Unsupervised Color-Based Nuclei Segmentation in Histopathology Images with Various Color Spaces and K Values Selection

The development of digital pathology offers a significant opportunity to evaluate and analyze the whole slides of disease tissue effectively. In particular, the segmentation of nuclei from histopathology images plays an important role in quantitatively measuring and evaluating the acquired diseased tissue. There are many automatic methods to segment cell nuclei in histopathology images. One widely used unsupervised segmentation approach is based on standard k-means or fuzzy c-means (FCM) to process the color histopathology images to segment cell nuclei. Compared with the supervised learning method, this approach can obtain segmented nuclei without annotated nuclei labels for training, which saves a lot of labeling and training time. The color space and [Formula: see text] value among this method plays a crucial role in determining the nuclei segmentation performance. However, few works have investigated various color spaces and [Formula: see text] value selection simultaneously in unsupervised color-based nuclei segmentation with [Formula: see text]-means or FCM algorithms. In this study, we will present color-based nuclei segmentation methods with standard [Formula: see text]-means and FCM algorithms for histopathology images. Several color spaces of Haematoxylin and Eosin (H&E) stained histopathology data and various [Formula: see text] values among [Formula: see text]-means and FCM are investigated correspondingly to explore the suitable selection for nuclei segmentation. A comprehensive nuclei dataset with 7 different organs is used to validate our proposed method. Related experimental results indicate that [Formula: see text] and the YCbCr color spaces with a [Formula: see text] of 4 are more reasonable for nuclei segmentation via [Formula: see text]-means, while the [Formula: see text] color space with [Formula: see text] of 4 is useful via FCM.