The application of multi-modality medical image fusion based method to cerebral infarction

Springer Science and Business Media LLC - Tập 2017 - Trang 1-16 - 2017
Yin Dai1,2, Zixia Zhou3, Lu Xu4
1Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China
2China Medical University, Shenyang, China
3Department of Electronic Engineering, Fudan University, Shanghai, China
4Biomedical Scince and Medical Engineering School, Beihang University, BeiJing, China

Tóm tắt

A multi-modality image fusion can process images of certain organs or issues which were collected from diverse medical imaging equipment. The fusion can extract complementary information and integrate into images with more comprehensive information. The multi-modality image fusion can provide image that was combined with anatomical and physiological information for doctors and bring convenience for diagnosis. Basically, the thesis mainly studies the fusion of MRI and CT images, while taking the cerebral infraction-suffered patients’ images as example. Furthermore, T1 and DWI sequences are respectively carrying on wavelet fusion, pseudo color fusion, and α channel fusion. Meanwhile, the numerous image data will be objectively assessed and compared from several aspects such as information entropy, mutual information, the mean grads, and spatial frequency. By means of the observation and analysis, compared with original image, it can be figured out that fused image not only has richer details but also more clearly highlights the lesions of cerebral infarction.

Tài liệu tham khảo

R. Stokking, I.G. Zubal, M.A. Viergever, Display of fused images: methods, interpretation, and diagnostic improvements. Seminar. Nucl. Med. 33(3), 219–227 (2003) G.M. Rojas, U. Raff, Image fusion in neuroradiology: three clinical examples including MRI of Parkinson disease. Comput. Med. Imag. Grap. 31(1), 17–27 (2007) G. Shruti, K. Ushah Kiran, R. Mohan, Multilevel Medical Image Fusion Using Segmented Image by Level set Evolution with Region Competition (Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 2005), pp. 1–4 W. Quangui, Application of window technology in CT diagnosis. Pract. Med. J. 18(03), 286 (2011) L. Liguo, Effects of CT image factors. Journal of Medical Science 30(0307), 02 (2014) L. Wang, Research and Development of Multi-Phase Tissue 3D Visualization System Based on Medical Image (Hebei University of Technology, Tianjin, 2009) Z. Weijian, The basic principle and medical application of X-CT. Acad. Forum 099(30), 188–193 (2010) K. Xu, Medical Image Enhancement Processing and Analysis (Jilin University, Changchun, 2006) D.W. Townsend, Multimodality imaging of structure and function. Phys. Med. Biol. 53(4), R1–R39 (2008) W.R. Crum, L.D. Griffin, D.L.G. Hill, D.J. Hawkes, Zen and the art of medical image registration: Correspondence, homology, and quality. Neuro Image 20(3), 1425–1437 (2003) J. Tsao, Interpolation artifacts in multimodality image registration based on maximization of mutual information. IEEE T Med. Imaging 22(7), 854–864 (2003) J. Zhang, Z. Zhou, J. Teng, T. Li, in 2nd International Conference on Biomedical Engineering and Informatics. Fusion algorithm of functional images and anatomical images based on wavelet transform (IEEE press, Tianjin, 2009), pp. 215–219 W. Ge, L. Gao, Multi-modality medical image fusion algorithm based on non-separable wavelet. Appl. Res. Comput. 26(5), 1965–1967 (2009) S.G. Mallat, A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989) L. Yang, B. Guo, W. Ni, Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing 72(1), 203–211 (2008) R.C. Gonnzalez, R.E. Woods, Digital image processing. (Publishing House of Electronics Industry, Beijing, 2011) A. Nishie, A.H. Stolpen, M. Obuchi, Evaluation of locally recurrent pelvic malignancy: Performance of T2- and diffusion-weighted MRI with image fusion[J]. J. Magn. Reson. Imaging 28, 705–713 (2008) P. Bhargavi, H. Bindu, A Novel Medical Image Fusion with Color Transformation. Int. Conference Comput. Commun. Inform. 01, 08–10 (2015) J. Xiaoyu, Multi-image fusion based on false color. J. Beijing Inst. Technol. 17(5), 645–649 (1997) T. Porter, T. Duff, Compositing digital images. Comput. Graph. 18, 253–259 (1984) A.R. Smith, Alpha and the history of digital compositing. Microsoft Technical Memo 24(2010), 235-238 (1995) A. Toet, J.M. Valeton, L.J. van Ruyven, Merging thermal and visual images by a contrast pyramid. Opt. Eng. 28, 287789–287792 (1989) M. Ignotte, A multiresolution markovian fusion model for the color visualizatioin of hyperspectral image. IEEE T Geosci Remote. 48(12), 4236–4247 (2010) G. Piella, New quality measures for image fusion, The 7th International Conference on Information Fusion. Opt. Eng. (1) 542–546 (2004) Liu Cheng, Wang Xingwu. Medical imaging diagnosis. People’s Medical Publishing House