Inf. Fusion 8, 177–192 (2007).
Y. Zhang and G. Hong, “An IHS and wavelet integrated approach to improve pansharpening visual quality of natural colour IKONOS and QuickBird images,” Inf. Fusion 6, 225–234 (2005).
F. E. Ali, I. M. El-Dokany, A. A. Saad, and F. E. A. El- Samie, “Curvelet fusion of MR and CT images,” Progr. Electromagn. Res. 3, 215–224 (2008).
K. G. Baum, M. Helguera, J. P. Hornak, J. P. Kerekes, E. D. Montag, M. Z. Unlu, D. H. Feiglin, and A. Krol, “Techniques for fusion of multimodal images: Application to breast imaging,” in Proc. IEEE Int. Conf. on Image Processing (Atlanta, 2006), pp. 2521–2524.
K. Baum and M. Helguera, “Execution of the SimSET Monte Carlo PET/SPECT simulator in the condor distributed computing environment,” J. Digital Imag. 20, 72–82 (2007).
K. G. Baum, “Multimodal breast imaging: Registration, visualization, and image synthesis,” Ph. D. Degree in Imaging Science (College of Science, Center for Imaging Science Rochester Institute of Technology, Rochester, NY, 2008).
I. Perfilieva, “F-Transform,” in Springer Handbook of Computational Intelligence (Springer, 2015), pp. 113–130.
T. Jionghua, W. Suhuan, Z. Jingzhou, and W. Xue, “Fusion algorithm of medical images based on fuzzy logic,” in Proc. 7th Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD) (Yantai, 2010), pp. 546–550.
I. Perfilieva, M. Daňková, I. Perfilieva, and M. D. Ňková, “Image fusion on the basis of fuzzy transforms,” in Proc. 8th Int. FLINS Conf. on Computational Intelligence in Decision and Control (Madrid, 2008), pp. 471–476.
T. Zaveri, I. Makwana, and M. Zaveri, “A fuzzy based hybrid multispectral image fusion method using DWT,” in Proc. 10th Int. Conf. on Hybrid Intelligent Systems (HIS) (Atlanta, 2010), pp. 13–18.
I. Perfilieva, “F-transform: Theoretical aspects and advanced applications,” Fuzzy Sets Syst. 288, 1–2 (2016).
N. Al-Azzawi, H. A. M. Sakim, and W. A. K. W. Abdullah, “Fast free-form registration based on kullbackleibler distance for multimodal medical image,” in IEEE Symp. on Industrial Electronics and Applications, ISIEA 2010 (Penang, 2010), pp. 484–489.
G. Pajares and J. Manuel de la Cruz, “A wavelet-based image fusion tutorial,” Pattern Recogn. 37, 1855–1872 (2004).
Y. Zheng and Z. Qin, “Objective image fusion quality evaluation using structural similarity,” Tsinghua Sci. Technol. 14, 703–709 (2009).
Z. Li, Z. Jing, X. Yang, and S. Sun, “Color transfer based remote sensing image fusion using non-separable wavelet frame transform,” Pattern Recogn. Lett. 26, 2006–2014 (2005).
Y. Zheng, E. A. Essock, and B. C. Hansen, “Advanced discrete wavelet transform fusion algorithm and its optimization by using the metric of image quality index,” Opt. Eng. 44, 037003 (1–12) (2005).
Author information
Authors and Affiliations
Corresponding author
Additional information
The article is published in the original.
Nemir Ahmed Al-Azzawi received his B.Sc. degree (with honors) in Electrical Engineering, College of Engineering University of Al-Mustansiriya, Iraq, in 1994. He received his M.Sc. Electronics and Communication, in College of Engineering University of Baghdad, Iraq, in 1998. Received Ph.D. degree in BioMedical Electronics in 2011, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus. His research interests include speech compression, digital image processing and medical image fusion and registration, machine learning, data mining and computer vision. Currently he is a head of mechatronics department, Al-Khwarizmi college of engineering, University of Baghdad. Author of 25 papers.
Rights and permissions
About this article
Cite this article
Al-Azzawi, N.A. Color Medical Imaging Fusion Based on Principle Component Analysis and F-Transform. Pattern Recognit. Image Anal. 28, 393–399 (2018). https://doi.org/10.1134/S105466181803001X
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S105466181803001X