Automated extraction and classification of dynamic metrical features of morphological development in dissociated Purkinje neurons.
The morphological development of in vitro single cerebellar Purkinje cells obtained from wild type P1 CD1 mice was assessed through a dedicated non-invasive technique based on image processing algorithms and multivariate analysis. Image processing algorithms were implemented to extract metrical features characterizing cell structure and dendritic arborization from sequential optical micrographs. Quantitative morphological features were analyzed in order to identify relevant metrical characteristics common to Purkinje cells in wild type P1 CD1 mice. Cell arborization was found to be characterized by a high fractal dimension and the directionality and level of complexity were shown to be key features for cell morphology classification, as underlined using a three-way PCA analysis.
Billeci
L
L
Interdepartmental Research Center E. Piaggio, University of Pisa, via Diotisalvi 2, 56126 Pisa, Italy.
Pioggia
G
G
Vaglini
F
F
Ahluwalia
A
A
D000465:Algorithms; D000818:Animals; D002531:Cerebellum / Q000166:cytology*; D016014:Linear Models; D051379:Mice; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis; D011689:Purkinje Cells / Q000166:cytology* / Q000502:physiology; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors