IEEE/ACM Trans Comput Biol Bioinform. 2017 Nov-Dec;14(6):1366-1377. doi: 10.1109/TCBB.2016.2591520. Epub 2016 Jul 14.
Segmentation and analysis of histological images provides a valuable tool to gain insight into the biology and function of microglial cells in health and disease. Common image segmentation methods are not suitable for inhomogeneous histology image analysis and accurate classification of microglial activation states has remained a challenge. In this paper, we introduce an automated image analysis framework capable of efficiently segmenting microglial cells from histology images and analyzing their morphology. The framework makes use of variational methods and the fast-split Bregman algorithm for image denoising and segmentation, and of multifractal analysis for feature extraction to classify microglia by their activation states. Experiments show that the proposed framework is accurate and scalable to large datasets and provides a useful tool for the study of microglial biology.
组织学图像的分割和分析为深入了解健康和疾病状态下小胶质细胞的生物学和功能提供了有价值的工具。常见的图像分割方法不适用于不均匀的组织学图像分析，并且准确分类小胶质细胞的激活状态仍然是一个挑战。在本文中，我们介绍了一种自动化的图像分析框架，该框架能够有效地从小胶质细胞的组织学图像中进行分割，并分析其形态。该框架利用变分方法和快速分裂的 Bregman 算法进行图像去噪和分割，并利用多重分形分析进行特征提取，以根据其激活状态对小胶质细胞进行分类。实验表明，所提出的框架准确且可扩展到大型数据集，为小胶质细胞生物学的研究提供了有用的工具。
