Novel methods for microglia segmentation, feature extraction, and classification

Research output: Contribution to journalArticlepeer-review


  • Yuchun Ding
  • Marie Christine Pardon
  • Alessandra Agostini
  • Henryk Faas
  • Wil O.C. Ward
  • Felicity Easton
  • Dorothee Auer
  • Li Bai

Colleges, School and Institutes

External organisations

  • University of Nottingham


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.


Original languageEnglish
Pages (from-to)1366-1377
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
Early online date14 Jul 2016
Publication statusPublished - 7 Dec 2017


  • fast Fourier transform, fast split Bregman, histology data analysis, Microglia analysis, multifractal analysis, Mumford-Shah