QUANTITATIVE MORPHOLOGY AND SHAPE CLASSIFICATION OF NEURONS BY COMPUTERIZED IMAGE-ANALYSIS

We describe a new image processing method for semiautomatic quantitati ve-analysis of neuronal morphology. It has been developed in a specifi c image analysis environment (IBAS 2.0), but the algorithms and the me thods can be employed elsewhere. The program is versatile and allows t he analysis of histological preparations of different quality on the b asis of different levels of evaluation and image extraction. Some sign ificant algorithms have been implemented (i.e. one for multiple focus image acquisition and one for automatic cell body shape recognition an d classification). A wide set of specific morphological parameters has been defined to allow a better mathematical characterization of neuro nal morphology as regards both dendrite trees and cell bodies. Cell bo dies' shapes can be classified automatically, defining different neuro nal populations. This is done by evaluating the number of main dendrit es and perykaria shapes through a multi-valued-decision-tree based met hod, 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 popula tions.