A method for automatic classification of large and small myelinated fibre populations in peripheral nerves

The statistical analysis of morphometric data collected from biopsies of human superficial peroneal nerve is complicated by the heterogeneity of the population of myelinated fibres. In order to make separate statistical analyses of the subpopulations of large and small fibres we have developed a computer program (written in PASCAL) for their automatic separation. The method is based on a dynamic centres clustering algorithm and was applied to the multifactorial space defined by the principal component analysis of the morphometric variables: axonal diameter, myelin sheath thickness, circularity index and g-ratio. The classification technique was applied to measurements obtained from 5 control nerves, and to simulated data, and in each case it gave consistent Gaussian subpopulations with no need for the introduction of supplementary variables.