[Statistical and regression analysis in interpreting computerized morphometric results].
The aim of the study is to construct the statistical framework requested by the interpretation of the results obtained through computerized morphometry procedures. The study also includes the regression analysis of the investigated quantitative features.


METHODS
The automatic measurements performed under Zeiss KS400 environment were achieved on a microscopic specimen of dental pulp. The areas and the perimeters were determined for 139 fibroblasts, so as to define two statistical series able to characterize the size of the cells.


CONCLUSIONS
The results are formulated in both analytic and numerical terms, the latter relying on the graphical support and computational resources of Zeiss KS400 environment. Also, the mathematical support is implemented. The research focuses on three key aspects: (i) the role of the class organization (clustering) in the correct evaluation of the measured cellular areas, (ii) the global characterization of these areas by the standard statistical indices, (iii) the linear and quadratic regression perimeter versus area.


CONCLUSIONS
The study is based on a concrete morphometric investigation of a dental pulp microscopic specimen, but the statistical and regression analysis can be applied to any other kind of specimen that undergoes a morphometric examination. The implementation under Zeiss KS400 is easy adaptable to other software environments providing similar facilities. For an exhaustive approach, in accordance with the sampling and selection laws, this type of study should be successively practiced on several fields of the microscopic specimens.
Căruntu
Irina-Draga
ID
Disciplina de Histologie, Facultatea de Medicină Stomatologică, Universitatea de Medicină şi Farmacie Gr.T. Popa Iaşi.
D000465:Algorithms; D003627:Data Interpretation, Statistical; D003782:Dental Pulp / Q000648:ultrastructure*; D005347:Fibroblasts / Q000648:ultrastructure; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models