Interpolation of binarized CLSM images for extraction of premotor neuron branch structures in silkworm moth

Abstract

Purpose

The purpose of this paper is to propose an automatic interpolation method for binarized confocal laser scanning microscopy (CLSM) images of a premotor neuron in the silkworm moth.

Design/methodology/approach

Partial deficiencies occur in binary images through the form extraction process because of noises in a CLSM image series. The proposed method selects several points from a binarized image series and connects these points with a Bezier curve based on premotor neuron characteristics in order to interpolate partial deficiencies.

Findings

To verify the availability of the proposed method, a three‐dimensional form of a premotor neuron of a silkworm moth was extracted. The results of each branch's relation of connection and of the interpolated neuron thickness show that the proposed method realizes to interpolate partial deficiencies and to extract three‐dimensional form of the premotor neuron.

Practical implications

The proposed method contributes to realize efficient premotor extraction process using image‐processing techniques. The extracted result by proposed method can be utilized for the form comparison among many data of the premotor neurons quickly. Moreover, it also contributes to provide the parameters of an accurate neuron model for realizing computer simulation of electrical of the neurons.

Originality/value

The proposed method extracts not only a topological form but also a premotor neuron's thickness by interpolating partial deficiencies based on specific characteristics of the neuron. Thickness values of the neuron are an important factor for a simulating accurate electrical response of the neuronal circuit.

Citation

Nakajima, K., Morishita, S., Kazawa, T., Kanzaki, R., Kawabata, K., Asama, H. and Mishima, T. (2009), "Interpolation of binarized CLSM images for extraction of premotor neuron branch structures in silkworm moth", Sensor Review, Vol. 29 No. 2, pp. 137-147. https://doi.org/10.1108/02602280910936246