A novel implementation of medical image registration and fusion using ASM-SSIF

  • Abstract
  • Keywords
  • References
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  • Abstract

    In medical imaging a single image of a targeted organ is captured using different imaging modalities such as MR (Magnetic Resonance) and CT (Computed Tomography), to get desired information from the region of interest. For better diagnosis the information obtained from these two modalities has to be combined into a single image. Image fusion is a process of integrating useful or complementary information from multiple images into a single image. In this work, we proposed a novel method for image fusion with biomedical images i.e., MR and CT images, by formulating a convex optimization problem. The optimization problem uses an Active Slope Meagerness (ASM) regularizer with statistics based steered image filtration (SSIF). Precise registration is needed by the MR and CT image fusion, while the misalignment is quite hard to obviate during the preprocessing step. To surmount this, we had proposed a robust and new approach for both registration as well as the fusion of MR and CT images. Our approach focuses on simultaneous registration during the fusion procedure. Initially, the MR image is focalized to enhanced resolution, which permits us for more precise registration of images. At the same time, fusion of images can be done precisely by extinguishing the misalignment gradually. We iteratively persist these two procedures until the convergence. The performance of the algorithm is judged both qualitatively and quantitatively. The Proposed ASM-SSIF method is compared with the existing methods. The simulation results showed that our algorithm has given greater performance by enhancing the overall fusion quality of MR and CT images in terms of image quality assessment. Specifically, our proposed approach is shown to be much more powerful on the medical data sets of real-world with pre-registration errors.



  • Keywords

    Active Slope Meagerness Regularize; Fast Iterative Shrinkage Thresholding Algorithm; Image Registration; Image Fusion; Image Quality Assessment Metrics; MR and CT Imaging; Steerable Image Filtration; Statistics and Vectorial Total Variation.

  • References

      [1] Rajiv Singh, “Medical image fusion: applications, approaches and evaluation, in International conference on Medical Imaging and Diagnosis, Chicago, USA, (2016).

      [2] Friston, K., Ashburner, J., Frith, C., Poline, J. Heather, J., & Frackowiak, R., “Statistical parametric maps in functional imaging: a general linear approach”, Hum Brain Mapp 2, (2004), 189–210.

      [3] Pengqiang, Z., Xuchu, Y., Li, H., & Lihua, S., “Automatic registration of airborne image sequences based on line matching approach”, ISPRS 37, (2008).

      [4] Sarvaiya, J., Patnaik, S., & Kothari, K., “Feature based image registration using Hough Transform”, Surat, Gujarat, India: National Institute of Technology, (2013).

      [5] Wang, X., Feng, D., & Jin, J., “Elastic Medical Image Registration Based on Image Intensity”, Pan-Sydney Area Workshop on Visual Information Processing, Sydney: Australian Computer Society, (2001).

      [6] Zheng, L.-T., Qian, G.-P., & Lin, L.-F., “Medical Image Registration Based on Improved PSO Algorithm”, In J. Lee (Ed.), Advanced Electrical and Electronics Engineering, Lecture Notes on Electrical Engineering, Springer: Berlin Heidelberg, Vol. 87, (2011), 487–494.

      [7] J. B. Antoine Maintz and Max A. Viergever, “A survey of medical image registration”, Medical Image Analysis, Vol. 2, No. 1, (1998), 1-36.

      [8] Hamza A, He Y, Krim H, Willsky A., “A multiscale approach to pixel-level image fusion”, Integrated Computer. Aided Engineering, Vol. 12, No. 2, (2005), 135– 146.

      [9] B. Zitova, J. Flusser, “Image registration methods: a survey”, Image and Vision Computing, Vol. 21, No. 11, (2003), 977–1000.

      [10] M. Wyawahare, P. Patil, H. Abhyankar, “Image registration techniques: an overview”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 2, No. 3, (2009), 11–28.

      [11] J.B. Antoine Maintz , Stefan Klein , Keelin Murphy , Marius Staring , Josien P.W. Pluim, “A survey of medical image registration – under review”, Medical Image Analysis, Vol. 33, (2016), 140-144.

      [12] D. Hill, P. Batchelor, M. Holden, D. Hawkes, “Medical image registration”, Physics in Medicine and Biology, Vol. 46, No. 3, (2001), 1-45.

      [13] K. Bhatia, J. Hajnal, A. Hammers, D. Rueckert, “Similarity metrics for group wise non-rigid registration”, In: Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, (2007), 544–552.

      [14] P. Markelj, D. Toma ˇ Zevi ˇ c, B. Likar, F. Pernu ˇ s, “A review of 3D/2D registration methods for image-guided interventions”, Medical Image Analysis, Vol. 16, No. 3, (2012), 642–661.

      [15] F.P. Oliveira, J.M.R. Tavares, “Medical image registration: a review”, Computer Methods in Biomechanical and Biomedical Engineering, Vol. 17, No. 2, (2012), 73–93.

      [16] W. Crum, T. Hartkens, D. Hill, “Non-rigid image registration: theory and practice”, Br. J. Radiol. Vol. 77, (2004), 140-153

      [17] A.Sotiras, C. Davatzikos, N. Paragios, “Deformable medical image registration: a survey”, IEEE Transactions on Medical Imaging, Vol. 32, No. 7, (2013), 1153–1190.

      [18] H. Stone, R. Wolpov, “Blind cross-spectral image registration using pre-filtering and Fourier-based translation detection”, IEEE Transactions Geoscience and Remote Sensing, Vol. 40, No. 3, (2002), 637–650.

