激光共焦扫描显微镜系统中显微图像处理的研究
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摘要
激光共焦扫描显微镜作为一种具有高纵深分辨率的现代显微成像设备，在生物医学等领域中具有重要的应用价值。它可用于细胞和组织的形态检测和定位、立体结构重组、动态变化过程的研究，为定量荧光测定、定量图像分析等提供了研究手段。为了充分发挥激光共焦扫描显微镜的优势，需要在显微图像处理技术方面进行研究，以建立一个基于激光共焦扫描显微镜的图像处理技术平台，为生物医学显微图像的分析提供强大的支持。本文在显微图像复原、显微图像的滤波、增强处理以及序列断层图像的三维重构等方面开展了深入的研究，完成了整个软件系统的开发工作。主要研究工作包括以下几个方面：
点扩展函数的估计对图像复原具有重要的作用，本文根据误差曲线估算了点扩展函数的参数，并由此实现了激光共焦扫描显微图像的复原；根据激光共焦扫描显微镜采集到的显微图像具有丰富细节的特点，改进了NAS-RIF算法，实现了细节特征明显的显微图像的复原。
深入分析了图像滤波、图像增强算法，为了尽量不损害图像中的边缘信息及各种细节，提出并实现了根据显微图像特点设计的基于自适应的非线性滤波算法、累积增强算法以及空间混合图像增强算法。
研究了三维数据场可视化的两类典型的绘制方法，在激光共焦扫描显微镜系统中，根据检测精细物体表面信息的特点提出了一种新的三维重构面绘制算法，较传统的面绘制算法，表面的构造更加简洁，只需要高度信息，便可构造出表面并显示出生动的三维图形；介绍了体绘制重构算法，在激光共焦扫描显微镜系统的三维重构中，采用了基于光线投射的技术，为提高算法的处理速度，在投影时引入了错切-变换算法的思想，较好地实现了序列断层图像的三维重构。
在解决以上问题的基础上，结合大量的图像处理理论和技术，完成了激光共焦扫描显微镜软件系统的设计。通过对实际的激光共焦扫描显微镜采集到的显微图像处理，证明了其理论意义和应用价值，为深入开展生物医学分析工作创造了条件。
Laser Confocal Scanning Microscopy (LCSM) is a microscopic instrument which provideshigh depth (Z-axis) resolution. It plays an important role in biomedical science. It can beused in studying morphology, recombination of three dimensional (3D) structures anddynamics of cell and tissue. It has been widely used for quantitative fluorescencemeasurement and quantitative imaging analysis in biomedical science. To take fulladvantage of LCSM, it is necessary to improve microscopic image processing technologyto develop a platform for LCSM to support high resolution image analysis in biomedicalapplications. To this end, this study investigated microscopic image restoration, imageprocessing and three dimensional (3D) image reconstruction from an array of 2D images inLCSM system. This study presents improved algorithms and new methods for LCSMimage processing in the following areas:
1. The determination of Point Spread Function (PSF) is essential for image restoration.This paper has developed an improved deconvolution algorithm for estimating PSFusing error curve analysis. In addition, to improve the restoration of image detail inLCSM, the study improved NAS-RIF method to achieve the fine details restoration ofLCSM image.
2. This paper has proposed and implemented anisotropic diffusion filtering based onadaptive filtering, accumulated image enhancing and spatial mixing image enhancingalgorithms for microscopic image processing, without losing of edge information andother details of image.
3. Based on the study of two typical rendering methods for visualization of 3D data field,this paper proposes a new surface rendering algorithm of 3D image reconstructionbased on the examination of the surface information of object. Compared to traditionalsurface rendering method, the newly proposed algorithm simplifies surface rendering,which requires only height to generate vivid, realistic 3D dimension image. The 3Dimage reconstruction using volume rendering was discussed in detail. In LCSM 3Dimage reconstruction, Shear-Warp was incorporated into light reflection algorithm toimprove image processing speed. As a result 3D image was effectively reconstructedfrom a stack of 2D images.
