Image processing of finite size rat retinal ganglion cells using multifractal and local connected fractal analysis

Abstract

Automated image processing aids in classification of biological images. Many natural structures such as neurons may be multifractal and therefore not analyzable using current methods. The multifractal spectrum proposed here may mitigate this, Here we report the outcome of applying three methods that elucidate the variation within 16 rat retinal ganglion cells using the local connected fractal dimension (LCFD), mass-radius (MR) and maximum likelihood multifractal (MLM) analyses. Our results based on LCFD indicate that the neurons studied are possibly multifractal. However utilizing the MR method provided inconclusive results due to the finite size of the cells and the density variation throughout their structure. This has been addressed by utilizing a novel unbiased method - the MLM method. To improve the our results we are now aiming to use AI algorithms to optimize the selection of parameter values associated with the MLM method.