ABSTRACT

Breast cancer is a major concern among women that causes high risk of death. Early diagnosis of such cancer becomes challenging due to alterations in the color of the histopathological breast images. This study uses a publicly available dataset of breast cancer histopathology images. This paper introduces a dual stage normalization approach, to address the color variation problem of biopsy specimen collectively caused by incompatible staining in biopsy process and bizarre imaging quality. The dual stage normalization proposed here consists of a stain normalization unit and a light normalization unit. This system addresses the variations of both imaging and staining of specimen that are caused by a microscopic imaging setup. Later on, eight features have been extracted from the normalized images and used for the classification of breast cancer (benign and malignant). The overall accuracy of the back propagation algorithm (BPA) classifier is obtained as 81.8%. After comparison with other classifier accuracies, BPA classifier is found to be acceptable. Recall and precision values are approximately 89% and 90%, respectively, which is acceptable. The saturation-weighted hue statistics produces balanced and uniform color hues for stain normalization. This statistic is powerful against variations in model parameters and unsusceptible to image subjects and achromatic colors. This normalization technique retains all histological data with an enhanced performance.

摘要

乳腺癌是女性的一个主要问题，它会导致高风险的死亡。由于组织病理学乳房图像颜色的变化，这种癌症的早期诊断变得具有挑战性。本研究使用了一个公开可用的乳腺癌组织病理学图像数据集。本文介绍了一种双阶段归一化方法，以解决活检过程中由于染色不相容和成像质量异常引起的活检标本颜色变化问题。这里提出的双阶段归一化包括一个污点归一化单元和一个光归一化单元。该系统解决了由显微成像设置引起的样本成像和染色变化。稍后的，从归一化图像中提取了八个特征，用于乳腺癌（良性和恶性）的分类。反向传播算法（BPA）分类器的总体准确率为 81.8%。与其他分类器精度比较后，发现BPA分类器是可以接受的。召回率和精度值分别约为 89% 和 90%，这是可以接受的。饱和度加权色调统计为污点归一化生成平衡且均匀的色调。该统计数据对模型参数的变化非常有效，并且不受图像主体和消色差的影响。这种标准化技术以增强的性能保留所有组织学数据。与其他分类器精度比较后，发现BPA分类器是可以接受的。召回率和精度值分别约为 89% 和 90%，这是可以接受的。饱和度加权色调统计为污点归一化生成平衡且均匀的色调。该统计数据对模型参数的变化非常有效，并且不受图像主体和消色差的影响。这种标准化技术以增强的性能保留所有组织学数据。与其他分类器精度比较后，发现BPA分类器是可以接受的。召回率和精度值分别约为 89% 和 90%，这是可以接受的。饱和度加权色调统计为污点归一化生成平衡且均匀的色调。该统计数据对模型参数的变化非常有效，并且不受图像主体和消色差的影响。这种标准化技术以增强的性能保留所有组织学数据。该统计数据对模型参数的变化非常有效，并且不受图像主体和消色差的影响。这种标准化技术以增强的性能保留所有组织学数据。该统计数据对模型参数的变化非常有效，并且不受图像主体和消色差的影响。这种标准化技术以增强的性能保留所有组织学数据。