首页> 外文会议>SPIE Conference on Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues >Automatic cell nuclei detection: a protocol to acquire multispectral images and to compare results between color and multispectral images
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Automatic cell nuclei detection: a protocol to acquire multispectral images and to compare results between color and multispectral images

机译:自动细胞核检测:获取多光谱图像的协议,并比较颜色和多光谱图像之间的结果

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High-throughput screening in histology and analysis need a necessary automatic cell or nucleus counting. Current methods and systems based on grayscale or color images give results with counting errors. We suggest to use multispectral imaging (with more than three bands) rather than color one for nucleus counting. A traditional acquisition chains uses a source of white light and a CCD camera in addition to the optical microscope. To pass to a multispectral acquisition, we use a Programmable Light Source (PLS) in the place of the white light source. This PLS is capable of generating different wavelengths in the visible spectrum. So, one color image and four multispectral images have been acquired from histological slices. The four multispectral images contain respectively 3 bands, 5 bands, 7 bands and 10 bands. To make a proper comparison of data, several considerations have been taken, like camera linearity, intensity difference between the wavebands from the PLS and non uniformity of the light intensity range in the images. So, a set of measures were done for calibrating the system. An automatic detection method based on segmentation by expectation-maximization and ellipse fitting is used. An extension of this method is proposed in order to be applied to multispectral images. The original and the extended method are then applied to the data previously acquired to have first results regarding the effect of using multispectral images rather than color ones.
机译:组织学和分析中的高通量筛选需要必要的自动细胞或细胞核计数。基于灰度或彩色图像的当前方法和系统提供计数误差的结果。我们建议使用多光谱成像(具有三个以上的频带)而不是颜色为核计数。除光学显微镜之外,传统的采集链除了光学显微镜之外,还使用白光源和CCD相机。要传递给多光谱习得,我们使用可编程光源(PLS)代替白色光源。该PLS能够在可见光谱中产生不同的波长。因此,已经从组织学切片中获取了一种彩色图像和四个多光谱图像。四个多光谱图像分别包含3个频带,5个频带,7条带和10个频带。为了进行适当的数据比较,已经考虑了多个考虑因素,如相机线性,来自PLS的波段之间的强度差异,并且图像中的光强度范围的不均匀性。因此,完成了一系列措施来校准系统。使用基于预期最大化和椭圆拟合的分割的自动检测方法。提出了该方法的扩展,以便应用于多光谱图像。然后将原始和扩展方法应用于先前获取的数据,以便在使用多光谱图像而不是颜色的数据的第一结果。

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