基于内源信号的脑功能光学成像图像处理方法研究进展

doi:10.3969/j.issn.0258-8021.2012.06.019

Abstract
Figure/Table
References (0)
Related Citation (2)

Download: PDF (596 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Optical imaging based on intrinsic signals is a novel technique for brain functional imaging, which can help researchers explore the brain function more effectively because of its high spatial resolution, simple structure and long-time in-vivo recording. However, the functional signals recorded by the optical imaging based on intrinsic signals are usually quite weak, it is very necessary to investigate appropriate image processing methods to improve the SNR. This article reviewed the research progress of the image processing methods in optical imaging based on intrinsic signals. After introducing the traditional image processing methods, we mainly introduced some novel image processing approaches including principal component analysis, independent component analysis, local similarity minimization, receiver operating characteristic curve and indicator function. It has been demonstrated that the novel image processing methods can effectively improve the image quality.

Key words： optical imaging based on intrinsic signals image processing methods SNR

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LU YunDa¹LU Yi Liang¹CHEN Yao¹CHAI XinYu^1REN Qiu Shi² LI Li Ming^1*

Cite this article:

LU YunDa¹LU Yi Liang¹CHEN Yao¹CHAI XinYu^1REN Qiu Shi² LI Li Ming^1*
. Progress in Image Processing Methods of Optical Imaging Based on Intrinsic Signals[J]. journal1, 2012, 31(6): 934-940.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2012.06.019 OR http://cjbme.csbme.org/EN/Y2012/V31/I6/934

