A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution
A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution
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DOI：
10.1109/tbme.2014.2303294
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发表时间：
2014-06-01
影响因子：
4.6
通讯作者：
Magee, Derek
中科院分区：
文献类型：
--
作者：
Khan, Adnan Mujahid;Rajpoot, Nasir;Magee, Derek
组织学诊断是基于在显微镜下对组织切片的形态进行目视检查。随着数字切片扫描仪的日益普及，基于数字病理图像分析的决策支持系统的需求很高。然而，计算机化的决策支持系统充满了问题，这些问题源于由于组织制备的变化、来自不同制造商/批次的染色剂反应性的变化、用户或协议变化以及来自不同制造商的扫描仪的使用而引起的组织外观的颜色变化。在本文中，我们提出了一种新的方法来染色正常化的组织病理学图像。该方法是基于源图像到目标图像的非线性映射，使用从颜色去卷积得到的表示。颜色去卷积是一种在给出描述颜色如何受染色剂浓度影响的染色剂矩阵时获得染色剂浓度值的方法。而不是依赖于标准的染色矩阵，这可能是不适当的一个给定的图像，我们建议使用一个基于颜色的分类器，采用了一种新的染色颜色描述符来计算图像特定的染色矩阵。为了证明所提出的染色矩阵估计和染色归一化方法的有效性，将其应用于乳腺组织病理学图像中的肿瘤分割问题。实验结果表明，颜色归一化的范例，作为预处理步骤，可以显着地帮助组织学图像分析算法，以表现出稳定的性能，这是不敏感的成像条件一般和扫描仪的变化，特别是。
Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.