      [19] M. Pfingsthorn, A. Birk, S. Schwertfeger, H. Bülow, K. Pathak, “Maximum likelihood mapping with spectral image registration”, Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Anchorage, USA, (2010), 4282–4287

      [20] M. Hasan, X. Jia, A. Robles-Kelly, J. Zhou, M.R. Pickering, “Multi-spectral remote sensing image registration via spatial relationship analysis on sift key points”, Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, USA, (2010), 1011–1014.

      [21] De I, Chanda B, Chattopadhyay B, “Enhancing effective depth-of-field by image fusion using mathematical morphology”, Image and Vision Computing, Vol. 24, No. 12, (2006), 1278–1287.

      [22] Yang B, Li S, “Multi-focus image fusion based on spatial frequency and morphological operators”, Chinese Optical Letters, Vol. 5, No. 8, (2007), 452–453.

      [23] Fasbender D, Radoux J, Bogaert P., “Bayesian data fusion for adaptable image pan sharpening”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 6, (2008), 1847–1857.

      [24] Shen R, Cheng I, Shi J, Basu A. Generalized random walks for fusion of multi-exposure images. IEEE Transactions on Image Processing, Vol.20, (2011), 3634–3646.

      [25] Xu M, Chen H, Varshney PK, “An image fusion approach based on Markov random fields”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 12, (2011), 5116–5127

      [26] Hong Zhang, Xiao-Nan Sun, Lei Zhao, Lei Liu, “Image Fusion Algorithm Using RBF Neural Networks”, In 3rd International Workshop on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, (2008), 417-424.

      [27] Hong Jiang, Yufen Tian, “Fuzzy image fusion based on modified Self-Generating Neural Network”, Expert Systems with Applications, Vol. 38, No. 7, (2011), 8515-8523.

      [28] Nianyi Wang, Yide Ma, Kun Zhan, “spiking cortical model for multi focus image fusion”, Neurocomputing, Vol. 130, (2014), 130: 44-51.

      [29] Peter J. Burt, Edward H. Adelson, “The Laplacian Pyramid as a Compact Image Code”, IEEE Transactions on Communications, Vol. 31, No. 4, (1983), 532-540.

      [30] Akanksha Sahu, Vikrant Bhateja, Abhinav Krishn and Himanshi, “Medical Image Fusion with Laplacian Pyramids”, In International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), Greater Noida, India, (2014), 448-453

      [31] Hannandeep Kaur and Jyothi Rani, “Image Fusion on Digital images using Laplacian Pyramid with DWT”, In 3rd International Conference on Image Information Processing, Waknaghat, India, (2015), 393-398.

      [32] Deepali Sale, Varsha Patil, Dr. Madhuri A. Joshi, “Effective Image Enhancement Using Hybrid Multi Resolution Image Fusion”, In IEEE Global Conference on Wireless Computing and Networking, Lonavala, India, (2014).

      [33] Bhavana V and Krishnappa H K., “Multi-modality Medical Image Fusion Using Discrete Wavelet Transform”, In 4th International Conference on Eco-friendly Computing and Communication Systems (ICECCS), Vol. 70, (2015), 625-631.

      [34] M D Nandesh and M Meenakshi, “A Novel Technique of Medical Image Fusion Using Stationary Wavelet Transform and Principle Component Analysis”, In International Conference on Smart Sensors and Systems (IC-SSS), Bangalore, Inida, (2015).

      [35] V P S Naidu, “Image Fusion Technique Using Multi-resolution Singular Value Decomposition”, Defense Science Journal, Vol. 61, No. 5, (2011), 479-484.

      [36] Junli Liang, Yang He and Ding Liu., “Image Fusion Using Higher-order Singular Value Decomposition”, IEEE Transactions on Image Processing, Vol. 21, No. 5, (2012), 2898-2909.

      [37] Bavirisetti DP and Dhuli R., “Two-scale image fusion of visible and infrared images using saliency detection”, Infrared Physics and Technology, Vol 76, (2016), 52–64.

      [38] Zhao J, Feng H, Xu Z, Li Q and Liu T., “Detail enhanced multisource fusion using visual weight map extraction based on multi scale edge preserving decomposition”, Optics Communications, Vol. 287, (2013), 45–52.

      [39] Jiang Y and Wang M., “Image fusion using multiscale edge preserving decomposition based on weighted least squares filter”, IET Image Processing, Vol. 8, No. 3, (2014), 183–190.

      [40] Li S, Kang X, Hu J., “Image fusion with guided filtering”, IEEE Transactions on Image Processing, Vol. 22, No. 7, (2013), 2864–2875.

      [41] Bavirisetti DP and Dhuli R. “Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform”, IEEE Sensors Journal, Vol. 16, No. 1, (2016), 203–209.

      [42] A.Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems”, SIAM Journal on Imaging Sciences, Vol. 2, No. 1, (2009), 183–202.

      [43] J. Huang, C. Chen, and L. Axel, “Fast Multi-contrast MRI Reconstruction”, Magnetic Resonance Imaging, Vol. 32, No. 10, (2012), 1344-1352.

      [44] C. Chen, Y. Li, and J. Huang, “Calibration less Parallel MRI with Joint Total Variation Regularization”, In International Conference on Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, Springer, Vol. 8151, (2013), 106–114

      [45] L Zhan and XiuXia Ji, “CT and MR Images Fusion Method based on Non-sub sampled Contourlet Transform”, In 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, (2016), 257-260.

      [46] C V Rao, J Malleswara Rao, A Senthil Kumar, D S Jain and V K Dhawal, “Satellite image Fusion using Fast Discrete Curvelet Transforms”, In IEEE International Advance Computing Conference, Gurgaon, India, (2014). 952-957.




Article ID: 19328
DOI: 10.14419/ijet.v7i3.29.19328

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