［1］俞洪波，邢大军，寿天德. 脑内源信号光学成像术：猫视皮质方位功能柱的活体显示［J］. 中国神经科学杂志, 2000, 16(4): 355-359.
［2］Grinvald A, Shoham D, Shmuel A et al. Invivo optical imaging of cortical architecture and dynamics ［M］// Windhorst U, Johansson H. Modern Techniques in Neuroscience Research. Springer Verlag, 2001.
［3］Grinvald A. Functional architecture of cortex revealed by optical image of intrinsic signals ［J］. Nature, 1986, 324(6095): 361-364.
［4］张鲲，俞洪波，寿天德. 应用基于内源信号的光学成像技术的视觉脑研究现状［J］. 动物学研究, 2000, 21(1): 7-11.
［5］张鲲，俞洪波，寿天德. 基于内源信号的脑光学成像系统的研制［J］. 生物物理学报, 1999, 15(3): 597-604.
［6］Grinvald A, Frostig R, Siegel R, et al. Highresolution optical imaging of functional brain architecture in the awake monkey ［J］. Proceedings of the National Academy of Sciences of the United States of America, 1991, 8824): 11559-11563.
［7］陈昕，寿天德. 用脑光学成像精确测定猫初级视皮层视野拓扑投射关系［J］. 生理学报, 2003, 55(50): 541-546.
［8］Shmuel A, Grinvald A. Functional organization for direction of motion and its relationship to orientation maps in cat area 18 ［J］. Journal of Neusoscience, 1996, 16(21): 6945-6964.
［9］Harel N, Mori N, Sawada S, et al. Three distinct auditory areas of cortex (AI, AII, and AAF) defined by optical imaging of intrinsic signals ［J］. NeuroImage, 2000, 11(4): 302-312.
［10］Spitzer MW, Calford MB, Clarey JC, et al. Spontaneous and stimulus evoked intrinsic optical signals in primary auditory cortex of the cat ［J］. Journal of Neurophysiology, 2001, 85(3): 1283-1298.
［11］Tsytsarev V, Pope D, Pumbo E, et al. Intrinsic optical imaging of directional selectivity in rat barrel cortex: Application of a multidirectional magnetic whisker stimulator ［J］. Journal of Neuroscience Methods, 2010, 189(1): 80-83.
［12］Rubin B, Katz L. Optical imaging of odorant representations in the mammalian olfactory bulb ［J］. Neuron, 1999, 23(3): 499-511.
［13］Belluscio L, Katz L. Symmetry, stereotypy, and topography of odorant representations in mouse olfactory bulbs ［J］. Journal of Neuroscience, 2001, 21(6): 2113-2122.
［14］Yuan Q, Harley C, Mclean J, et al. Optical imaging of odor preference memory in the rat olfactory bulb ［J］. Journal of Neurophysiology, 2002, 87(6): 3156-3159.
［15］Lin AJ, Koike MA, Green KN, et al. Spatial frequency domain imaging of intrinsic optical property contrast in a mouse model of Alzheimers disease ［J］. Annals of Biomedical Engineering, 2011, 39(4): 1349-1357.
［16］Li Chaoyi, Cui Wanxing, Wang Xujing, et al. Intrinsic optical signal imaging of glucosestimulated insulin secreting β-cells ［J］. Optics Express, 2011, 19(1): 99-106.
［17］Mayhew J, Askew S, Zheng Y, et al. Cerebral vasomotion: 01-Hz oscillation in reflected light imaging of neural activity ［J］. NeuroImage, 1996, 4(3): 183-193.
［18］Zhan C, Baker J. Boundary cue invariance in cortical orientation maps ［J］. Cerebral Cortex, 2006, 16(6): 896-906.
［19］Hubel D, Wiesel T. Receptive fields, binocular interactions and functional architecture in the cats visual cortex ［J］. Journal of Physiology, 1962, 160(1): 106-154.
［20］Gabbay M, Brennan C, Kaplan E, et al. A principal componentsbased method for the detection of neuronal activity maps: Application to optical imaging ［J］. NeuroImage, 2000, 11(4): 313-325.
［21］Stetter M, Schiessl　I, Otto T, et al. Principal components analysis and blind separation of source for optical imaging of intrinsic signals ［J］. NeuroImage, 2000, 11(5): 482-490.
［22］Carmona RA, Hwang WL, Frostig R. Wavelet analysis for brain function imaging ［J］. IEEE Transaction on Medical Imaging, 1995, 14(3): 556-564.
［23］Sornborger A, Yokoo T, Delorme A, et al. Extraction of the average and differential dynamical response in stimuluslocked experimental data ［J］. Journal of Neuroscience Methods, 2005, 141(2): 223-229.
［24］Sornborger A, Sailstad C, Kaplan E, et al. Spatiotemporal analysis of optical imaging data ［J］. NeuroImage, 2003, 8(3): 610-621.
［25］Jung T, Makeig S, Mckeown M, et al. Imaging brain dynamics using independent component analysis ［J］. Proceeding of IEEE, 2001, 89(7): 1107-1122.
［26］Comon P. Independent component analysis, a new concept? ［J］. Signal processing, 1994, 36(3): 287-314.
［27］Bell A and Sejnowski T. An informationmaximization approach to blind separation and blind deconvolution ［J］. Neural Computation, 7(6): 1129-1159.
［28］Hyvrinen A, Oja E. Independent component analysis: algorithms and applications ［J］. Neural Networks, 2000, 13(4-5): 411-430.
［29］Hyvrinen A. Fast and robust fixedpoint algorithms for independent component analysis ［J］. IEEE Transaction on Neural Networks, 1999, 10(3): 626-634.
［30］Fekete T, Omer DB, Naaman S, et al. Removal of spatial biological artifacts in functional maps by local similarity minimization ［J］. Journal of Neuroscience Methods, 2009, 178(1): 31-39.
［31］Chen SC, Wong YT, Hallum LE, et al. Optical imaging of electrically evoked visual signals in cats: II. ICA “Harmonic filtering” Noise reduction ［C］// Proceedings of the 29th Annual International Conference of the IEEE EMBS. Lyon: IEEE, 2007: 3380-3383.
［32］Gopathy P, Ilya K, Casagrande VA. Quantification of optical images of cortical responses for inferring functional maps ［J］. Journal of Neurophysiology, 2009, 101(5): 2708-2724.
［33］Everson R, Prashanth A, Gabbay M, et al. Representation of spatial frequency and orientation in the visual cortex ［J］. Neurobiology, 1998, 95(14): 8334-8338.
［34］Everson R, Knight B, Sirovich L. Separating spatially distributed response to stimulation from background. I. Optical imaging ［J］. Biological Cybernetics, 1997, 77(6): 407-417.［35］Yokoo T, Knight B, Sirovich L. An optimization approach to signal extraction from noisy multivariate data ［J］. NeuroImage, 2001, 14(6): 1309-1326.