title: Quantitative morphology and shape classification of neurons by computerized image analysis.
abstract: We describe a new image processing method for semiautomatic quantitative analysis of neuronal morphology. It has been developed in a specific image analysis environment (IBAS 2.0), but the algorithms and the methods can be employed elsewhere. The program is versatile and allows the analysis of histological preparations of different quality on the basis of different levels of evaluation and image extraction. Some significant algorithms have been implemented (i.e. one for multiple focus image acquisition and one for automatic cell body shape recognition and classification). A wide set of specific morphological parameters has been defined to allow a better mathematical characterization of neuronal morphology as regards both dendrite trees and cell bodies. Cell bodies' shapes can be classified automatically, defining different neuronal populations. This is done by evaluating the number of main dendrites and perikarya shapes through a multi-valued-decision-tree based method, tested on somatostatin-positive cells in mouse brain. The methods presented have been applied to analysis of neurons, but they can well be used for any quantitative morphological study of other cell populations.
authors.affiliation: Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Masseroli
authors.forename: A
authors.initials: A
authors.lastname: Bollea
authors.forename: G
authors.initials: G
authors.lastname: Forloni
mesh_terms: D000465:Algorithms; D000818:Animals; D015982:Bias; D002983:Clinical Competence; D003663:Decision Trees; D003712:Dendrites / Q000145:classification; D005069:Evaluation Studies as Topic; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D009474:Neurons / Q000145:classification* / Q000166:cytology*; D012372:ROC Curve