期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 14, 期 6, 页码 1366-1377出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2016.2591520
关键词
Microglia analysis; Mumford-Shah; fast split Bregman; fast Fourier transform; multifractal analysis; histology data analysis
类别
资金
- MRC [MC_PC_13072] Funding Source: UKRI
- Medical Research Council [MC_PC_13072] Funding Source: Medline
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.
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