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STAIN DECONVOLUTION OF HISTOLOGY IMAGES VIA INDEPENDENT COMPONENT ANALYSIS IN THE WAVELET DOMAIN

机译:通过小波域中的独立分量分析染色组织学图像的污染去折叠

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With the ubiquity of digital slide scanners, histology image analysis is rapidly emerging as an active area of research. Several histology image analysis algorithms such as those for mitotic cell detection, nuclei segmentation and hormone receptors scoring depend on colour information obtained from images of the scanned slides. However, different standards followed by different labs and the technical variation among different scanners result in stain inconsistency in histology images. Thus, applications that use colour information may fail when they are applied to images with different appearance of stain colours. In this paper, we propose a novel method to estimate the so called stain matrix via independent component analysis in the wavelet domain for stain deconvolution in histology images. Experimental results demonstrate stable and more accurate stain deconvolution results as compared to other recently proposed algorithms.
机译:随着数字幻灯片扫描仪的无处不在,组织学图像分析正在迅速成为一种活跃的研究领域。若干组织学图像分析算法,例如有丝分裂细胞检测,核细胞分段和激素受体评分取决于从扫描载玻片的图像获得的颜色信息。然而,不同的标准随后是不同的实验室和不同扫描仪之间的技术变化导致组织学图像中的污渍不一致。因此,当它们应用于具有不同外观的染色的图像时,使用颜色信息的应用可能会失败。在本文中,我们提出了一种新的方法来估计所谓的染色矩阵通过小波域的独立分量分析来估计组织学图像中的染色去卷积的独立分量分析。实验结果表明,与其他最近提出的算法相比,稳定且更精确的污渍折叠结果。

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