Brain extracellular matrix (ECM) structure mediates many aspects of neuronal function. When ECM structure becomes dysregulated in neurological disease, one resulting impact is impaired neuronal function. Therefore, probing changes in ECM structure could provide insights into disease mechanisms and expose potential therapeutic pathways. Previous work in our group determined that degrading neural ECM structures, including perineuronal nets (PNNs), leads to a significant increase in the diffusive ability of nanoparticles navigating the brain extracellular space. However, this diffusion-based analysis provides little insight into changes in PNN-specific morphology or structure; it only predicts whether or not they are present and the degree to which they may be altered from normal. With this project, we aim to quantify changes in PNN structure with high spatial resolution. PNNs are stained using a fluorescently labeled lectin (Wisteria floribunda agglutinin) and images are acquired via confocal microscopy. Using Python, a coding language, we developed an automated image processing workflow to characterize morphological and structural features associated with PNNs, including total number of branches, average branch length, average mesh size of the net, and the areal density of fluorescence. This approach was applied to brains that span a range of chronological ages, from 14 days old to adult. PNNs are known to increase in counts early on in life, so this age-based study served as a proof of concept for our methodology. This same approach can be applied to study the effect of various neurological diseases on PNN structure. Collectively, this work aims to enhance our understanding of neurological disease mechanisms and open new avenues of therapeutic intervention.