A Novel Computational Approach based on 3D Reconstruction and WEKA Tool to analyse the morphology of Golgi-Cox stained Rat Brain Cells

Inspite the many significant researches has been done in the area of neurological field based on detection and extraction of morphology of the dendritic spines. Still the current researches and automated tools have various shortcomings against the detection and analysis of the dendritic spines. The detection and extraction of neuronal morphology of spines is especially challenging due to spines compounded with lower image resolution & contrast levels. To address this issue, a novel approach is proposed to detect and quantify the dendritic spines of the rat brain cells. The overall approach includes: (i) a novel 3D reconstruction algorithm to enhance the spines in 3D space, (ii) the mathematical description of proposed algorithm and (iii) trainable WEKA tool to classify the spines as stubby, thin & mushroom using random forest classifier and to calculate the number of each spine type. This approach enables the computation of volumteric representation of the raw spines in 3D space and morphological analysis of each spine type. We illustrate the proposed approach using Golgi-Cox stained dataset images. This dataset consists of two types of images i.e. EMF exposed images (with radiation) and SHAM exposed (without radiation) images of rat brain cells. The proposed approach achieved the accuracy of 98% (approx) in classifying the dendritic spines of rat brain cells. The proposed approach has successfully conveyed the degeneracy in the maturational shift of spine types for EMF exposure spine images as compared to SHAM exposure spine images during development in the rat primary visual cortex. Moreover, this approach is responsible for finding the defects in the morphology and size of the spines which leads to several neurological disorders.