title: Proof of concept of an automatic tool for bioluminescence imaging data analysis.
abstract: Bioluminescence Imaging (BLI) is an important molecular imaging tool to assess complex biological processes in vivo. BLI is a sensitive technique, which is frequently used in small-animal preclinical research, mainly in oncology and neurology. Tracking of labeled cells is one of the major applications. However, BLI data analysis for the segmentation of up-taking regions and their quantification is not trivial and it is usually an operator-dependent activity. In this work, a proof of concept of an automatic method to analyze BL images is presented which is based on a multi-step approach. Different segmentation algorithms (K-means, Gaussian Mixture Model (GMM), and GMM initialized by K-means) were evaluated and an adequate image normalization step was suggested to include the background bioluminescence in the data analysis process. K-means segmentation is the most stable and accurate approach for different levels of signal intensity.
authors.forename: Alfonso
authors.initials: A
authors.lastname: Mastropietro
authors.forename: Annette
authors.initials: A
authors.lastname: Tennstaedt
authors.forename: Andreas
authors.initials: A
authors.lastname: Beyrau
authors.forename: Nadine
authors.initials: N
authors.lastname: Henn
authors.forename: Mathias
authors.initials: M
authors.lastname: Hoehn
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Baselli
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology; D058948:Cell Tracking / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008163:Luminescent Measurements; D008819:Mice, Nude; D058953:Neural Stem Cells / Q000637:transplantation; D059906:Neuroimaging; D016011:Normal Distribution; D059629:Signal-To-Noise Ratio