T he pathologist determines the malignancy of a breast tumor by studying the histopathological images. In particular, the characteristics and distribution of nuclei contribute greatly to the decision process. Hence, the segmentation of nuclei constitutes a crucial task in the classification of breast histopathological images. Manual analysis of these images is subjective, tedious and susceptible to human error. Consequently, the development of computer-aided diagnostic systems for analysing these images have become a vital factor in the domain of medical imaging. However, the usage of medical image processing techniques to segment nuclei is challenging due to the diverse structure of the cells, poor staining process, the occurrence of artifacts, etc. Although supervised computer-aided systems for nuclei segmentation is popular, it is dependent on the availability of standard annotated datasets. In this regard, this work presents an unsupervised method based on Chan-Vese model to segment nuclei from breast histopathological images. The proposed model utilizes multi-channel color information to efficiently segment the nuclei. Also, this study proposes a pre-processing step to select appropriate color channel such that it discriminates nuclei from the background region. An extensive evaluation of the proposed model on two challenging datasets demonstrates its validity and effectiveness.

病理学家通过研究组织病理学图像来确定乳腺肿瘤的恶性程度。特别是，原子核的特征和分布对决策过程有很大贡献。因此，细胞核的分割构成了乳腺组织病理学图像分类的关键任务。对这些图像的手动分析是主观的、乏味的并且容易受到人为错误的影响。因此，用于分析这些图像的计算机辅助诊断系统的开发已成为医学成像领域的重要因素。然而，由于细胞结构的多样性、染色过程不佳、伪影的出现等，使用医学图像处理技术来分割细胞核具有挑战性。 尽管用于细胞核分割的监督计算机辅助系统很受欢迎，它取决于标准注释数据集的可用性。在这方面，这项工作提出了一种基于 Chan-Vese 模型的无监督方法，用于从乳房组织病理学图像中分割细胞核。所提出的模型利用多通道颜色信息来有效地分割细胞核。此外，这项研究提出了一个预处理步骤来选择合适的颜色通道，以便将核与背景区域区分开来。在两个具有挑战性的数据集上对所提出模型的广泛评估证明了其有效性和有效性。这项研究提出了一个预处理步骤，以选择合适的颜色通道，以便将细胞核与背景区域区分开来。在两个具有挑战性的数据集上对所提出模型的广泛评估证明了其有效性和有效性。这项研究提出了一个预处理步骤来选择合适的颜色通道，以便将细胞核与背景区域区分开来。在两个具有挑战性的数据集上对所提出模型的广泛评估证明了其有效性和有效性。