  The ability to precisely quantify similarity between various entities has
been a fundamental complication in various problem spaces specifically in the
classification of cellular images. Contemporary similarity measures applied in
the domain of image processing proposed by the scientific community are mainly
pursued in supervised settings. In this work, we will explore the innovative
algorithmic normalized compression distance metric based on the information
theoretic concept of Kolmogorov Complexity. Additionally we will observe its
possible implementation in Convolutional Neural Networks to facilitate and
automate the classification of Retinal Pigment Epithelial cell cultures for use
in Age Related Macular Degeneration Stem Cell therapy in an unsupervised
setting.
