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首页> 外文会议>Conference on Optical Diagnostics of Living Cells Ⅴ, Jan 23-25, 2002, San Jose, USA >Evaluation of least squares designed contrast-enhancing FIR filters for automatic segmentation of 3D confocal images
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Evaluation of least squares designed contrast-enhancing FIR filters for automatic segmentation of 3D confocal images

机译：评估最小二乘法设计的对比度增强FIR滤波器，以自动分割3D共焦图像

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摘要

With recent advances in high-speed confocal imaging, data storage, and computational power, practical high-speed 3D cytometry instrumentation is on the horizon. For 3D cytometry to become practical for example from the perspective of a pathologist, speed attained in part by walk-away automation is fundamentally important. This level of automation can only be obtained with fully automated segmentation of image objects from background. Accuracy of this first image analysis task is crucial since it determines the results of all subsequent quantitative analyses. Confocal cell images often have low contrast due to both inherently low signal-to-noise ratios and high cell-cell contrast ratios that can occupy much of the available imaging dynamic range. A contrast-enhancing technique previously developed for 2D images of fluorescent cell nuclei was extended for 3D confocal images (stacks of 2D image slices). Edge sharpening and contrast-enhancement necessary for automatic thresholding are achieved by filtering with a finite impulse response (FIR) filter. These optimal FIR filters range in size from 3x3x3 to 13x13x13 and were designed by utilizing the perceptron criterion and nonlinear least squares on confocal training datasets derived from fluorescent microspheres. By utilizing fluorescent beads of known shapes and sizes, the ideal (or standard) segmented image is known a priori. The contrast-enhancing performance of these filters on 3D confocal images of DAPI stained cell nuclei demonstrates that they should lead to accurate, fully automated 3D image segmentation.
机译：随着高速共聚焦成像，数据存储和计算能力的最新发展，实用的高速3D细胞术仪器即将出现。例如，从病理学家的角度来看，要使3D细胞计数法变得切实可行，通过自动化实现的部分速度至关重要。只有从背景中对图像对象进行全自动分割，才能获得这种自动化水平。第一项图像分析任务的准确性至关重要，因为它确定了所有后续定量分析的结果。共聚焦细胞图像通常由于固有的低信噪比和高细胞-细胞对比度而具有较低的对比度，而固有的低信噪比和高细胞-细胞对比度可占据许多可用成像动态范围。以前为荧光细胞核的2D图像开发的对比度增强技术已扩展到3D共焦图像（2D图像切片的堆栈）。通过使用有限脉冲响应（FIR）滤波器进行滤波，可以实现自动阈值处理所需的边缘锐化和对比度增强功能。这些最佳FIR滤波器的大小范围从3x3x3到13x13x13，是通过利用感知器标准和源自荧光微球的共聚焦训练数据集上的非线性最小二乘法设计的。通过利用已知形状和大小的荧光珠，理想的（或标准的）分割图像是先验的。这些滤镜在DAPI染色的细胞核的3D共聚焦图像上的对比度增强性能表明，它们应导致准确的，全自动3D图像分割。

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