4. In addition to the above results, this study has constructed a framework and technicalplatform for biomedical image processing of LCSM. In conclusion, the improved biomedical images processing algorithms and platform weretested and verified in the LCSM image processing. The test results show that the improvedalgorithms and new methods as well as the platform can effectively process and analyzemicroscopic images of LCSM in practice. The results of this research can be a valuable forthe future research in biomedical image analysis.
点扩展函数的估计对图像复原具有重要的作用，本文根据误差曲线估算了点扩展函数的参数，并由此实现了激光共焦扫描显微图像的复原；根据激光共焦扫描显微镜采集到的显微图像具有丰富细节的特点，改进了NAS-RIF算法，实现了细节特征明显的显微图像的复原。
深入分析了图像滤波、图像增强算法，为了尽量不损害图像中的边缘信息及各种细节，提出并实现了根据显微图像特点设计的基于自适应的非线性滤波算法、累积增强算法以及空间混合图像增强算法。
研究了三维数据场可视化的两类典型的绘制方法，在激光共焦扫描显微镜系统中，根据检测精细物体表面信息的特点提出了一种新的三维重构面绘制算法，较传统的面绘制算法，表面的构造更加简洁，只需要高度信息，便可构造出表面并显示出生动的三维图形；介绍了体绘制重构算法，在激光共焦扫描显微镜系统的三维重构中，采用了基于光线投射的技术，为提高算法的处理速度，在投影时引入了错切-变换算法的思想，较好地实现了序列断层图像的三维重构。
在解决以上问题的基础上，结合大量的图像处理理论和技术，完成了激光共焦扫描显微镜软件系统的设计。通过对实际的激光共焦扫描显微镜采集到的显微图像处理，证明了其理论意义和应用价值，为深入开展生物医学分析工作创造了条件。
Laser Confocal Scanning Microscopy (LCSM) is a microscopic instrument which provideshigh depth (Z-axis) resolution. It plays an important role in biomedical science. It can beused in studying morphology, recombination of three dimensional (3D) structures anddynamics of cell and tissue. It has been widely used for quantitative fluorescencemeasurement and quantitative imaging analysis in biomedical science. To take fulladvantage of LCSM, it is necessary to improve microscopic image processing technologyto develop a platform for LCSM to support high resolution image analysis in biomedicalapplications. To this end, this study investigated microscopic image restoration, imageprocessing and three dimensional (3D) image reconstruction from an array of 2D images inLCSM system. This study presents improved algorithms and new methods for LCSMimage processing in the following areas:
1. The determination of Point Spread Function (PSF) is essential for image restoration.This paper has developed an improved deconvolution algorithm for estimating PSFusing error curve analysis. In addition, to improve the restoration of image detail inLCSM, the study improved NAS-RIF method to achieve the fine details restoration ofLCSM image.
2. This paper has proposed and implemented anisotropic diffusion filtering based onadaptive filtering, accumulated image enhancing and spatial mixing image enhancingalgorithms for microscopic image processing, without losing of edge information andother details of image.
3. Based on the study of two typical rendering methods for visualization of 3D data field,this paper proposes a new surface rendering algorithm of 3D image reconstructionbased on the examination of the surface information of object. Compared to traditionalsurface rendering method, the newly proposed algorithm simplifies surface rendering,which requires only height to generate vivid, realistic 3D dimension image. The 3Dimage reconstruction using volume rendering was discussed in detail. In LCSM 3Dimage reconstruction, Shear-Warp was incorporated into light reflection algorithm toimprove image processing speed. As a result 3D image was effectively reconstructedfrom a stack of 2D images.
4. In addition to the above results, this study has constructed a framework and technicalplatform for biomedical image processing of LCSM. In conclusion, the improved biomedical images processing algorithms and platform weretested and verified in the LCSM image processing. The test results show that the improvedalgorithms and new methods as well as the platform can effectively process and analyzemicroscopic images of LCSM in practice. The results of this research can be a valuable forthe future research in biomedical image analysis.
引文
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