摘要
光学显微成像技术已经成为生命科学研究的强有力工具,结合荧光标记技术,人们可以从荧光显微图像中获得更为清晰的样品结构信息。但是,由于成像系统衍射和噪声干扰等因素,荧光显微图像不可避免出现模糊和噪声等退化现象。特别是厚组织成像时,随着深度的增加,生物样品对荧光的散射作用导致图像退化更加严重。图像复原技术作为后处理手段,是消除噪声和成像模糊,恢复样品荧光物体本来面目的有效途径。因此,本文针对厚组织的荧光显微图像的复原方法进行了深入研究,同时,在提高成像信噪比的随机扫描成像系统设计方面也做了一些有益的探索。主要研究工作如下:
研究了荧光显微成像系统的成像模型,对系统中的噪声来源进行了分析,认为光子计数噪声是其主要噪声,该噪声与荧光本身相关而不可避免。泊松噪声成像模型符合探测器的光子统计特性,比高斯噪声模型更适用于荧光成像。探讨了常用荧光显微系统的点扩散函数及光学分辨率。研究了荧光显微图像的反卷积技术,对代表性的算法行了实验验证和简单结果分析。结果表明,反卷积结果都存在不同程度的噪声放大,出现了振铃状结构假象。
反卷积之前对观测图像进行预去噪处理无疑是一种明智的方案。因此,深入研究了以Perona-Malik(P-M)非线性各向异性扩散方程为基础的边缘保持型去噪算法。分析了P-M方程边缘保持去噪的原理及不足,提出了一种融入图像局部方差信息的改进P-M扩散算法,算法既有效地消除了随机噪声又较好地保持了边缘结构。在此基础上,为进一步提高保边性能,提出了一种基于鲁棒估计子和像素局部统计特征的鲁棒P-M扩散自适应去噪算法,该算法在噪声消除的同时更好地保持了图像边缘结构,且具有收敛速度快、算法稳定的优点。模拟图像、标准测试图像和厚组织实际荧光显微切片图像的试验结果均证实了算法的有效性。
正则化方法是解决反卷积不适定问题的有效方法。因此,深入研究了荧光显微图像Richardson-Lucy反卷积的正则化方法。在对最大熵复原研究的基础上,提出了鲁棒P-M扩散预去噪结合最大熵正则化的Richardson-Lucy反卷积方案。同时,由于Tikhonov和Total Variation正则化函数在形式上相似,在分析它们正则作用不足的基础上,提出了一种Tikhonov和Total Variation相结合的混合正则化方案。试验结果表明,两种方案均能一定程度上复原样品原始结构信息。
通过随机扫描控制延长激发光在感兴趣区域的停留时间用于荧光信号积分,是从荧光图像来源上提高图像信噪比最本质的解决办法。因此,针对自行构建的随机扫描双光子荧光显微成像系统的声光偏转器扫描的精确时间控制,提出了一种基于数据采集卡子系统间时钟关系的高速时间同步方法,实现了精确的激光扫描控制和灵活设置荧光信号积分时间。
Combined with fluorescence labeling technique, fluorescence microscopy is becoming a powerful tool to investigate activities and morphologies of specimens in life science. However, image stacks are always inevitably blurred due to the diffraction limit and distorted by noises. The degradation increases with depth when imaging in deep tissue due to strong and multi-scattering of specimens. As a post-processing approach, image restoration is a potential technique to reverse the degradation and restore the original fluorescence objects. Therefore, the algorithms of denoising and deconvolution for deep tissue are well investigated in this dissertation. In addition, attempts to realize the random access controlling of laser and obtain variable integral time of fluorescence signal through the design of random scanning two-photon fluorescence microscope are explored. The main subjects are as follows:
The imaging model of fluorescence microscope is studied. First, different sources of noise are analyzed and the photon counting noise, which is naturally induced by Poisson counting procedure of detector and dependent on fluorescence signal, is considered as the main one and can not be avoided. So Poisson imaging model is more suitable for image restoration than Gaussian model because it coincide with the natural properties of photon detection. Second, point spread function and optical resolutions of three typical fluorescence microscopes are discussed. Last, almost all of the deconvolution techniques for fluorescence image are comparably introduced, and some typical deconvolution algorithms are studied either theoretically or experimentally. The conclusion can be drawn that noise amplifying will occur in restored results more or less, and some tedious ringing artifacts are reproduced too.
More literatures are shown that it is a wise approach to pre-filter noise before performing the deconvolution. So the edge-persevering denoising algorithms based on the Perona-Malik (P-M) nonlinear anisotropic diffusion equation are studied thoroughly. First of all, the principles of edge-persevering and the drawback of P-M diffusion equation are discussed, and then a modified scheme that the local variation of pixel is incorporated in diffusion coefficient is proposed. The modified algorithm performs well in random noise removing and edge preserving. Furthermore, in order to enhance the performance of edge-preserving, another adaptive and robust P-M diffusion algorithm is proposed, which combines the robust estimator with two local statistic parameters of pixel and obtains better edge preserving. Experimental results of synthetic, standard image and real fluorescence image slices in deep tissue show the better performance of proposed scheme in edge-persevering and noise removing, and the fast convergence and stable solution is obtained, too.
Regularization technique is more vital to moderate the degree of ill-posed of deconvolution and it is well studied based on Richardson-Lucy deconvolution in this dissertation. A novel scheme that pre-filters noise employed modified robust anisotropic P-M diffusion then performs maximum entropy regularized Richardson-Lucy deconvolution is proposed, which is based on maximum entropy regularization technique. Meanwhile, another hybrid regularization scheme is also proposed too, which integrate Tikhonov regularization with Total Variation regularization together on the consideration of their similar model and their principles of regularization and drawbacks. Experiments show the limited effects on restored objects.
Last, it is an essential alternative to improve the signal-to-noise of fluorescence image by exactly controlling the laser scanning and prolonging the dwell-time of regions of interesting of specimens. However, the precise controlling of acousto-optic deflector is a significant puzzle. Therefore, a high-speed timing synchronization approach between the clock relationships of two sub-systems in only one multi-functions board is proposed in the design of custom-built random access scanning two-photon fluorescence microscopy. It makes possible that laser scanning can be controlled accurately and dwell-time of fluorescence signal integral can be set flexibly.
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