On map-based classification of insect neurons using three-dimensional quantification

A method of classifying interneurons of silkworm moths is presented in this paper. Self-organizing maps (SOM´s) are employed as tools for the classification. The maps are trained by presenting the data, each of which has a fractal dimension value, a ratio of the number of voxels after applying the erosion operation compared to that after labeling voxels, and a degree of circularity, as three elements. The first and second elements quantify denseness of branching structures and thickness of main dendrites in neurons, respectively. The remaining element is provided for quantifying uniformity of branching structures. The classification result is given as unit clusters formed in the trained map. It is established that the proposed method allows us to obtain the favorable classification result. It is much close to the result manually classified by a neuroscientist, compared with that obtained by the previously proposed map-based method.