Computational image analysis is used in many areas of biological and medical research, but advanced techniques including machine learning remain underutilized. Here, we used automated segmentation and shape analyses, with pre-defined features and with computer generated components, to compare nuclei from various premature aging disorders caused by alterations in nuclear proteins. We considered cells from patients with Hutchinson-Gilford progeria syndrome (HGPS) with an altered nucleoskeletal protein; a mouse model of XFE progeroid syndrome caused by a deficiency of ERCC1-XPF DNA repair nuclease; and patients with Werner syndrome (WS) lacking a functional WRN exonuclease and helicase protein. Using feature space analysis, including circularity, eccentricity, and solidity, we found that XFE nuclei were larger and significantly more elongated than control nuclei. HGPS nuclei were smaller and rounder than the control nuclei with features suggesting small bumps. WS nuclei did not show any significant shape changes from control. We also performed principle component analysis (PCA) and a geometric, contour based metric. PCA allowed direct visualization of morphological changes in diseased nuclei, whereas standard, feature-based approaches required pre-defined parameters and indirect interpretation of multiple parameters. Both methods yielded similar results, but PCA proves to be a powerful pre-analysis methodology for unknown systems.