title: Novel Methods for Microglia Segmentation, Feature Extraction, and Classification.
abstract: 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.
authors.forename: Yuchun
authors.initials: Y
authors.lastname: Ding
authors.forename: Marie Christine
authors.initials: MC
authors.lastname: Pardon
authors.forename: Alessandra
authors.initials: A
authors.lastname: Agostini
authors.forename: Henryk
authors.initials: H
authors.lastname: Faas
authors.forename: Jinming
authors.initials: J
authors.lastname: Duan
authors.forename: Wil O C
authors.initials: WOC
authors.lastname: Ward
authors.forename: Felicity
authors.initials: F
authors.lastname: Easton
authors.forename: Dorothee
authors.initials: D
authors.lastname: Auer
authors.forename: Li
authors.initials: L
authors.lastname: Bai
mesh_terms: D000544:Alzheimer Disease / Q000000981:diagnostic imaging; D000818:Animals; D001921:Brain / Q000000981:diagnostic imaging; D005583:Fourier Analysis; D017709:Fractals; D006651:Histocytochemistry / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D017628:Microglia / Q000166:cytology*